Tesis sobre el tema "Communications de type machine"

The internet we have known thus far has been an internet of people, as it has connected people with one another. However, these connections are forecasted to occupy only a minuscule of future communications. The internet of tomorrow is indeed: the internet of things. The Internet of Things (IoT) promises to improve all aspects of life by connecting everything to everything. An enormous amount of effort is being exerted to turn these visions into a reality. Sensors and actuators will communicate and operate in an automated fashion with no or minimal human intervention. In the current literature, these sensors and actuators are referred to as machines, and the communication amongst these machines is referred to as Machine to Machine (M2M) communication or Machine-Type Communication (MTC). As IoT requires a seamless mode of communication that is available anywhere and anytime, wireless communications will be one of the key enabling technologies for IoT. In existing wireless cellular networks, users with data to transmit first need to request channel access. All access requests are processed by a central unit that in return either grants or denies the access request. Once granted access, users' data transmissions are non-overlapping and interference free. However, as the number of IoT devices is forecasted to be in the order of hundreds of millions, if not billions, in the near future, the access channels of existing cellular networks are predicted to suffer from severe congestion and, thus, incur unpredictable latencies in the system. On the other hand, in random access, users with data to transmit will access the channel in an uncoordinated and probabilistic fashion, thus, requiring little or no signalling overhead. However, this reduction in overhead is at the expense of reliability and efficiency due to the interference caused by contending users. In most existing random access schemes, packets are lost when they experience interference from other packets transmitted over the same resources. Moreover, most existing random access schemes are best-effort schemes with almost no Quality of Service (QoS) guarantees. In this thesis, we investigate the performance of different random access schemes in different settings to resolve the problem of the massive access of IoT devices with diverse QoS guarantees. First, we take a step towards re-designing existing random access protocols such that they are more practical and more efficient. For many years, researchers have adopted the collision channel model in random access schemes: a collision is the event of two or more users transmitting over the same time-frequency resources. In the event of a collision, all the involved data is lost, and users need to retransmit their information. However, in practice, data can be recovered even in the presence of interference provided that the power of the signal is sufficiently larger than the power of the noise and the power of the interference. Based on this, we re-define the event of collision as the event of the interference power exceeding a pre-determined threshold. We propose a new analytical framework to compute the probability of packet recovery failure inspired by error control codes on graph. We optimize the random access parameters based on evolution strategies. Our results show a significant improvement in performance in terms of reliability and efficiency. Next, we focus on supporting the heterogeneous IoT applications and accommodating their diverse latency and reliability requirements in a unified access scheme. We propose a multi-stage approach where each group of applications transmits in different stages with different probabilities. We propose a new analytical framework to compute the probability of packet recovery failure for each group in each stage. We also optimize the random access parameters using evolution strategies. Our results show that our proposed scheme can outperform coordinated access schemes of existing cellular networks when the number of users is very large. Finally, we investigate random non-orthogonal multiple access schemes that are known to achieve a higher spectrum efficiency and are known to support higher loads. In our proposed scheme, user detection and channel estimation are carried out via pilot sequences that are transmitted simultaneously with the user's data. Here, a collision event is defined as the event of two or more users selecting the same pilot sequence. All collisions are regarded as interference to the remaining users. We first study the distribution of the interference power and derive its expression. Then, we use this expression to derive simple yet accurate analytical bounds on the throughput and outage probability of the proposed scheme. We consider both joint decoding as well as successive interference cancellation. We show that the proposed scheme is especially useful in the case of short packet transmissions

To cover all the needs and requirements of mobile networks in the future, the predicted usage of the mobile networks has been split into three use-cases: enhanced mobile broadband, ultra-reliable low-latency communication, and massive machine-type communication. In this thesis we focus on the massive machine-type communication use-case which is intended to facilitate the ever increasing number of smart devices and sensors. In the massive machine-type communication use-case, the main challenges are to accommodate a huge number of devices while keeping the battery lives of the devices long, and allowing them to be placed in far-away locations. However, these devices are not concerned about other features such as latency, high data rate, or mobility. In this thesis we study the application of massive MIMO (multiple-input multiple-output) technology for the massive machine-type communication use-case. Massive MIMO has been on the radar as an enabler for future communication networks in the last decade and is now firmly rooted in both academia and industry. The main idea of massive MIMO is to utilize a base station with a massive number of antennas which gives the ability to spatially direct signals and serve multiple devices in the same time- and frequency resource. More specifically, in this thesis we study A) a scenario where the base station takes advantage of a device's low mobility to improve its channel estimate, B) a random access scheme for massive machine-type communication which can accommodate a huge number of devices, and C) a case study where the benefits of massive MIMO for long range devices are quantified. The results are that the base station can significantly improve the channel estimates for a low mobility user such that it can tolerate lower SNR while still achieving the same rate. Additionally, the properties of massive MIMO greatly helps to detect users in random access scenarios and increase link-budgets compared to single-antenna base stations.

With a wide range of potential applications, the machine type communication (MTC) is gaining a tremendous interest among mobile network operators, system designers, MTC specialist companies, and research institutes. The idea of having electronic devices and systems automatically connected to each other without human intervention is one of the most significant objectives for future wireless communications. Low data rate transmission and the requirement for low energy consumption are two typical characteristics for MTC applications. In terms of supporting low cots MTC devices, industrial standards will be more efficient if designers can re-use many features of existing radio access technologies. This will yield a cost effective solution to support MTC in future communication systems. This thesis investigates efficient MTC waveform and receiver designs for superior signal transmission quality with low operational costs. In terms of the downlink receiver design, this thesis proposes a novel virtual carrier (VC) receiver system for MTC receivers, which aims to reduce the maximum bandwidth to improve the data processing efficiency and cost-efficiency by using analogue filters to extract only sub-carriers of interest. For the VC receiver systems, we thus reduce the sampling rate in order to reduce the number of subsequent processing operations, which significantly reduces the analogue-to-digital converter (ADC) cost and power consumption while providing high signal to interference noise ratio (SINR) and low bit to error rate (BER) to support low data rate MTC devices. Our theoretical equations account for the interference effect of aliasing on the sub-carrier location, and this helps the system designer to evaluate what kind of filters and receiver sampling rate can be used to balance the energy cost and detection performance. In terms of the uplink waveform design, considering the enhanced number of MTC devices in the future communication systems, i.e. the fifth generation (5G) communications, the same tight synchronisation as used in today appears not to be cost-effective or even possible. Synchronisation signals, which aim to provide a perfect time or frequency synchronisation in the current fourth generation (4G) communication systems (known as the long-term evolution, LTE), is much more costly for low data rate MTC transmissions. The system bandwidth will be significantly reduced if a base station tries to synchronise all received signals among hundreds or thousands MTC devices in one transmission time period. In terms of relaxing the synchronisation requirements, this thesis compares and analyses the side-lobe reduction performance for several candidate multi-carrier waveforms to avoid these problems. We also propose the infinite impulse response universal filtered multi-carrier (UFMC) system and the overlap and add UFMC system, which significantly reduce the processing complexity compared with the state of the art UFMC techniques. This thesis derives closed-form expressions for the interference caused by time offsets between adjacent unsynchronised MTC users. Our analytical equations can be used in both simple and complex time-offset transmission scenarios, and enable the system designer to evaluate the SINR, the theoretical Shannon capacity and the BER performance.

Les communications de type machine-à-machine M2M sont considérées comme des formes de communication de données qui ne requièrent pas nécessairement d'interaction humaine. Cependant, ce type de communication n'est pas efficace dans les réseaux cellulaires, en raison de leurs caractéristiques spécifiques, telles que. L'objectif de cette thèse est de proposer des mécanismes et d'optimiser les techniques de la couche d'accès radio LTE pour les communications M2M. Pour l'accès au canal de liaison montante, nous proposons deux méthodes afin d'améliorer la performance d'accès aléatoire en terme de latence et de consommation énergétique: une méthode d'agrégation de paquets et une autre de transmission multiple pendant l'intervalle de temps de transmission. Afin de réduire encore plus le temps de latence de liaison montante et permettre une connexion d'un grand nombre de machines au réseau, nous proposons une nouvelle méthode d'accès basée sur la contention CBA pour éviter d'une part la signalisation redondante pour accéder au canal et d'autre part la latence de l'ordonnanceur. Pour la réception de liaison descendante, nous proposons deux méthodes pour analyser les performances du mécanisme de réception discontinu DRX pour les applications M2M: la première se base sur une distribution de Poisson, la suivante sur une distribution Pareto pour le trafic sporadique. Avec les modèles proposés, le facteur d'économie d’énergie et la latence pour transiter du mode sommeil au mode actif peuvent être estimés avec précision pour un choix donné de paramètres DRX, permettant ainsi de sélectionner ceux permettant d'atteindre le compromis optimal
Machine type communications is seen as a form of data communication, among devices and/or from devices to a set of servers, that do not necessarily require human interaction. However, it is challenging to accommodate MTC in LTE as a result of its specific characteristics and requirements. The aim of this thesis is to propose mechanisms and optimize the access layer techniques for MTC in LTE. For uplink access, we propose two methods to improve the performance of random access in terms of latency: a packet aggregation method and a Transmission Time Interval bundling scheme. To further reduce the uplink latency and enable massive number of connected device, we propose a new contention based access method (CBA) to bypass both the redundant signaling in the random access procedure and also the latency of regular scheduling. For downlink reception, we propose two methods to analyze the performance of discontinuous reception DRX mode for MTC applications: the first with the Poisson distribution and the second with the Pareto distribution for sporadic traffic. With the proposed models, the power saving factor and wake up latency can be accurately estimated for a given choice of DRX parameters, thus allowing to select the ones presenting the optimal tradeoff

The Internet of Things promises to be a key-factor in the forthcoming industrial and social revolution. The Internet of Things concept rely on pervasive communications where ’things’ are ’always connected’. The focus of the thesis is on Heterogeneous Networks for Internet of Things and Machine Type Communications. Heterogeneous Networks are an enabling factor of paramount important in order to achieve the ’always connected’ paradigm. On the other hand, Machine Type Communications are deeply different from Human-to-Human communications both in terms of traffic patterns and requirements. This thesis investigate both concepts. In particular, here are studied short and long range solutions for Machine-to-machine applications. For this work a dual approach has been followed: for the short-range solutions analysis an experimental approach has been privileged; meanwhile for the long-range solutions analysis a theoretical and simulation approach has been preferred. In both case, a particular attention has been given to the feasibility of the solutions proposed, hence solutions based on products that already exist in the market have been privileged.

The Internet of Things promises to be a key-factor in the forthcoming industrial and social revolution. The Internet of Things concept rely on pervasive communications where ’things’ are ’always connected’. The focus of the thesis is on Heterogeneous Networks for Internet of Things and Machine Type Communications. Heterogeneous Networks are an enabling factor of paramount important in order to achieve the ’always connected’ paradigm. On the other hand, Machine Type Communications are deeply different from Human-to-Human communications both in terms of traffic patterns and requirements. This thesis investigate both concepts. In particular, here are studied short and long range solutions for Machine-to-machine applications. For this work a dual approach has been followed: for the short-range solutions analysis an experimental approach has been privileged; meanwhile for the long-range solutions analysis a theoretical and simulation approach has been preferred. In both case, a particular attention has been given to the feasibility of the solutions proposed, hence solutions based on products that already exist in the market have been privileged.

Dans cette thèse de doctorat, nous explorons des solutions basées sur l'apprentissage automatique pour le décodage canal dans les systèmes de communication de type Machine-à-Machine, où l'atteinte de communications ultra-fiables et à faible latence (URLLC) est essentielle. Leur principal problème provient de la croissance exponentielle de la complexité du décodeur à mesure que la taille du paquet augmente. Cette "malédiction de la complexité" se manifeste sous trois aspects différents : i) le nombre de schémas de bruit corrigeables, ii) l'espace des mots de code à explorer, et iii) le nombre de paramètres entraînables dans les modèles. Pour pallier la première limitation, nous explorons des solutions basées sur les Machines à Vecteurs de Support (SVM) et proposons une approche bit-à-bit qui réduit considérablement la complexité des solutions SVM existantes. Pour aborder la deuxième limitation, nous investiguons des décodeurs neuronaux de type "syndrome-based" et introduisons un nouveau décodeur orienté message qui améliore les schémas existants tant au niveau de son architecture que du choix de la matrice de parité. Concernant la taille du réseau, nous développons une version récurrente d'un décodeur basé sur le transformer qui réduit le nombre de paramètres tout en maintenant l'efficacité par rapport aux solutions précédentes. Enfin, nous étendons le décodeur proposé pour supporter les modulations d'ordre supérieur via les Modulations Codées avec et sans Entrelacement de Bits (BICM et CM, resp.), facilitant ainsi son application dans des environnements de communication plus réalistes
In this Ph.D. thesis, we explore machine learning-based solutions for channel decoding in Machine-to-Machine type communications, where achieving ultra-reliable low-latency communications (URLLC) is essential. Their primary issue arises from the exponential growth in the decoder's complexity as the packet size increases. This "curse of dimensionality" manifests itself in three different aspects: i) the number of correctable noise patterns, ii) the codeword space to be explored, and iii) the number of trainable parameters in the models. To address the first limitation, we explore solutions based on a Support Vector Machine (SVM) framework and suggest a bitwise SVM approach that significantly reduces the complexity of existing SVM-based solutions. To tackle the second limitation, we investigate syndrome-based neural decoders and introduce a novel message-oriented decoder, which improves on existing schemes both in the decoder architecture and in the choice of the parity check matrix. Regarding the neural network size, we develop a recurrent version of a transformer-based decoder, which reduces the number of parameters while maintaining efficiency, compared to previous neural-based solutions. Lastly, we extend the proposed decoder to support higher-order modulations through Bit-Interleaved and generic Coded Modulations (BICM and CM, respectively), aiding its application in more realistic communication environments

Abstract. This thesis evaluates the performance of reliability and latency in machine type communication networks, which composed of single transmitter and receiver in the presence of Rayleigh fading channel. The source’s traffic arrivals are modeled as Markovian processes namely Discrete-Time Markov process, Fluid Markov process, Discrete-Time Markov Modulated Poisson process and Continuous-Time Markov Modulated Poisson process, and delay/buffer overflow constraints are imposed. Our approach is based on the reliability and latency outage probability, where transmitter not knowing the channel condition, therefore the transmitter would be transmitting information over the fixed rate. The fixed rate transmission is modeled as a two-state Discrete-time Markov process, which identifies the reliability level of wireless transmission. Using effective bandwidth and effective capacity theories, we evaluate the trade-off between reliability-latency and identify QoS requirement. The impact of different source traffic originated from MTC devices under QoS constraints on the effective transmission rate are investigated.

Les communications de type machine-à-machine M2M sont considérées comme des formes de communication de données qui ne requièrent pas nécessairement d'interaction humaine. Cependant, ce type de communication n'est pas efficace dans les réseaux cellulaires, en raison de leurs caractéristiques spécifiques, telles que. L'objectif de cette thèse est de proposer des mécanismes et d'optimiser les techniques de la couche d'accès radio LTE pour les communications M2M. Pour l'accès au canal de liaison montante, nous proposons deux méthodes afin d'améliorer la performance d'accès aléatoire en terme de latence et de consommation énergétique: une méthode d'agrégation de paquets et une autre de transmission multiple pendant l'intervalle de temps de transmission. Afin de réduire encore plus le temps de latence de liaison montante et permettre une connexion d'un grand nombre de machines au réseau, nous proposons une nouvelle méthode d'accès basée sur la contention CBA pour éviter d'une part la signalisation redondante pour accéder au canal et d'autre part la latence de l'ordonnanceur. Pour la réception de liaison descendante, nous proposons deux méthodes pour analyser les performances du mécanisme de réception discontinu DRX pour les applications M2M: la première se base sur une distribution de Poisson, la suivante sur une distribution Pareto pour le trafic sporadique. Avec les modèles proposés, le facteur d'économie d’énergie et la latence pour transiter du mode sommeil au mode actif peuvent être estimés avec précision pour un choix donné de paramètres DRX, permettant ainsi de sélectionner ceux permettant d'atteindre le compromis optimal
Machine type communications is seen as a form of data communication, among devices and/or from devices to a set of servers, that do not necessarily require human interaction. However, it is challenging to accommodate MTC in LTE as a result of its specific characteristics and requirements. The aim of this thesis is to propose mechanisms and optimize the access layer techniques for MTC in LTE. For uplink access, we propose two methods to improve the performance of random access in terms of latency: a packet aggregation method and a Transmission Time Interval bundling scheme. To further reduce the uplink latency and enable massive number of connected device, we propose a new contention based access method (CBA) to bypass both the redundant signaling in the random access procedure and also the latency of regular scheduling. For downlink reception, we propose two methods to analyze the performance of discontinuous reception DRX mode for MTC applications: the first with the Poisson distribution and the second with the Pareto distribution for sporadic traffic. With the proposed models, the power saving factor and wake up latency can be accurately estimated for a given choice of DRX parameters, thus allowing to select the ones presenting the optimal tradeoff

Internet of Things (IoT) refers to the interconnection of uniquely identifiable smart devices which enables them to participate more actively in everyday life. Among large-scale applications, machine-type communications (MTC) supported by cellular networks will be one of the most important enablers for the success of IoT. The existing cellular infrastructure has been optimized for serving a small number of long-lived human-oriented communications (HoC) sessions, originated from smartphones whose batteries are charged in a daily basis. As a consequence, serving a massive number of non-rechargeable machine-type devices demanding a long battery lifetime is a big challenge for cellular networks. The present work is devoted to energy consumption modeling, battery lifetime analysis, and lifetime-aware network design for massive MTC services over cellular networks. At first, we present a realistic model for energy consumption of machine devices in cellular connectivity, which is employed subsequently in deriving the key performance indicator, i.e. network battery lifetime. Then, we develop an efficient mathematical foundation and algorithmic framework for lifetime-aware clustering design for serving a massive number of machine devices. Also, by extending the developed framework to non-clustered MTC, lifetime-aware uplink scheduling and power control solutions are derived. Finally, by investigating the delay, energy consumption, spectral efficiency, and battery lifetime tradeoffs in serving coexistence of HoC and MTC traffic, we explore the ways in which energy saving for the access network and quality of service for HoC traffic can be traded to prolong battery lifetime for machine devices. The numerical and simulation results show that the proposed solutions can provide substantial network lifetime improvement and network maintenance cost reduction in comparison with the existing approaches.

QC 20161103

The interest of Machine Type Communication (MTC) is increasing and is expected to play an important role in the future network society. In the process of increasing the number of connected devices, the coverage plays an important role. This thesis work aims to study the possibility of supporting coverage limited MTC-devices within LTE by extending the LTE coverage. It shows that coverage increase by means of repetition is a good candidate, which allows for a significant increase without hardware upgrades at a low cost in terms of radio resources. For inter-site distances up to 2500 m, the proposed repetition scheme with an increase of 20 dB allows for almost complete coverage where today’s LTE have significant lack of coverage. It also shows that even though the increased coverage implies higher resource usage, the limitation is not in the number of users supported, but rather the coverage at longer inter-site distances.

Abstract. We examine the physical layer security for machine type communication networks and highlight a secure communication scenario that consists of a transmitter Alice, which employs Transmit Antenna Selection, while a legitimate receiver Bob that uses Maximum Ratio Combining, as well as an eavesdropper Eve. We provide a solution to avoid eavesdropping and provide ways to quantify security and reliability. We obtain closed-form expressions for Multiple-Input Multiple-Output and Multi-antenna Eavesdropper (MIMOME) scenario. The closed{-}form expressions for three useful variations of MIMOME scenario, i.e., MISOME, MIMOSE, and MISOSE are also provided. A low cost and less complex system for utilizing the spatial diversity in multiple antennas system, while guaranteeing secrecy and reliability. Similarly, it is also assumed that Alice, Bob, and Eve can estimate their channel state information, and then we evaluate the performance of closed-form expressions in terms of secrecy outage probability and provide Monte Carlo simulations to corroborate the proposed analytical framework.

Wireless sensor networks (WSNs) are systems used for detecting events and gathering information from an area of interest in many different application domains, from home and industry automation, to healthcare and transportation, to environmental monitoring. With regard to the communication task involved in WSNs, they can also be seen as an instance of the new paradigm, known as machine-type communication (MTC). Similar to traditional wireless sensors, MTC-enabled devices can communicate together without direct human interference. Energy efficiency for the sake of longevity is perhaps the most challenging requirement for many WSNs and MTC networks. In this thesis, we consider ultra-wideband (UWB) transmission technology for energy-efficient communication in WSNs. UWB achieves frugal use of energy by transmitting with low spectral efficiency when compared to legacy wireless technologies. This also allows it to operate license-exempt in many jurisdictions around the world. More recently, however, wireless service operators consider the use of cellular technology also for low data-rate applications originally only served by WSN-type technology. In particular, long-term evolution (LTE) technology has moved into the focus for joint personal-communication and MTC networks. Recent releases of the LTE standard and ongoing work items in LTE standardization specifically accommodate low-cost and low-power MTC. This thesis presents contributions that improve the performance of UWB WSN and LTE MTC networks in several aspects, namely lifetime, localization accuracy, and coverage. A common theme of these different contributions are the use of optimization methods for obtaining scalable, robust, and/or low-complexity solutions. We first address the lifetime maximization problem in a UWB-based WSN designed for multiple event detection. The key contribution is the joint optimization of transmission and routing parameters of sensor nodes so that the energy consumption is distributed as evenly as possible among the entire WSN. We then investigate the challenges of localization in WSNs and provide a convex solution which is robust to measurement uncertainties. In the last part of this thesis we focus on providing coverage for low-cost LTE MTC networks, where the challenge is to develop efficient transmission strategies that maximize the coverage of MTC devices in an LTE cell.

En la actualidad, la Internet de las Cosas (Internet of Things, IoT) es una tecnología esencial para la próxima generación de sistemas inalámbricos. La conectividad es la base de IoT, y el tipo de acceso requerido dependerá de la naturaleza de la aplicación. Uno de los principales facilitadores del entorno IoT es la comunicación machine-to-machine (M2M) y, en particular, su enorme potencial para ofrecer conectividad ubicua entre dispositivos inteligentes. Las redes celulares son la elección natural para las aplicaciones emergentes de IoT y M2M. Un desafío importante en las redes celulares es conseguir que la red sea capaz de manejar escenarios de acceso masivo en los que numerosos dispositivos utilizan comunicaciones M2M. Por otro lado, los sistemas celulares han experimentado un tremendo desarrollo en las últimas décadas: incorporan tecnología sofisticada y nuevos algoritmos para ofrecer una amplia gama de servicios. El modelado y análisis del rendimiento de estas redes multiservicio es también una tarea desafiante que podría requerir un gran esfuerzo computacional. Para abordar los desafíos anteriores, nos centramos en primer lugar en el diseño y la evaluación de las prestaciones de nuevos mecanismos de control de acceso para hacer frente a las comunicaciones masivas M2M en redes celulares. Posteriormente nos ocupamos de la evaluación de prestaciones de redes multiservicio y proponemos una nueva técnica analítica que ofrece precisión y eficiencia computacional. Nuestro principal objetivo es proporcionar soluciones para aliviar la congestión en la red de acceso radio cuando un gran número de dispositivos M2M intentan conectarse a la red. Consideramos los siguientes tipos de escenarios: (i) los dispositivos M2M se conectan directamente a las estaciones base celulares, y (ii) forman grupos y los datos se envían a concentradores de tráfico (gateways) que les proporcionan acceso a la infraestructura. En el primer escenario, dado que el número de dispositivos añadidos a la red aumenta continuamente, esta debería ser capaz de manejar el considerable incremento en las solicitudes de acceso. El 3rd Generation Partnership Project (3GPP) ha propuesto el access class barring (ACB) como una solución práctica para el control de congestión en la red de acceso radio y la red troncal. El ajuste correcto de los parámetros de ACB de acuerdo con la intensidad del tráfico es crítico, pero cómo hacerlo de forma dinámica y autónoma es un problema complejo cuya solución no está recogida en las especificaciones del 3GPP. Esta tesis doctoral contribuye al análisis del rendimiento y al diseño de nuevos algoritmos que implementen efectivamente este mecanismo, y así superar los desafíos introducidos por las comunicaciones masivas M2M. En el segundo escenario, dado que la heterogeneidad de los dispositivos IoT y las arquitecturas celulares basadas en hardware imponen desafíos aún mayores para permitir una comunicación flexible y eficiente en los sistemas inalámbricos 5G, esta tesis doctoral también contribuye al diseño de software-defined gateways (SD-GWs) en una nueva arquitectura propuesta para redes inalámbricas definidas por software que se denomina SoftAir. Esto permite manejar tanto un gran número de dispositivos como el volumen de datos que estarán vertiendo en la red. Otra contribución de esta tesis doctoral es la propuesta de una técnica novedosa para el análisis de prestaciones de redes multiservicio de alta capacidad que se basa en un nuevo enfoque del modelizado analítico de sistemas que operan a diferentes escalas temporales. Este enfoque utiliza el análisis del transitorio de una serie de subcadenas absorbentes y lo denominamos absorbing Markov chain approximation (AMCA). Nuestros resultados muestran que para un coste computacional dado, AMCA calcula los parámetros de prestaciones habituales de un sistema con mayor precisión, en comparación con los resultados obtenidos por otr
Nowadays, Internet of Things (IoT) is an essential technology for the upcoming generation of wireless systems. Connectivity is the foundation for IoT, and the type of access required will depend on the nature of the application. One of the leading facilitators of the IoT environment is machine-to-machine (M2M) communication, and particularly, its tremendous potential to offer ubiquitous connectivity among intelligent devices. Cellular networks are the natural choice for emerging IoT and M2M applications. A major challenge in cellular networks is to make the network capable of handling massive access scenarios in which myriad devices deploy M2M communications. On the other hand, cellular systems have seen a tremendous development in recent decades; they incorporate sophisticated technology and algorithms to offer a broad range of services. The modeling and performance analysis of these large multi-service networks is also a challenging task that might require high computational effort. To address the above challenges, we first concentrate on the design and performance evaluation of novel access control schemes to deal with massive M2M communications. Then, we focus on the performance evaluation of large multi-service networks and propose a novel analytical technique that features accuracy and computational efficiency. Our main objective is to provide solutions to ease the congestion in the radio access or core network when massive M2M devices try to connect to the network. We consider the following two types of scenarios: (i) massive M2M devices connect directly to cellular base stations, and (ii) they form clusters and the data is forwarded to gateways that provide them with access to the infrastructure. In the first scenario, as the number of devices added to the network is constantly increasing, the network should handle the considerable increment in access requests. Access class barring (ACB) is proposed by the 3rd Generation Partnership Project (3GPP) as a practical congestion control solution in the radio access and core network. The proper tuning of the ACB parameters according to the traffic intensity is critical, but how to do so dynamically and autonomously is a challenging task that has not been specified. Thus, this dissertation contributes to the performance analysis and optimal design of novel algorithms to implement effectively this barring scheme and overcome the challenges introduced by massive M2M communications. In the second scenario, since the heterogeneity of IoT devices and the hardware-based cellular architectures impose even greater challenges to enable flexible and efficient communication in 5G wireless systems, this dissertation also contributes to the design of software-defined gateways (SD-GWs) in a new architecture proposed for wireless software-defined networks called SoftAir. The deployment of these SD-GWs represents an alternative solution aiming at handling both a vast number of devices and the volume of data they will be pouring into the network. Another contribution of this dissertation is to propose a novel technique for the performance analysis of large multi-service networks. The underlying complexity of the network, particularly concerning its size and the ample range of configuration options, makes the solution of the analytical models computationally costly. However, a typical characteristic of these networks is that they support multiple types of traffic flows operating at different time-scales. This time-scale separation can be exploited to reduce considerably the computational cost associated to determine the key performance indicators. Thus, we propose a novel analytical modeling approach based on the transient regime analysis, that we name absorbing Markov chain approximation (AMCA). For a given computational cost, AMCA finds common performance indicators with greater accuracy, when compared to the results obtained by other approximate methods proposed in the literature.
En l'actualitat, la Internet de les Coses (Internet of Things, IoT) és una tecnologia essencial per a la propera generació de sistemes sense fil. La connectivitat és la base d'IoT, i el tipus d'accés requerit dependrà de la naturalesa de l'aplicació. Un dels principals facilitadors de l'entorn IoT és la comunicació machine-to-machine (M2M) i, en particular, el seu enorme potencial per oferir connectivitat ubiqua entre dispositius intel · ligents. Les xarxes mòbils són l'elecció natural per a les aplicacions emergents de IoT i M2M. Un desafiament important en les xarxes mòbils que actualment está rebent molta atenció és aconseguir que la xarxa siga capaç de gestionar escenaris d'accés massiu en què una gran quantitat de dispositius utilitzen comunicacions M2M. D'altra banda, els sistemes mòbils han experimentat un gran desenvolupament en les últimes dècades: incorporen tecnologia sofisticada i nous algoritmes per oferir una àmplia gamma de serveis. El modelatge i análisi del rendiment d'aquestes xarxes multiservei és també un desafiament important que podria requerir un gran esforç computacional. Per abordar els desafiaments anteriors, en aquesta tesi doctoral ens centrem en primer lloc en el disseny i l'avaluació de les prestacions de nous mecanismes de control d'accés per fer front a les comunicacions massives M2M en xarxes cel · lulars. Posteriorment ens ocupem de l'avaluació de prestacions de xarxes multiservei i proposem una nova tècnica analítica que ofereix precisió i eficiència computacional. El nostre principal objectiu és proporcionar solucions per a alleujar la congestió a la xarxa d'accés ràdio quan un gran nombre de dispositius M2M intenten connectar-se a la xarxa. Considerem els dos tipus d'escenaris següents: (i) els dispositius M2M es connecten directament a les estacions base cel · lulars, i (ii) formen grups i les dades s'envien a concentradors de trànsit (gateways) que els proporcionen accés a la infraestructura. En el primer escenari, atès que el nombre de dispositius afegits a la xarxa augmenta contínuament, aquesta hauria de ser capaç de gestionar el considerable increment en les sol · licituds d'accés. El 3rd Generation Partnership Project (3GPP) ha proposat l'access class barring (ACB) com una solució pràctica per al control de congestió a la xarxa d'accès ràdio i la xarxa troncal. L'ajust correcte dels paràmetres d'ACB d'acord amb la intensitat del trànsit és crític, però com fer-ho de forma dinàmica i autònoma és un problema complex, la solució del qual no està recollida en les especificacions del 3GPP. Aquesta tesi doctoral contribueix a l'anàlisi del rendiment i al disseny de nous algoritmes que implementen efectivament aquest mecanisme, i així superar els desafiaments introduïts per les comunicacions massives M2M en les xarxes mòbils actuals i futures. En el segon escenari, atès que l'heterogeneïtat dels dispositius IoT i les arquitectures cel · lulars basades en hardware imposen desafiaments encara més grans per permetre una comunicació flexible i eficient en els sistemes sense fil 5G, aquesta tesi doctoral també contribueix al disseny de software-defined gateways (SD-GWS) en una nova arquitectura proposada per a xarxes sense fils definides per programari que s'anomena SoftAir. Això permet gestionar tant un gran nombre de dispositius com el volum de dades que estaran abocant a la xarxa. Una altra contribució d'aquesta tesi doctoral és la proposta d'una tècnica innovadora per a l'anàlisi de prestacions de xarxes multiservei d'alta capacitat que es basa en un nou enfocament del modelitzat analític de sistemes que operen a diferents escales temporals. Aquest enfocament utilitza l'anàlisi del transitori d'una sèrie de subcadenes absorbents i l'anomenem absorbing Markov chain Approximation (AMCA). Els nostres resultats mostren que per a un cost computacional donat, AMCA calcula els paràmetres de prestacions habituals d
Tello Oquendo, LP. (2018). Design and Performance Analysis of Access Control Mechanisms for Massive Machine-to-Machine Communications in Wireless Cellular Networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/107946
TESIS

Wireless communication systems is a well-researched area in electrical engineering that has continually evolved over the past decades. This constant evolution and development have led to well-formulated theoretical baselines in terms of reliability and efficiency. However, most communication baselines are derived by splitting the baseband communications into a series of modular blocks like modulation, coding, channel estimation, and orthogonal frequency modulation. Subsequently, these blocks are independently optimized. Although this has led to a very efficient and reliable process, a theoretical verification of the optimality of this design process is not feasible due to the complexities of each individual block. In this work, we propose two modifications to these conventional wireless systems. First, with the goal of designing better space-time block codes for improved reliability, we propose to redesign the transmit and receive blocks of the physical layer. We replace a portion of the transmit chain - from modulation to antenna mapping with a neural network. Similarly, the receiver/decoder is also replaced with a neural network. In other words, the first part of this work focuses on jointly optimizing the transmit and receive blocks to produce a set of space-time codes that are resilient to Rayleigh fading channels. We compare our results to the conventional orthogonal space-time block codes for multiple antenna configurations. The second part of this work investigates the possibility of designing a distributed multiagent reinforcement learning-based multi-access algorithm for machine type communication. This work recognizes that cellular networks are being proposed as a solution for the connectivity of machine type devices (MTDs) and one of the most crucial aspects of scheduling in cellular connectivity is the random access procedure. The random access process is used by conventional cellular users to receive an allocation for the uplink transmissions. This process usually requires six resource blocks. It is efficient for cellular users to perform this process because transmission of cellular data usually requires more than six resource blocks. Hence, it is relatively efficient to perform the random access process in order to establish a connection. Moreover, as long as cellular users maintain synchronization, they do not have to undertake the random access process every time they have data to transmit. They can maintain a connection with the base station through discontinuous reception. On the other hand, the random access process is unsuitable for MTDs because MTDs usually have small-sized packets. Hence, performing the random access process to transmit such small-sized packets is highly inefficient. Also, most MTDs are power constrained, thus they turn off when they have no data to transmit. This means that they lose their connection and can't maintain any form of discontinuous reception. Hence, they perform the random process each time they have data to transmit. Due to these observations, explicit scheduling is undesirable for MTC. To overcome these challenges, we propose bypassing the entire scheduling process by using a grant free resource allocation scheme. In this scheme, MTDs pseudo-randomly transmit their data in random access slots. Note that this results in the possibility of a large number of collisions during the random access slots. To alleviate the resulting congestion, we exploit a heterogeneous network and investigate the optimal MTD-BS association which minimizes the long term congestion experienced in the overall cellular network. Our results show that we can derive the optimal MTD-BS association when the number of MTDs is less than the total number of random access slots.
Master of Science
Wireless communication systems is a well researched area of engineering that has continually evolved over the past decades. This constant evolution and development has led to well formulated theoretical baselines in terms of reliability and efficiency. This two part thesis investigates the possibility of improving these wireless systems with machine learning. First, with the goal of designing more resilient codes for transmission, we propose to redesign the transmit and receive blocks of the physical layer. We focus on jointly optimizing the transmit and receive blocks to produce a set of transmit codes that are resilient to channel impairments. We compare our results to the current conventional codes for various transmit and receive antenna configuration. The second part of this work investigates the possibility of designing a distributed multi-access scheme for machine type devices. In this scheme, MTDs pseudo-randomly transmit their data by randomly selecting time slots. This results in the possibility of a large number of collisions occurring in the duration of these slots. To alleviate the resulting congestion, we employ a heterogeneous network and investigate the optimal MTD-BS association which minimizes the long term congestion experienced in the overall network. Our results show that we can derive the optimal MTD-BS algorithm when the number of MTDs is less than the total number of slots.

UAV is widely used in civil applications such as environmental hazards monitoring, traffic management and pollution monitoring, all of those contribute to smart city development. Thanks to its high mobility and feasibility, UAVs can be employed as a base station, gathering data from IoT devices distribute in a certain area during its flight. In this case, an appropriate trajectory and suitable parameters set is necessary to achieve better performance. This thesis studies a scenario of NB-IoT machine-type communication network served by an unmanned aerial base station (UAB). In this scenario, the user devices are deployed with Tomas Cluster Process (TCP)[19]. Some of the nodes are named parent nodes and generated with Poisson Point Process (PPP). One single UAB is employed and its trajectory is predefined with a Travelling Salesman Problem model among the parent nodes. All the devices only generate one data packet for UL, whose activation time and expiration time is considered. Due to NB-IoT protocols, the number of resource units available on NPUSCH and the data rate for IoT devices is constrained. This study makes the network throughput and the number of users served as the main basis of performance evaluation, the variation of UAB speed, NPRACH periodicity, deployment variance, and size of the data packet as influencing factors. Finally, we present an analysis of how these parameters affect the overall performance and how the optimal configuration may be chosen according to arbitrary criteria.

Avec le nombre croissant d’appareils intelligents à faible puissance, au cours ces dernières années, la question de l’efficacité énergétique a joué un rôle de plus en plus indispensable dans la conception des systèmes de communication. Cette thèse vise à concevoir des schémas de transmission distribués à faible consommation d’énergie pour les réseaux sans fil, utilisant la théorie des jeux et le codage réseau instantanément décodable (IDNC), qui est une sous-classe prometteuse du codage réseau. En outre, nous étudions le modèle de l'échange coopératif de donnée (CDE) dans lequel tous les périphériques coopèrent en échangeant des paquets codés dans le réseau, jusqu’à ce qu’ils récupèrent tous l’ensemble des informations requises. En effet, la mise en œuvre du CDE basé sur l’IDNC soulève plusieurs défis intéressants, notamment la prolongation de la durée de vie du réseau et la réduction du nombre de transmissions afin de répondre aux besoins des applications temps réel. Par conséquent, contrairement à la plupart des travaux existants concernant l’IDNC, nous nous concentrons non seulement sur le délai, mais également sur l’énergie consommée. En premier lieu, nous étudions le problème de minimisation de l’énergie consommée et du délai au sein d’un petit réseau IDNC coopératif, entièrement connecté et à faible puissance. Nous modélisons le problème en utilisant la théorie des jeux coopératifs de formation de coalitions. Nous proposons un algorithme distribué (appelé “merge and split“) permettant aux nœuds sans fil de s’auto-organiser, de manière distribuée, en coalitions disjointes et indépendantes. L’algorithme proposé garantit une consommation d’énergie réduite et minimise le délai de complétion dans le réseau clustérisé résultant. Par ailleurs, nous ne considérons pas seulement l'énergie de transmission, mais aussi la consommation de l'énergie de calcul des nœuds. De plus, nous nous concentrons sur la question de la mobilité et nous analysons comment, à travers la solution proposée, les nœuds peuvent s’adapter à la topologie dynamique du réseau. Par la suite, nous étudions le même problème au sein d’un réseau large et partiellement connecté. En effet, nous examinons le modèle de CDE multi-sauts. Dans un tel modèle, nous considérons que les nœuds peuvent choisir la puissance d’émission et change ainsi de rayon de transmission et le nombre de voisin avec lesquels il peut entrer en coalition. Pour ce faire, nous modélisons le problème avec un jeu à deux étages; un jeu non-coopératif de contrôle de puissance et un jeu coopératif de formation de coalitions. La solution optimale du premier jeu permet aux joueurs de coopérer à travers des rayons de transmission limités en utilisant la théorie des jeux coopérative. En outre, nous proposons un algorithme distribué “merge and split“ afin de former des coalitions dans lesquelles les joueurs maximisent leurs utilités en termes de délai et de consommation d’énergie. La solution proposée permet la création d’une partition stable avec une interférence réduite et une complexité raisonnable. Nous démontrons que la coopération entre les nœuds au sein du réseau résultant, permet de réduire considérablement la consommation d’énergie par rapport au modèle coopératif optimal qui maintient le rayon de transmission maximal
With significantly growing number of smart low-power devices during recent years, the issue of energy efficiency has taken an increasingly essential role in the communication systems’ design. This thesis aims at designing distributed and energy efficient transmission schemes for wireless networks using game theory and instantly decodable network coding (IDNC) which is a promising network coding subclass. We study the cooperative data exchange (CDE) scenario in which all devices cooperate with each other by exchanging network coded packets until all of them receive all the required information. In fact, enabling the IDNC-based CDE setting brings several challenges such us how to extend the network lifetime and how to reduce the number of transmissions in order to satisfy urgent delay requirements. Therefore, unlike most of existing works concerning IDNC, we focus not only on the decoding delay, but also the consumed energy. First, we investigate the IDNC-based CDE problem within small fully connected networks across energy-constrained devices and model the problem using the cooperative game theory in partition form. We propose a distributed merge-and-split algorithm to allow the wireless nodes to self-organize into independent disjoint coalitions in a distributed manner. The proposed algorithm guarantees reduced energy consumption and minimizes the delay in the resulting clustered network structure. We do not only consider the transmission energy, but also the computational energy consumption. Furthermore, we focus on the mobility issue and we analyse how, in the proposed framework, nodes can adapt to the dynamic topology of the network. Thereafter, we study the IDNC-based CDE problem within large-scale partially connected networks. We considerate that each player uses no longer his maximum transmission power, rather, he controls his transmission range dynamically. In fact, we investigate multi-hop CDE using the IDNC at decentralized wireless nodes. In such model, we focus on how these wireless nodes can cooperate in limited transmission ranges without increasing the IDNC delay nor their energy consumption. For that purpose, we model the problem using a two-stage game theoretical framework. We first model the power control problem using non-cooperative game theory where users jointly choose their desired transmission power selfishly in order to reduce their energy consumption and their IDNC delay. The optimal solution of this game allows the players at the next stage to cooperate with each other through limited transmission ranges using cooperative game theory in partition form. Thereafter, a distributed multihop merge-and-split algorithm is defined to form coalitions where players maximize their utilities in terms of decoding delays and energy consumption. The solution of the proposed framework determines a stable feasible partition for the wireless nodes with reduced interference and reasonable complexity. We demonstrate that the co-operation between nodes in the multihop cooperative scheme achieves a significant minimization of the energy consumption with respect to the most stable cooperative scheme in maximum transmission range without hurting the IDNC delay

L'Internet des objets (IoT) fait référence à la croissance continue des réseaux d'objets du quotidien qui s'interconnectent entre eux ou avec d'autres systèmes Internet via les capteurs sans fil qui y sont attachés. L'IoT promet un futur où des milliards de terminaux intelligents seront connectés et gérés via une gamme de réseaux de communication et de serveurs basés dans le cloud, permettant ainsi l'apparition d'un large spectre d’applications de surveillance et de contrôle. Les communications machine-à-machine (M2M), également connues sous le nom de “Machine-Type-Communication” (MTC) par les réseaux cellulaires, constituent une technologie clé permettant d'activer partiellement l'IoT. Les communications M2M sont un nouveau paradigme qui facilite la connectivité omniprésente entre une myriade de dispositifs sans ou avec intervention humaine limitée. La demande croissante de connectivité a mis au défi les opérateurs de réseau à concevoir de nouveaux algorithmes d'allocation de ressources radio pour gérer l'échelle massive des communications MTC.Contrairement aux technologies d'accès radio traditionnelles, adaptées aux communications usuelles, dites de humain-à-humain (H2H), l'objectif de cette thèse est de développer de nouvelles techniques de partage de ressources radio efficaces et adaptatives pour les MTC dans un scénario de coexistence H2H/M2M. Dans le cadre de cette thèse, notre première contribution consiste en la proposition d'un système d'accès multiple adapté pour résoudre à la fois les problèmes liés à la rareté des ressources radio, à la scalabilité et à la surcharge de la station de base (BS). À cette fin, nous proposons de décomposer les opérations de communication en les groupant. Ainsi, les MTC correspondent à des communications locales en liaison montante entre des dispositifs connus sous le nom de “Machine-Type-Device” (MTD), et un cluster head appelé “Machine-Type- Head” (MTH). Nous examinons ainsi la nécessité d'agréger la technologie M2M et le “dispositif-à-dispositif” (D2D), considéré comme composante majeure des réseaux cellulaires évolutifs du futur. Nous modélisons le problème de partage de ressources radio entre les MTDs et les utilisateurs H2H sous la forme d’un graphe biparti et développons un algorithme de partage de ressources radio pour MTC basé sur les graphes afin d’atténuer les interférences co-canal et donc améliorer l'efficacité du réseau. En outre, une solution semi-distribuée de faible complexité est développée pour atténuer la surcharge de communication d'une solution centralisée que nous proposons également. Ensuite, dans une deuxième contribution de cette thèse, nous nous intéressons à examiner comment les dispositifs M2M peuvent partager les ressources radio disponibles sans pour autant dégrader les performances des applications H2H. Par conséquent, nous proposons un système de partage de ressources efficace en terme de spectre et de puissance. Nous introduisons à l'algorithme de partage de ressources radio basé sur les graphes une fonction adaptative de contrôle de puissance utilisant l'un des deux mécanismes suivants : un contrôleur proportionnel intégral dérivé (PID) et la logique floue. Enfin, comme troisième contribution de cette thèse, nous développons un système de partage de ressources radio efficace en terme de puissance et entièrement distribué pour les MTC. Nous utilisons la théorie des jeux et modélisons le problème de partage de ressources par un jeu hybride où les dispositifs M2M rivalisent pour les ressources radio et basculent de façon opportuniste entre un jeu non-coopératif et un jeu coopératif. Une évaluation des performances des solutions dérivées dans le contexte des réseaux LTE est menée. Les résultats des simulations montrent que les solutions proposées ont un impact significatif sur la maximisation de l'efficacité de l'utilisation du spectre, l'atténuation de l'effet négatif sur les services H2H et la prolongation de la durée de vie des batteries des MTDs
The Internet-of-Things (IoT) refers to the ever-growing network of everyday objects that interconnect to each other or to other Internet-enabled systems via wireless sensors attached to them. IoT envisions a future where billions of smart devices will be connected and managed through a range of communication networks and cloud-based servers, enabling a variety of monitoring and control applications. Machine-to-Machine (M2M) communications supported by cellular networks, also known as Machine-Type-Communications (MTC) acts as a key technology for partially enabling IoT. M2M communications is a new technology paradigm that facilitates the ubiquitous connectivity between a myriad of devices without requiring human intervention. The surge in the demand for connectivity has further challenged network operators to design novel radio resource allocation algorithms at affordable costs to handle the massive scale of MTC.Different from current radio access technologies tailored to traditional Human- to-Human (H2H) communications, the goal of this thesis is to provide novel efficient and adaptive radio resource sharing schemes for MTC under a H2H/M2M coexistence scenario. We first provide a suitable multiple access scheme to address the joint spectrum scarcity, scalability and Base Station (BS) overload issues. Toward this end, we design a group-based operation where MTC corresponds to local uplink communications between Machine-Type-Devices (MTDs), which represent a specific type of devices that do not rely on the presence of a human interface, and a Machine-Type-Head (MTH). This latter plays the role of a cluster head that relays the information to the BS. We thus address the need to aggregate M2M and Device-to-Device (D2D) technology, as one of the major components of the future evolving cellular networks. Having said that, we first propose in this thesis to model the radio resource sharing problem between MTDs and H2H users as a bipartite graph and develop a novel interference-aware graph-based radio resource sharing algorithm for MTC so as to mitigate the co-channel interference and thus enhance network efficiency. Moreover, low-complexity semi-distributed solution is investigated to alleviate the communication overhead of a centralized solution that we propose as well. Then, as a second contribution, we examine how M2M devices can share the available radio resources in cellular networks with no or limited impact on existing H2H services. Consequently, we propose a joint spectrally and power efficient radio resource sharing scheme. Convinced by the strength of the bipartite graph modeling for the resource sharing problem between H2H users and M2M devices, we empower the graph-based radio resource sharing algorithm with a novel adaptive power control feature using one of two following mechanisms: the Proportional Integral Derivative (PID) controller and the fuzzy logic. Finally, in our third contribution of this thesis, we develop a power efficient and fully-distributed radio resource sharing framework for MTC underlying cellular networks. We use game theory and model the resource sharing problem as an efficient hybrid-game where M2M devices compete for radio resources and switch opportunistically, as M2M devices are selfish in nature, between non-cooperative and cooperative games. The different derived solutions are extended to existing cellular networks, and extensive simulation studies in the context of LTE are conducted. The various simulation results show that the proposed solutions can significantly increase the efficiency of the spectrum usage, mitigate the negative effect on H2H services and save the battery life of M2M devices

Face à la diversité des besoins en communication des réseaux 5G et de la future 6G, l’allocation des ressources disponibles est considérée comme un élément clé pour augmenter la densité de dispositifs, leur débit ou la fiabilité de leurs communications. Dans les réseaux de communication de type machine, des travaux récents ont proposé d’adapter l’allocation des ressources temporelles en fonction du processus sous-jacent qui régit l’activité des dispositifs. Cette thèse se concentre tout d’abord sur l’étude de l’impact d’une connaissance imparfaite de ce processus, et propose des méthodes pour atténuer le biais induit par les connaissances erronées. En second lieu, un algorithme permettant d’optimiser conjointement l’allocation des ressources temporelles et la puissance de transmission des dispositifs est proposé. L’algorithme permet aux dispositifs ayant une forte probabilité de transmettre au même moment, de le faire sur des ressources (temporelles ou de puissance) assurant leur décodabilité. Enfin, dans les réseaux ayant un objectif de haut débit, nous proposons d’optimiser conjointement la puissance, les ressources fréquentielles ainsi que le nombre de flux de données parallèles utilisées par les dispositifs. Notre étude par simulations témoigne que notre optimisation conjointe est plus performante que les méthodes utilisées actuellement en 5G pour lesquelles ces paramètres sont calculés indépendamment les uns des autres
Facing the diversity of communication needs of 5G networks and the future 6G, resource allocation is considered as a key enabler to increase the number of devices, the data rate or the reliability of the communication links. In machine-type communication networks, recent work has proposed to adapt the temporal resource allocation as a function of the underlying process driving the activity of the devices. This thesis firstly focuses on the impact of having only limited knowledge of the underlying process, and proposes methods to mitigate the bias induced by the lack of knowledge. Secondly, an algorithm for the joint optimization of the temporal resource allocation and the transmit power of the devices is proposed. The algorithm ensures that devices that are likely to transmit on the same resources do so with a sufficient power diversity to ensure their decodability by the base station. Finally, in networks with an enhanced mobile broadband objective, we propose to jointly optimize the power, the frequency resources used, as well as the number of parallel data streams used by the devices. Our simulation study shows that our joint optimization outperforms current 5G baselines for which these parameters are common to all devices of the cell

Several UE energy consumption optimization techniques have been proposed for Machine Type Communication (MTC) devices. Extended Discontinuous Reception (eDRX) in idle mode is one such technique wherein an UE in idle mode wakes up only during its Paging Occasion (PO) to monitor paging messages from eNodeB (eNB). The PO is located within a Paging frame (PF). The PF is a function of System Frame Number (SFN) cycle of eNB. The paging messages may be sent asynchronously from multiple eNBs to a UE. Due to asynchronous operation of eNBs, SFN takes on different values at a given point in time and therefore a paging message is transmitted at different points in time from different eNBs. Due to this SFN misalignment between eNBs, an idle mode UE might receive and respond to the same paging message from different eNBs and/or miss a PO and thus the paging message. Due to this spread in time of SFN and PO, the actual handling of paging message by the UE becomes inefficient leading to increased UE energy consumption and decreased reachability. These issues, resulting from paging handling, will get amplified further if DRX period is extended longer (eDRX). In this study, we investigate the impact of eDRX cycles and mobility related parameters such as UE speed, cell size and size of SFN misalignment between eNBs on UE energy consumption, use of network resources and UE reachability. Receiving and responding to the same paging message results in increased energy consumption for UE and increased signaling between UE and the network. Missing a PO results in delayed paging reception and hence decreases UE reachability. As the DRX cycle lengths are increased from existing maximum of 2.56 seconds to 10.24 seconds and beyond, we see a reduction in UE energy consumption by more than 90%, but the network signaling and the delay to reach the UE increases linearly as a function of the DRX cycle length. We observe that the number of duplicate paging message receptions/missed POs is minuscule for DRX cycle lengths below 10.24 sec. At DRX cycle length of 10.24 seconds, UEs travelling across 500 m cell radius at speeds of 3, 50, 100 km/h the percentage of duplicate paging receptions are 0.07, 0.11, and 0.15 respectively. This duplicate paging message reception increases the UE energy consumption by 2.31, 6.15 and 12 percent of the total energy units respectively. Similarly, UE misses nearly 0.34, 0.39, and 0.405 percent of the total POs respectively. Depending on the number of consecutive PO misses, the UE reachability decreases. But by reducing the size of SFN misalignment between eNBs, we see that it’s possible to increase the reachability for UEs in eDRX. Further we have proposed solutions based on our analytical study to avoid duplicate paging message reception by UE, increase UE reachability and also reduce UE energy consumption using a windowing technique. We conclude that when a UE is configured with eDRX cycles, the tradeoff between battery lifetimes and UE reachability is based on mobility characteristics and service requirements.

La quinta generación de redes móviles (5G) se encuentra a la vuelta de la esquina. Se espera provea de beneficios extraordinarios a la población y que resuelva la mayoría de los problemas de las redes 4G actuales. El éxito de 5G, cuya primera fase de estandarización ha sido completada, depende de tres pilares: comunicaciones tipo-máquina masivas, banda ancha móvil mejorada y comunicaciones ultra fiables y de baja latencia (mMTC, eMBB y URLLC, respectivamente). En esta tesis nos enfocamos en el primer pilar de 5G, mMTC, pero también proveemos una solución para lograr eMBB en escenarios de distribución masiva de contenidos. Específicamente, las principales contribuciones son en las áreas de: 1) soporte eficiente de mMTC en redes celulares; 2) acceso aleatorio para el reporte de eventos en redes inalámbricas de sensores (WSNs); y 3) cooperación para la distribución masiva de contenidos en redes celulares. En el apartado de mMTC en redes celulares, esta tesis provee un análisis profundo del desempeño del procedimiento de acceso aleatorio, que es la forma mediante la cual los dispositivos móviles acceden a la red. Estos análisis fueron inicialmente llevados a cabo por simulaciones y, posteriormente, por medio de un modelo analítico. Ambos modelos fueron desarrollados específicamente para este propósito e incluyen uno de los esquemas de control de acceso más prometedores: access class barring (ACB). Nuestro modelo es uno de los más precisos que se pueden encontrar en la literatura y el único que incorpora el esquema de ACB. Los resultados obtenidos por medio de este modelo y por simulación son claros: los accesos altamente sincronizados que ocurren en aplicaciones de mMTC pueden causar congestión severa en el canal de acceso. Por otro lado, también son claros en que esta congestión se puede prevenir con una adecuada configuración del ACB. Sin embargo, los parámetros de configuración del ACB deben ser continuamente adaptados a la intensidad de accesos para poder obtener un desempeño óptimo. En la tesis se propone una solución práctica a este problema en la forma de un esquema de configuración automática para el ACB; lo llamamos ACBC. Los resultados muestran que nuestro esquema puede lograr un desempeño muy cercano al óptimo sin importar la intensidad de los accesos. Asimismo, puede ser directamente implementado en redes celulares para soportar el tráfico mMTC, ya que ha sido diseñado teniendo en cuenta los estándares del 3GPP. Además de los análisis descritos anteriormente para redes celulares, se realiza un análisis general para aplicaciones de contadores inteligentes. Es decir, estudiamos un escenario de mMTC desde la perspectiva de las WSNs. Específicamente, desarrollamos un modelo híbrido para el análisis de desempeño y la optimización de protocolos de WSNs de acceso aleatorio y basados en cluster. Los resultados muestran la utilidad de escuchar el medio inalámbrico para minimizar el número de transmisiones y también de modificar las probabilidades de transmisión después de una colisión. En lo que respecta a eMBB, nos enfocamos en un escenario de distribución masiva de contenidos, en el que un mismo contenido es enviado de forma simultánea a un gran número de usuarios móviles. Este escenario es problemático, ya que las estaciones base de la red celular no cuentan con mecanismos eficientes de multicast o broadcast. Por lo tanto, la solución que se adopta comúnmente es la de replicar e contenido para cada uno de los usuarios que lo soliciten; está claro que esto es altamente ineficiente. Para resolver este problema, proponemos el uso de esquemas de network coding y de arquitecturas cooperativas llamadas nubes móviles. En concreto, desarrollamos un protocolo para la distribución masiva de contenidos, junto con un modelo analítico para su optimización. Los resultados demuestran que el modelo propuesto es simple y preciso, y que el protocolo puede reducir el con
La cinquena generació de xarxes mòbils (5G) es troba molt a la vora. S'espera que proveïsca de beneficis extraordinaris a la població i que resolga la majoria dels problemes de les xarxes 4G actuals. L'èxit de 5G, per a la qual ja ha sigut completada la primera fase del qual d'estandardització, depén de tres pilars: comunicacions tipus-màquina massives, banda ampla mòbil millorada, i comunicacions ultra fiables i de baixa latència (mMTC, eMBB i URLLC, respectivament, per les seues sigles en anglés). En aquesta tesi ens enfoquem en el primer pilar de 5G, mMTC, però també proveïm una solució per a aconseguir eMBB en escenaris de distribució massiva de continguts. Específicament, les principals contribucions són en les àrees de: 1) suport eficient de mMTC en xarxes cel·lulars; 2) accés aleatori per al report d'esdeveniments en xarxes sense fils de sensors (WSNs); i 3) cooperació per a la distribució massiva de continguts en xarxes cel·lulars. En l'apartat de mMTC en xarxes cel·lulars, aquesta tesi realitza una anàlisi profunda de l'acompliment del procediment d'accés aleatori, que és la forma mitjançant la qual els dispositius mòbils accedeixen a la xarxa. Aquestes anàlisis van ser inicialment dutes per mitjà de simulacions i, posteriorment, per mitjà d'un model analític. Els models van ser desenvolupats específicament per a aquest propòsit i inclouen un dels esquemes de control d'accés més prometedors: el access class barring (ACB). El nostre model és un dels més precisos que es poden trobar i l'únic que incorpora l'esquema d'ACB. Els resultats obtinguts per mitjà d'aquest model i per simulació són clars: els accessos altament sincronitzats que ocorren en aplicacions de mMTC poden causar congestió severa en el canal d'accés. D'altra banda, també són clars en què aquesta congestió es pot previndre amb una adequada configuració de l'ACB. No obstant això, els paràmetres de configuració de l'ACB han de ser contínuament adaptats a la intensitat d'accessos per a poder obtindre unes prestacions òptimes. En la tesi es proposa una solució pràctica a aquest problema en la forma d'un esquema de configuració automàtica per a l'ACB; l'anomenem ACBC. Els resultats mostren que el nostre esquema pot aconseguir un acompliment molt proper a l'òptim sense importar la intensitat dels accessos. Així mateix, pot ser directament implementat en xarxes cel·lulars per a suportar el trànsit mMTC, ja que ha sigut dissenyat tenint en compte els estàndards del 3GPP. A més de les anàlisis descrites anteriorment per a xarxes cel·lulars, es realitza una anàlisi general per a aplicacions de comptadors intel·ligents. És a dir, estudiem un escenari de mMTC des de la perspectiva de les WSNs. Específicament, desenvolupem un model híbrid per a l'anàlisi de prestacions i l'optimització de protocols de WSNs d'accés aleatori i basats en clúster. Els resultats mostren la utilitat d'escoltar el mitjà sense fil per a minimitzar el nombre de transmissions i també de modificar les probabilitats de transmissió després d'una col·lisió. Pel que fa a eMBB, ens enfoquem en un escenari de distribució massiva de continguts, en el qual un mateix contingut és enviat de forma simultània a un gran nombre d'usuaris mòbils. Aquest escenari és problemàtic, ja que les estacions base de la xarxa cel·lular no compten amb mecanismes eficients de multicast o broadcast. Per tant, la solució que s'adopta comunament és la de replicar el contingut per a cadascun dels usuaris que ho sol·liciten; és clar que això és altament ineficient. Per a resoldre aquest problema, proposem l'ús d'esquemes de network coding i d'arquitectures cooperatives anomenades núvols mòbils. En concret, desenvolupem un protocol per a realitzar la distribució massiva de continguts de forma eficient, juntament amb un model analític per a la seua optimització. Els resultats demostren que el model proposat és simple i precís
The 5th generation (5G) of mobile networks is just around the corner. It is expected to bring extraordinary benefits to the population and to solve the majority of the problems of current 4th generation (4G) systems. The success of 5G, whose first phase of standardization has concluded, relies in three pillars that correspond to its main use cases: massive machine-type communication (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communication (URLLC). This thesis mainly focuses on the first pillar of 5G: mMTC, but also provides a solution for the eMBB in massive content delivery scenarios. Specifically, its main contributions are in the areas of: 1) efficient support of mMTC in cellular networks; 2) random access (RA) event-reporting in wireless sensor networks (WSNs); and 3) cooperative massive content delivery in cellular networks. Regarding mMTC in cellular networks, this thesis provides a thorough performance analysis of the RA procedure (RAP), used by the mobile devices to switch from idle to connected mode. These analyses were first conducted by simulation and then by an analytical model; both of these were developed with this specific purpose and include one of the most promising access control schemes: the access class barring (ACB). To the best of our knowledge, this is one of the most accurate analytical models reported in the literature and the only one that incorporates the ACB scheme. Our results clearly show that the highly-synchronized accesses that occur in mMTC applications can lead to severe congestion. On the other hand, it is also clear that congestion can be prevented with an adequate configuration of the ACB scheme. However, the configuration parameters of the ACB scheme must be continuously adapted to the intensity of access attempts if an optimal performance is to be obtained. We developed a practical solution to this problem in the form of a scheme to automatically configure the ACB; we call it access class barring configuration (ACBC) scheme. The results show that our ACBC scheme leads to a near-optimal performance regardless of the intensity of access attempts. Furthermore, it can be directly implemented in 3rd Generation Partnership Project (3GPP) cellular systems to efficiently handle mMTC because it has been designed to comply with the 3GPP standards. In addition to the analyses described above for cellular networks, a general analysis for smart metering applications is performed. That is, we study an mMTC scenario from the perspective of event detection and reporting WSNs. Specifically, we provide a hybrid model for the performance analysis and optimization of cluster-based RA WSN protocols. Results showcase the utility of overhearing to minimize the number of packet transmissions, but also of the adaptation of transmission parameters after a collision occurs. Building on this, we are able to provide some guidelines that can drastically increase the performance of a wide range of RA protocols and systems in event reporting applications. Regarding eMBB, we focus on a massive content delivery scenario in which the exact same content is transmitted to a large number of mobile users simultaneously. Such a scenario may arise, for example, with video streaming services that offer a particularly popular content. This is a problematic scenario because cellular base stations have no efficient multicast or broadcast mechanisms. Hence, the traditional solution is to replicate the content for each requesting user, which is highly inefficient. To solve this problem, we propose the use of network coding (NC) schemes in combination with cooperative architectures named mobile clouds (MCs). Specifically, we develop a protocol for efficient massive content delivery, along with the analytical model for its optimization. Results show the proposed model is simple and accurate, and the protocol can lead to energy savings of up to 37 percent when compared to the traditional approach.
Leyva Mayorga, I. (2018). On reliable and energy efficient massive wireless communications: the road to 5G [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/115484
TESIS

The methods of transmitting information may be divided as follows: direct; and, indirect. The âdirectâ method occurs when a creature transmits a signal that other creatures in its local environment can receive. Word of mouth advertising is a form of direct communication. âIndirectâ communication relays a message through the environment. This type of communication is known as stigmergy. Both word of mouth communication and stigmergy require the existence of groups of communicators. It is, however, difficult to analyse a very large number of local interactions that occur in group behaviour. A global phenomenon known as âemergenceâ arises from such behaviour. The phrase ââthe whole is greater than the sum of its partsâ normally describes emergence. In this research, we investigate how the two methods of communicating, direct and indirect (including a combination of these), result in emergent behaviour. In order to establish this outcome we employed the use of agent-based software in which we designed groups of agents to evolve over generations in response to specific situations. The manner in which these agent groups evolve is by a genetic algorithm. This is based on the consumption and collection of resources from the environment - a metric for gauging how well the population performs as a whole. For the purpose of this dissertation, we measure and examine the performance of four styles of the two methods of communication: No Communication, Word of Mouth, Stigmergic and Both (a combination of direct and indirect). We observe the fitness arising through successive generations of agents for each of the four styles and compare the results. The âNo Communicationâ style is markedly the worst performer and is âthe sum of the partsâ in terms of the definition of emergence. The âWord of Mouthâ style is marginally below the best performer but is rated well above that of âNo Communicationâ. The âStigmergicâ style is only the third best performer. Combining the direct and indirect methods yields the best result for the âBothâ style. All the communicating categories, considered âthe wholeâ in terms of the definition for emergence, outperform the âNo Communicationâ style. This demonstrates that emergence occurs when using these communication methods in groups. Keywords: Communication, Emergence, Genetic Algorithms, Group Behaviour

Cellular network based Machine-to-Machine (M2M) communications have been growing rapidly in recent years, being used in a wide range of services such as security, metering, health, remote control, tracking and so on. A critical issue that needs to be considered in M2M communications is the energy efficiency, typically the machines are powered by batteries of low capacity and it is important to optimize the way the power is consumed. In search of better M2M systems, we propose a context-aware framework for M2M communications so the machine type communication (MTC) devices dynamically adapt their settings depending on a series of characteristics such as data reporting mode and quality of service (QoS) features so higher energy efficient is achieved, extending the operating lifetime of the M2M network. Simulations were performed with four commonly used M2M applications:home security, telehealth, climate and smart metering, achieving considerable energy savings and operating lifetime extension on the network. Thus, it is shown that contexts play an important role on the energy efficiency of a M2M system.

The next-generation electric power system, known as smart grid, relies on a robust and reliable underlying communication infrastructure to improve the efficiency of electricity distribution. Cellular networks, e.g., LTE/LTE-A systems, appear as a promising technology to facilitate the smart grid evolution. Their inherent performance characteristics and well-established ecosystem could potentially unlock unprecedented use cases, enabling real-time and autonomous distribution grid operations. However, cellular technology was not originally intended for smart grid communication, associated with highly-reliable message exchange and massive device connectivity requirements. The fundamental differences between smart grid and human-type communication challenge the classical design of cellular networks and introduce important research questions that have not been sufficiently addressed so far. Motivated by these challenges, this doctoral thesis investigates novel radio access network (RAN) design principles and performance analysis for the seamless integration of smart grid traffic in future cellular networks. Specifically, we focus on addressing the fundamental RAN problems of network scalability in massive smart grid deployments and radio resource management for smart grid and human-type traffic. The main objective of the thesis lies on the design, analysis and performance evaluation of RAN mechanisms that would render cellular networks the key enabler for emerging smart grid applications. The first part of the thesis addresses the radio access limitations in LTE-based networks for reliable and scalable smart grid communication. We first identify the congestion problem in LTE random access that arises in large-scale smart grid deployments. To overcome this, a novel random access mechanism is proposed that can efficiently support real-time distribution automation services with negligible impact on the background traffic. Motivated by the stringent reliability requirements of various smart grid operations, we then develop an analytical model of the LTE random access procedure that allows us to assess the performance of event-based monitoring traffic under various load conditions and network configurations. We further extend our analysis to include the relation between the cell size and the availability of orthogonal random access resources and we identify an additional challenge for reliable smart grid connectivity. To this end, we devise an interference- and load-aware cell planning mechanism that enhances reliability in substation automation services. Finally, we couple the problem of state estimation in wide-area monitoring systems with the reliability challenges in information acquisition. Using our developed analytical framework, we quantify the impact of imperfect communication reliability in the state estimation accuracy and we provide useful insights for the design of reliability-aware state estimators. The second part of the thesis builds on the previous one and focuses on the RAN problem of resource scheduling and sharing for smart grid and human-type traffic. We introduce a novel scheduler that achieves low latency for distribution automation traffic while resource allocation is performed in a way that keeps the degradation of cellular users at a minimum level. In addition, we investigate the benefits of Device-to-Device (D2D) transmission mode for event-based message exchange in substation automation scenarios. We design a joint mode selection and resource allocation mechanism which results in higher data rates with respect to the conventional transmission mode via the base station. An orthogonal resource partition scheme between cellular and D2D links is further proposed to prevent the underutilization of the scarce cellular spectrum. The research findings of this thesis aim to deliver novel solutions to important RAN performance issues that arise when cellular networks support smart grid communication.
Las redes celulares, p.e., los sistemas LTE/LTE-A, aparecen como una tecnología prometedora para facilitar la evolución de la próxima generación del sistema eléctrico de potencia, conocido como smart grid (SG). Sin embargo, la tecnología celular no fue pensada originalmente para las comunicaciones en la SG, asociadas con el intercambio fiable de mensajes y con requisitos de conectividad de un número masivo de dispositivos. Las diferencias fundamentales entre las comunicaciones en la SG y la comunicación de tipo humano desafían el diseño clásico de las redes celulares e introducen importantes cuestiones de investigación que hasta ahora no se han abordado suficientemente. Motivada por estos retos, esta tesis doctoral investiga los principios de diseño y analiza el rendimiento de una nueva red de acceso radio (RAN) que permita una integración perfecta del tráfico de la SG en las redes celulares futuras. Nos centramos en los problemas fundamentales de escalabilidad de la RAN en despliegues de SG masivos, y en la gestión de los recursos radio para la integración del tráfico de la SG con el tráfico de tipo humano. El objetivo principal de la tesis consiste en el diseño, el análisis y la evaluación del rendimiento de los mecanismos de las RAN que convertirán a las redes celulares en el elemento clave para las aplicaciones emergentes de las SGs. La primera parte de la tesis aborda las limitaciones del acceso radio en redes LTE para la comunicación fiable y escalable en SGs. En primer lugar, identificamos el problema de congestión en el acceso aleatorio de LTE que aparece en los despliegues de SGs a gran escala. Para superar este problema, se propone un nuevo mecanismo de acceso aleatorio que permite soportar de forma eficiente los servicios de automatización de la distribución eléctrica en tiempo real, con un impacto insignificante en el tráfico de fondo. Motivados por los estrictos requisitos de fiabilidad de las diversas operaciones en la SG, desarrollamos un modelo analítico del procedimiento de acceso aleatorio de LTE que nos permite evaluar el rendimiento del tráfico de monitorización de la red eléctrica basado en eventos bajo diversas condiciones de carga y configuraciones de red. Además, ampliamos nuestro análisis para incluir la relación entre el tamaño de celda y la disponibilidad de recursos de acceso aleatorio ortogonales, e identificamos un reto adicional para la conectividad fiable en la SG. Con este fin, diseñamos un mecanismo de planificación celular que tiene en cuenta las interferencias y la carga de la red, y que mejora la fiabilidad en los servicios de automatización de las subestaciones eléctricas. Finalmente, combinamos el problema de la estimación de estado en sistemas de monitorización de redes eléctricas de área amplia con los retos de fiabilidad en la adquisición de la información. Utilizando el modelo analítico desarrollado, cuantificamos el impacto de la baja fiabilidad en las comunicaciones sobre la precisión de la estimación de estado. La segunda parte de la tesis se centra en el problema de scheduling y compartición de recursos en la RAN para el tráfico de SG y el tráfico de tipo humano. Presentamos un nuevo scheduler que proporciona baja latencia para el tráfico de automatización de la distribución eléctrica, mientras que la asignación de recursos se realiza de un modo que mantiene la degradación de los usuarios celulares en un nivel mínimo. Además, investigamos los beneficios del modo de transmisión Device-to-Device (D2D) en el intercambio de mensajes basados en eventos en escenarios de automatización de subestaciones eléctricas. Diseñamos un mecanismo conjunto de asignación de recursos y selección de modo que da como resultado tasas de datos más elevadas con respecto al modo de transmisión convencional a través de la estación base. Finalmente, se propone un esquema de partición de recursos ortogonales entre enlaces celulares y D2

Ce rapport présente une contribution à la réalisation d’un système construit selon le modèle d’interconnexion des systèmes ouverts défini par l’ISO (International Standard Organisation), en insistant sur la Couche Présentation et la Couche Application. La Couche Application comprend : a) un système de gestion de bases de données, centralisé sur une Machine Base de Données (le DORSAL32), utilisant les notions d’ "activités" et d’ "images internes des utilisateurs" pour contrôler les sollicitations externes ; b) un ensemble de "Gérants de Terminaux" en interaction avec le système de gestion de bases de données, offrant à l’utilisateur final une interface souple et indépendante des caractéristiques du terminal utilisé. La Couche Présentation, extensible, est constituée : a) d’une sous-couche de "Présentations Spécifiques" par "image de présentation" ; b) d’une sous-couche de "Présentation Basique" utilisant les fonctions de la Couche Session pour offrir une connexion-présentation découpée en "phases" séquentielles, chaque "phase" étant définie comme support d’exécution d’une Présentation Spécifique. Le système réalisé a été mis en exploitation sous le moniteur MMT2 sur le MITRA-525.

Jamming is an ongoing threat that plagues wireless communications in contested areas. Unfortunately, jamming complexity and sophistication will continue to increase over time. The traditional approach to addressing the jamming threat is to harden radios, such that they sacrifice communications performance for more advanced jamming protection. To provide an escape from this trend, we investigate the previously unexplored area of jammer exploitation. This dissertation develops the concept of antifragile communications, defined as the capability for a communications system to improve in performance due to a system stressor or harsh condition. Antifragility refers to systems that increase in capability, resilience, or robustness as a result of disorder (e.g., chaos, uncertainty, stress). An antifragile system is fundamentally different from one that is resilient (i.e., able to recover from failure) and robust (i.e., able to resist failure). We apply the concept of antifragility to wireless communications through several novel strategies that all involve exploiting a communications jammer. These strategies can provide an increase in throughput, efficiency, connectivity, or covertness, as a result of the jamming attack itself. Through analysis and simulation, we show that an antifragile gain is possible under a wide array of electronic warfare scenarios. Throughout this dissertation we provide guidelines for realizing these antifragile waveforms. Other major contributions of this dissertation include the development of a communications jamming taxonomy, feasibility study of reactive jamming in a SATCOM-type scenario, and a reinforcement learning-based reactive jamming mitigation strategy, for times when an antifragile approach is not practical. Most of the jammer exploitation strategies described in this dissertation fall under the category of jammer piggybacking, meaning the communications system turns the jammer into an unwitting relay. We study this jammer piggybacking approach under a variety of reactive jamming behaviors, with emphasis on the sense-and-transmit type. One piggybacking approach involves transmitting using a specialized FSK waveform, tailored to exploit a jammer that channelizes a block of spectrum and selectively jams active subchannels. To aid in analysis, we introduce a generalized model for reactive jamming, applicable to both repeater-based and sensing-based jamming behaviors. Despite being limited to electronic warfare scenarios, we hope that this work can pave the way for further research into antifragile communications.
Ph. D.

Cette thèse traite le problème de garantir la qualité de service (QoS) dans l'internet des objets (IoT) en termes d’urgence et de fiabilité. Pour atteindre cet objectif, nous avons proposé différentes solutions adaptées aux réseaux étendus LoRa (LoRaWAN). Premièrement, nous mettons en œuvre un découpage virtuel du réseau LoRa en plusieurs tranches et nous évaluons son impact en utilisant plusieurs stratégies statiques et dynamiques. Les résultats des simulations exécutés sur NS3 ont prouvé l'efficacité de l'isolation virtuelle des ressources physiques pour une tranche d'un réseau ayant des communications urgentes en réduisant l'impact venant des autres communications IoT. Motivés par ces résultats, une méthode d'optimisation est ensuite proposée pour chercher une meilleure configuration des nœuds LoRa en regardant plus en détail leur paramètres au niveau de la couche physique permettant d’améliorer ses performances en termes de QoS, de fiabilité et d’efficacité énergétique. Par contre, même avec le découpage du réseau en tranches virtuels, l’évolutivité de LoRa reste un défi en raison du manque de flexibilité lors de la gestion des réseaux sans fil actuels. Par conséquent, pour atteindre l'objectif global de garantir une bonne QoS dans un réseau IoT à grande échelle, les technologies SDN et le découpage virtuel du réseau sont adoptés simultanément pour proposer une architecture virtualisée et distribuée. Cette dernière proposition est basée sur la théorie des jeux et s'adapte plus rapidement aux changements dans un environnement IoT encombré en exploitant la prise de décision du découpage du réseau et la configuration des nœuds LoRa à la périphérie du réseau
This thesis deals with the problem of guaranteeing heterogeneous quality of service (QoS) requirements for Internet of Things (IoT) communications in terms of urgency and reliability. Various solutions are proposed towards achieving this goal in LoRa Wide Area Networks (LoRaWAN). First, we implement network slicing over LoRa standard architecture and evaluate its impact using various static and dynamic strategies. Simulation Results performed over NS3 proved the efficiency of network slicing in isolating physical resources for each slice and serving delay critical communications. Motivated by these results, a slice-based optimization is proposed next to improve the dynamic slicing strategy by investigating more LoRa parameters at the physical layer. The proposed method finds for each device the best parameters configuration that potentially improves the performance of its slice in terms of QoS, reliability and energy efficiency. Moreover, we also looked towards meeting upcoming challenges in future IoT networks that comes from the increasing number of IoT devices. Even with network slicing, LoRa scalability remained as a big challenge that should be carefully considered especially due to the lack of flexibility in managing current wireless networks. Therefore, to meet the global objective in guaranteeing QoS in large scale IoT deployments, software defined networking (SDN) and network slicing are adopted as backbone technologies for a distributed virtualized architecture and slicing strategy. The latter proposition is based on game theory and adapts faster to the changes in a congested IoT environment by leveraging slicing decision making closer to the edge

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 121-128).
Fibers and fabrics are among the earliest forms of human expression, and yet they have not progressed much from a functional standpoint over the course of history. Recently, a new family of fibers composed of conductors, semiconductors and insulators has emerged. These fibers can achieve device attributes, yet are fabricated using scalable preform-based fiber-processing methods, yielding kilometers of functional fiber devices. Co-draw of different materials is possible for numerous material combinations and sizes, where one of the limiting factors to a continuous feature size in fibers is the Rayleigh-Plateau capillary instability. In my thesis I have shown that it is possible to utilize this adverse fluid instability phenomenon to fabricate uniformly sized and uniformly structured spherical particles internal to the fibers. Judicial choice of the materials and control over the kinetics of this process allowed to integrate the spherical particles into an active fiber device. We have introduced additional ability to control the structure of the fiber by making this process selective, forming high density array of self-assembled spherical photodetectors, connected to continuous electrodes. This fiber structure shows enhanced photoconductivity and sensitivity to wavelength variation, due to spherical geometry of the photoresistive domains. Additionally, an alternative strategy for integrating active devices into fibers was demonstrated. Rather than addressing all the challenges of thermal drawing, we have developed a method to directly integrate commercial functional devices (light emitting diodes, photodetectors etc.) into fibers through thermal drawing. We package these devices internal to the fibers in high density and integrate them with conductive buses, during the thermal draw. This approach enables to combine the benefits of several technologies - high-efficiency devices integrated into kilometer long fibers, which could be weaved into highly functional fabrics. Endowing fibers with active devices will potentially establish a new generation of multifunctional fibers, with highly desired electronic properties. For example, flexible and resilient light emitting fibers could be integrated into textiles to enable covert, optical signal transmission from the soldier uniform to the external world, or high bandwidth photodetectors could be embedded into garments to allow high volume information reception via LiFi (WiFi through light).
by Michael Rein.
Ph. D.

This thesis proposes the design for a prototype device that would be used by Air Traffic Controllers in the radar environment to input tactical Air Traffic Control (ATC) instructions to be sent to aircraft via the Mode S digital data link network. The purpose of the device is to reduce the time required to issue instructions and to eliminate misunderstandings that occur when instructions are issued over voice transmission frequencies. The purpose of this thesis was to develop the device in the most ergonomically suited manner based on the air traffic controller's communications requirements. Digital communications systems include both airborne and ground based components. This project was concerned with the development of the ground-based aspect of the communications system.

Wireless underwater acoustic (UWA) communications is a developing field with various applications. The underwater acoustic communication channel is very special and its behavior is environment-dependent. The UWA channel is characterized by low available bandwidth, and severe motion-introduced Doppler effectcompared to wireless radio communication. Recent literature suggests that machine learning (ML)-based channel estimation and equalization offer benefits overtraditional techniques (a decision feedback equalizer), in UWA communications. ML can be advantageous due to the difficultly in designing algorithms for UWA communication, as finding general channel models have proven to be difficult. This study aims to explore if ML-based channel estimation and equalization as a part of a sophisticated physical layer structure can offer improved performance. In the study, supervised ML using a deep neural network and a recurrent neural network will be utilized to improve the bit error rate. A channel simulator with environment-specific input is used to study a wide range of channels. The simulations are utilized to study in which environments ML should be tested. It is shown that in highly time-varying channels, ML outperforms traditional techniques if trained with prior information of the channel. However, utilizing ML without prior information of the channel yielded no improvement of the performance.

Space-based communications systems to be employed by future artificial satellites, or spacecraft during exploration missions, can potentially benefit from software-defined radio adaptation capabilities. Multiple communication requirements could potentially compete for radio resources, whose availability of which may vary during the spacecraft's operational life span. Electronic components are prone to failure, and new instructions will eventually be received through software updates. Consequently, these changes may require a whole new set of near-optimal combination of parameters to be derived on-the-fly without instantaneous human interaction or even without a human in-the-loop. Thus, achieving a sufficiently set of radio parameters can be challenging, especially when the communication channels change dynamically due to orbital dynamics as well as atmospheric and space weather-related impairments. This dissertation presents an analysis and discussion regarding novel algorithms proposed in order to enable a cognition control layer for adaptive communication systems operating in space using an architecture that merges machine learning techniques employing wireless communication principles. The proposed cognitive engine proof-of-concept reasons over time through an efficient accumulated learning process. An implementation of the conceptual design is expected to be delivered to the SDR system located on the International Space Station as part of an experimental program. To support the proposed cognitive engine algorithm development, more realistic satellite-based communications channels are proposed along with rain attenuation synthesizers for LEO orbits, channel state detection algorithms, and multipath coefficients function of the reflector's electrical characteristics. The achieved performance of the proposed solutions are compared with the state-of-the-art, and novel performance benchmarks are provided for future research to reference.

Recent innovations in machine learning have paved the way for new capabilities in the field of radio frequency (RF) communications. Machine learning techniques such as reinforcement learning and deep neural networks (DNN) can be leveraged to improve upon traditional wireless communications methods so that they no longer require expertly-defined features. Simultaneously, cybersecurity and electronic warfare are growing areas of focus and concern in an increasingly technology-driven world. Privacy and confidentiality of communication links are both more important and more difficult than ever in the current high threat environment. RF machine learning (RFML) systems contribute to this threat as they have been shown to be successful in gleaning information from intercepted signals, through the use of learning-enabled eavesdroppers. This thesis focuses on a method of defense against such communications threats termed an adversarial evasion attack in which intelligently crafted perturbations of the RF signal are used to fool a DNN-enabled classifier, therefore securing the communications channel. One often overlooked aspect of evasion attacks is the concept of maintaining intended use. In other words, while an adversarial signal, or more generally an adversarial example, should fool the DNN it is attacking, this should not come at the detriment to it's primary application. In RF communications, this manifests in the idea that the communications link must be successfully maintained with friendly receivers, even when executing an evasion attack against malicious receivers. This is a difficult scenario, made even more so by the nature of channel effects present in over-the-air (OTA) communications, as is assumed in this work. Previous work in this field has introduced a form of evasion attack for RFML systems called a communications aware attack that explicitly addresses the reliable communications aspect of the attack by training a separate DNN to craft adversarial signals; however, this work did not utilize the full RF processing chain and left residual indicators of the attack that could be leveraged for defensive capabilities. First, this thesis focuses on implementing forward error correction (FEC), an aspect present in most communications systems, in the training process of the attack. It is shown that introducing this into the training stage allows the communications aware attack to implicitly use the structure of the coding to create smarter and more efficient adversarial signals. Secondly, this thesis then addresses the fact that in previous work, the resulting adversarial signal exhibiting significant out-of-band frequency content, a limitation that can be used to render the attack ineffective if preprocessing at the attacked DNN is assumed. This thesis presents two novel approaches to solve this problem and eliminate the majority of side content in the attack. By doing so, the communications aware attack is more readily applicable to real-world scenarios.
Master of Science
Deep learning has started infiltrating many aspects of society from the military, to academia, to commercial vendors. Additionally, with the recent deployment of 5G technology, connectivity is more readily accessible than ever and an increasingly large number of systems will communicate with one another across the globe. However, cybersecurity and electronic warfare call into question the very notion of privacy and confidentiality of data and communication streams. Deep learning has further improved these intercepting capabilities. However, these deep learning systems have also been shown to be vulnerable to attack. This thesis exists at the nexus of these two problems, both machine learning and communication security. This work expands upon adversarial evasion attacks meant to help elude signal classification at a deep learning-enabled eavesdropper while still providing reliable communications to a friendly receiver. By doing so, this work both provides a new methodology that can be used to conceal communication information from unwanted parties while also highlighting the glaring vulnerabilities present in machine learning systems.

Un moteur synchrone triphase, est alimente en étoile par quatre transistors, trois d'entre eux assurent la fonction onduleur et un quatrième hache le courant par le neutre. Ce système permet d'autopilotage par mesure des tensions entre phases non alimentées. La simplicité de cette alimentation pose cependant des problèmes liés a la récupération d'énergie lors des commutations; les F. C. E. M. Des phases non alimentées peuvent débiter dans les circuits de récupération. L'objet principal de ce travail est la modélisation du fonctionnement du moteur alimenté avec différents circuits de récupération d'énergie. Une solution est décrite avec quelques applications pour des régions non alimentées par des réseaux classiques

Today, the Internet of Things (IoT) is one of the most important emerging technologies. Applicable to several fields, it has the potential to strongly influence people’s lives. “Things” are mostly embedded machines, and Machine-to-Machine (M2M) communications are used to exchange information. The main aspect of this type of communication is that a “thing” needs a mechanism to uniquely identify other “things” without human intervention. For this purpose, trust plays a key role. Trust can be incorporated in the smartness of “things” by using mobile “agents”. From the study of the IoT ecosystem, a new threat against M2M communications has been identified. This relates to the opportunity for an attacker to employ several forged IoT-embedded machines that can be used to launch attacks. Two “things-aware” detection mechanisms have been proposed and evaluated in this work for incorporation into IoT mobile trust agents. These new mechanisms are based on observing specific thing-related behaviour obtained by using a characterisation algorithm. The first mechanism uses a range of behaviours obtained from real embedded machines, such as threshold values, to detect whether a target machine is forged. This detection mechanism is called machine emulation detection algorithm (MEDA). MEDA takes around 3 minutes to achieve a detection accuracy of 79.21%, with 44.55% of real embedded machines labelled as belonging to forged embedded machines. These results indicated a need to develop a more accurate and faster detection method. Therefore, a second mechanism was created and evaluated. A dataset composed of behaviours from real, virtual and emulated embedded systems that can be part of the IoT was created. This was used for both training and testing classification methods. The results identified Random Forest (RF) as the most efficient method, recognising forged embedded machines in only 5 seconds with a detection rate of around 99.5%. It follows that this solution can be applied in real IoT scenarios with critical conditions. In the final part of this thesis, an attack against these new mechanisms has been proposed. This consists of using a modified kernel of a powerful machine to mimic the behaviour of a real IoT-embedded machine, referred to as a fake timing attack (FTA). Two metrics, mode and median from ping response time, have been found to effectively detect this attack. The final detection method involves combining RF and k-Nearest Neighbour to successfully detect forged embedded machines and FTA in only 40 seconds, with an overall detection performance (ODP) of 99.9% and 93.70% respectively. This method also was evaluated using behaviours from embedded machines that were not present in the training set. The results from that evaluation demonstrate that the proposed solution can detect embedded machines unknown to the method, both real and virtual, with an ODP of 99.96% and 99.92% respectively. In summary, a new algorithm able to detect forged embedded machines easily, quickly and with very high accuracy has been developed. The proposed method addresses the challenge of securing present and future M2M-embedded machines with power-constrained resources and can be applied to real IoT scenarios.

With the increasing number of wireless devices and the phenomenal amount of data that is being generated by them, there is a growing interest in the wireless communications community to complement the traditional model-driven design approaches with data-driven machine learning (ML)-based solutions. However, managing the large-scale multi-dimensional data to maintain the efficiency and scalability of the ML algorithms has obviously been a challenge. Tensors provide a useful framework to represent multi-dimensional data in an integrated manner by preserving relationships in data across different dimensions. This thesis studies two new applications of tensors to ML for wireless communications where the tensor structure of the concerned data is exploited in novel ways. The first contribution of this thesis is a tensor learning-based low-complexity precoder codebook design technique for a full-dimension multiple-input multiple-output (FD-MIMO) system with a uniform planar antenna (UPA) array at the transmitter (Tx) whose channel distribution is available through a dataset. Represented as a tensor, the FD-MIMO channel is further decomposed using a tensor decomposition technique to obtain an optimal precoder which is a function of Kronecker-Product (KP) of two low-dimensional precoders, each corresponding to the horizontal and vertical dimensions of the FD-MIMO channel. From the design perspective, we have made contributions in deriving a criterion for optimal product precoder codebooks using the obtained low-dimensional precoders. We show that this product codebook design problem is an unsupervised clustering problem on a Cartesian Product Grassmann Manifold (CPM), where the optimal cluster centroids form the desired codebook. We further simplify this clustering problem to a $K$-means algorithm on the low-dimensional factor Grassmann manifolds (GMs) of the CPM which correspond to the horizontal and vertical dimensions of the UPA, thus significantly reducing the complexity of precoder codebook construction when compared to the existing codebook learning techniques. The second contribution of this thesis is a tensor-based bandwidth-efficient gradient communication technique for federated learning (FL) with convolutional neural networks (CNNs). Concisely, FL is a decentralized ML approach that allows to jointly train an ML model at the server using the data generated by the distributed users coordinated by a server, by sharing only the local gradients with the server and not the raw data. Here, we focus on efficient compression and reconstruction of convolutional gradients at the users and the server, respectively. To reduce the gradient communication overhead, we compress the sparse gradients at the users to obtain their low-dimensional estimates using compressive sensing (CS)-based technique and transmit to the server for joint training of the CNN. We exploit a natural tensor structure offered by the convolutional gradients to demonstrate the correlation of a gradient element with its neighbors. We propose a novel prior for the convolutional gradients that captures the described spatial consistency along with its sparse nature in an appropriate way. We further propose a novel Bayesian reconstruction algorithm based on the Generalized Approximate Message Passing (GAMP) framework that exploits this prior information about the gradients. Through the numerical simulations, we demonstrate that the developed gradient reconstruction method improves the convergence of the CNN model.
Master of Science
The increase in the number of wireless and mobile devices have led to the generation of massive amounts of multi-modal data at the users in various real-world applications including wireless communications. This has led to an increasing interest in machine learning (ML)-based data-driven techniques for communication system design. The native setting of ML is {em centralized} where all the data is available on a single device. However, the distributed nature of the users and their data has also motivated the development of distributed ML techniques. Since the success of ML techniques is grounded in their data-based nature, there is a need to maintain the efficiency and scalability of the algorithms to manage the large-scale data. Tensors are multi-dimensional arrays that provide an integrated way of representing multi-modal data. Tensor algebra and tensor decompositions have enabled the extension of several classical ML techniques to tensors-based ML techniques in various application domains such as computer vision, data-mining, image processing, and wireless communications. Tensors-based ML techniques have shown to improve the performance of the ML models because of their ability to leverage the underlying structural information in the data. In this thesis, we present two new applications of tensors to ML for wireless applications and show how the tensor structure of the concerned data can be exploited and incorporated in different ways. The first contribution is a tensor learning-based precoder codebook design technique for full-dimension multiple-input multiple-output (FD-MIMO) systems where we develop a scheme for designing low-complexity product precoder codebooks by identifying and leveraging a tensor representation of the FD-MIMO channel. The second contribution is a tensor-based gradient communication scheme for a decentralized ML technique known as federated learning (FL) with convolutional neural networks (CNNs), where we design a novel bandwidth-efficient gradient compression-reconstruction algorithm that leverages a tensor structure of the convolutional gradients. The numerical simulations in both applications demonstrate that exploiting the underlying tensor structure in the data provides significant gains in their respective performance criteria.

碩士
國立成功大學
資訊工程學系
104
In Long Term Evolution-Advanced (LTE-A) networks, the Third Generation Partnership Project (3GPP) proposes machine-type communications (MTC) as a new paradigm where devices transfer data among themselves with limited human interaction. For MTC devices, power saving is an important issue. In LTE-A networks, the standard power saving mechanism, called discontinuous reception (DRX), is designed for normal mobile users, not for MTC devices. In this thesis, we propose the optimistic DRX (ODRX) mechanism to be suitable for MTC devices. ODRX considers radio resource control (RRC) connection release and re-establishment to save more power. We also introduce the optimistic flag to allow longer sleep period. Analytical and simulation models are proposed to investigate the performance of ODRX, which is then compared with the standard DRX and dynamic DRX (DDRX) through simulation experiments. The results show that ODRX outperforms standard DRX and DDRX by gaining significant extra power saving with little extra wake-up latency. We also propose guidelines to configure the ODRX parameters.

碩士
國立暨南國際大學
資訊工程學系
102
With the development of IT technologies, more and more intelligent devices can detect many kinds of environmental conditions, and send collected data or alarm back to server system automatically. Such applications, similar to wireless sensor networks, are developed for recent years. With the development of next-generation LTE wireless mobile network, SA working group also develop a related technologies, namely Machine Type Communication (MTC), in 3GPP. The MTC applications developed by 3GPP SA working group are also operated without interactive with human and can transmit data directly to servers located in LTE core network. However, the MTC devices may massively increase due to more and more fancy applications used. This may result in LTE core network over load as the transmission demand increased. Moreover, RACH congestion becomes more and more serious due to limited preamble slots in LTE system. This thesis attempts to use a hierarchical architecture for grouping MTC devices based on their futures. The ACB or EAB combined with group paging mechanisms are used to reduce the probability of congestion happen. Furthermore, this thesis also develops a Device-to-Device, D2D, communication approach to further reduce the LTE system load.

Machine Type Communication (MTC) applications were designed to monitor and control elements of our surroundings and environment. MTC applications have a different set of requirements compared to the traditional communication devices, with Machine to Machine (M2M) data being mostly short, asynchronous, bursty and sometimes requiring end-to-end delays below 1ms. With the growth of MTC, the new generation of mobile communications has to be able to present different types of services with very different requirements, i.e. the same network has to be capable of "supplying" connection to the user that just wants to download a video or use social media, allowing at the same time MTC that has completely different requirements, without deteriorating both experiences. The challenges associated to the implementation of MTC require disruptive changes at the Physical (PHY) and Medium Access Control (MAC) layers, that lead to a better use of the spectrum available. The orthogonality and synchronization requirements of the PHY layer of current Long Term Evolution Advanced (LTE-A) radio access network (based on glsofdm and Single Carrier Frequency Domain Equalization (SC-FDE)) are obstacles for this new 5th Generation (5G) architecture. Generalized Frequency Division Multiplexing (GFDM) and other modulation techniques were proposed as candidates for the 5G PHY layer, however they also suffer from visible degradation when the transmitter and receiver are not synchronized, leading to a poor performance when collisions occur in an asynchronous MAC layer. This dissertation addresses the requirements of M2M traffic at the MAC layer applying multipacket reception (MPR) techniques to handle the bursty nature of the traffic and synchronization tones and optimized back-off approaches to reduce the delay. It proposes a new MAC protocol and analyses its performance analytically considering an SC-FDE modulation. The models are validated using a system level cross-layer simulator developed in MATLAB, which implements the MAC protocol and applies PHY layer performance models. The results show that the MAC’s latency depends mainly on the number of users and the load of each user, and can be controlled using these two parameters.

TCH codes possess several properties that allow us to use them efficiently in various applications. One of these applications is channel estimation and, in this dissertation, it is studied the performance of TCH codes to estimate the channel in an Orthogonal Frequency Division Multiplexing system, regarding Machine-Type Communications. Bit error rate performance results were obtained by executing simulations that allowed the evaluation of the impact of using two different pilot techniques, such as data multiplexed and implicit pilots, different pilot power levels and different modulations, QPSK and 64-QAM. Pilots based on TCH codes are also compared with other conventional pilots. Results show that TCH codes have a very positive and reliable performance. Joint timing synchronization and channel estimation is also performed using different sparse based approaches, such as Orthogonal Matching Pursuit, L1- regularized and Iterative Reweighted L1. TCH codes are compared against different sequence types, namely Zadoff-Chu sequences and pseudorandom codewords, and variations in the pilot size, the channel length and the observation window size are executed in order to understand their effects. Results ultimately illustrate that TCH codes can be effectively used in joint channel estimation and synchronization, managing to withstand worst simulation conditions better than its counterparts. It is also proven that compressed sensing can successfully be utilized in joint synchronization and channel estimation, an area where its use has not been very explored.
Os códigos TCH possuem várias propriedades que nos permitem usá-los eficientemente em diversas aplicações. Uma delas é a estimação de canal e nesta dissertação é estudado o desempenho dos códigos TCH em estimação de canal num sistema OFDM, tendo em conta as comunicações Machine-Type. Resultados que ilustram a taxa de erro de bit foram obtidos através de simulações que permitem avaliar o impacto de usar diferentes técnicas de pilotos, nomeadamente multiplexados e implícitos, diferentes valores de potência para os pilotos e diferentes modulações, QPSK e 64-QAM. Também é feita a comparação entre os pilotos TCH e pilotos convencionais. Os resultados mostram que os pilotos TCH tem um desempenho muito positivo e confiável, dentro dos parâmetros testados. Também é efetuado o estudo de sincronização e estimação de canal conjunta usando métodos esparsos como o OMP, o L1-regularized e o Iterative Reweighted L1. Os códigos TCH são comparados com outros tipos de sequências, tais como as sequências Zadoff-Chu e os códigos pseudo-aleatórios. São consideradas variações no tamanho dos pilotos, no comprimento do canal e no tamanho da janela de observação para perceber quais são os seus efeitos no desempenho. Os resultados demonstram que os códigos TCH podem ser utilizados com sucesso em estimação de canal e sincronização conjunta e conseguem aguentar condições adversas de simulação melhor que os outros pilotos utilizados. Também é provado que compressed sensing pode ser utilizado com sucesso em sincronização e estimação conjunta, que é uma área onde o seu uso ainda não foi explorado aprofundadamente.

碩士
國立清華大學
資訊工程學系
101
The increasing Machine-Type Communications (MTC) devices will make the network congestion because enormous devices simultaneously perform the random access to obtain the uplink resources. The group paging mechanism may solve this issue by centralized control to disperse the devices. This paper develops an analytic model to study the operation of multi-group random access procedure. Based on the analytic model, we design an approach to implement the group paging mechanism that satisfies the success probability of random access and consumes the less completion time to finish the whole procedure. Furthermore, we compare the performance of the paging and group paging mechanism in our simulation.

碩士
國立交通大學
網路工程研究所
102
Machine-to-Machine (M2M) communication, which is also known as Internet of Things (IoT) or Machine-Type communications as well, is expected to be a major trend in mobile communication such as LTE Advanced Networks. M2M communications enables devices to be controlled via internet. Currently, LTE networks are primarily designed and optimized for human-to-human (H2H) communications. Nevertheless, some designs such as paging for a device in Idle Mode are not suitable for M2M communications. In this thesis, we first discover the paging issue for M2M communications. Furthermore, we investigate that there is trade-off between paging and Tracking Area Update (TAU). In addition, both paging and TAU are affected by the length of DRX cycle. The longer length of the DRX cycle is, the more paging cost will be. However, when the DRX cycle length becomes longer, the TAU cost will be smaller. Therefore, we discuss how DRX cycle length impacts the paging and TAU. Finally, we propose an easy and effective solution to minimize the signal resource consumption by adjusting the length of DRX cycle from the operators’ point of view. In performance evaluations, the optimal DRX cycle length we proposed can save more power and signal resources than the DRX cycle lengths defined in LTE specification (e.g., 32, 64, 128 or 256 millisecond).

碩士
國立成功大學
電腦與通信工程研究所
101
Current-day 3rd Generation Partnership Project (3GPP) networks charging system is not designed for Machine Type Communications (MTC) applications. The existing charging mechanisms usually just consider the traffic characteristics of human-based communication applications (i.e. a call session long, download data volume). However, MTC traffic characteristics are quite different to human-base traffic characteristics. The objective of this thesis is to propose a new credit reservation procedure, called Multiple Event-based Credit Reservation (MECR) to be make use of online charging for MTC services. We develop an analytical model and extensive simulation to in- vestigate the MECR procedure performance. Numerical results show that our MECR procedure can significantly reduce Credit Control Request (CCR) messages and bulk Charging Data Records (CDRs).

碩士
國立中正大學
資訊工程研究所
103
Internet of Things (IoT) has been considered as one of the key technologies for the next decade. One of the key features of IoT is the global connectivity of things, such as smart meters, wireless sensors, etc. LTE-A has been considered as the main means for connecting wireless devices to the Internet. For this reason, Machine Type Communication (MTC) has been defined in the 3GPP LTE-A specification. As the development of IOT , MTC will face many challenge. Firstly, the initial design for LTE-A network is to support Human Type Communication (HTC). However, traffic characteristics of MTC and HTC could be very different, such as data size, transmission cycle and delay tolerance. How to accommodate large amount of MTC without affecting the grade of service of HTC is a big challenge. Secondly, when a device wants to access the LTE-A network, it has to start Random Access(RA) procedure for communicating with the eNB. After the procedure, the device can then synchronize in uplink and downlink and transmit or receive data. As the number of machines is expected to be much larger than HTC devices, an eNB in LTE-A will face too many RA requests from MTC devices. Without proper access control, the success rate of RA requests from HTC devices could degrade quickly. Therefore, the focus of this research is to propose a proper random access procedure such that the access success rate of HTC devices will not be affected by the traffic of MTC or access requests from a large number of MTC devices. In this thesis, we proposed an Adaptive RACH Resource Allocation (ARRA) scheme which integrates several control schemes, include Resource Adjustment scheme, ACB scheme and Priority Device Setting scheme. Our simulation results show that the proposed ARRA scheme is able to achieve high access success rate and low delay of HTC devices while providing different quality of service (QoS) to different type of MTC traffic.