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Добірка наукової літератури з теми "Communications de type machine"

Автор: Grafiati

Опубліковано: 15 березня 2025

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Статті в журналах з теми "Communications de type machine"

Dutkiewicz, Eryk, Xavier Costa-Perez, Istvan Z. Kovacs, and Markus Mueck. "Massive Machine-Type Communications." IEEE Network 31, no. 6 (November 2017): 6–7. http://dx.doi.org/10.1109/mnet.2017.8120237.

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Osseiran, Afif, JaeSeung Song, Jose F. Monserrat, and Roland Hechwartner. "IoT and Machine Type Communications." IEEE Communications Standards Magazine 4, no. 2 (June 2020): 40. http://dx.doi.org/10.1109/mcomstd.2020.9139044.

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Zheng, Tongyi, Lei Ning, Qingsong Ye, and Fan Jin. "An XGB-Based Reliable Transmission Method in the mMTC Scenarios." Security and Communication Networks 2021 (December 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/9929051.

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Massive machine-type communications (mMTCs) for Internet of things are being developed thanks to the fifth-generation (5G) wireless systems. Narrowband Internet of things (NB-IoT) is an important communication technology for machine-type communications. It supports many different protocols for communication. The reliability and performance of application layer communication protocols are greatly affected by the retransmission time-out (RTO) algorithm. In order to improve the reliability and performance of machine-type communications, this study proposes a novel RTO algorithm UDP-XGB based on the user datagram protocol (UDP) and NB-IoT. It combines traditional algorithms with machine learning. The simulation results show that real round-trip time (RTT) is close to the RTO, which is obtained by this algorithm, and the reliability and performance of machine-type communications have improved.

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Dawy, Zaher, Walid Saad, Arunabha Ghosh, Jeffrey G. Andrews, and Elias Yaacoub. "Toward Massive Machine Type Cellular Communications." IEEE Wireless Communications 24, no. 1 (February 2017): 120–28. http://dx.doi.org/10.1109/mwc.2016.1500284wc.

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Jain, Puneet, Peter Hedman, and Haris Zisimopoulos. "Machine type communications in 3GPP systems." IEEE Communications Magazine 50, no. 11 (November 2012): 28–35. http://dx.doi.org/10.1109/mcom.2012.6353679.

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Choi, Dae-Sung, and Hyoung-Kee Choi. "An Group-based Security Protocol for Machine Type Communications in LTE-Advanced." Journal of the Korea Institute of Information Security and Cryptology 23, no. 5 (October 31, 2013): 885–96. http://dx.doi.org/10.13089/jkiisc.2013.23.5.885.

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Lai, Chengzhe, Rongxing Lu, Hui Li, Dong Zheng, and Xuemin Sherman Shen. "Secure machine-type communications in LTE networks." Wireless Communications and Mobile Computing 16, no. 12 (July 17, 2015): 1495–509. http://dx.doi.org/10.1002/wcm.2612.

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Yahiya, Tara I. "Towards Society Revolution." UKH Journal of Science and Engineering 2, no. 2 (December 26, 2018): 37–38. http://dx.doi.org/10.25079/ukhjse.v2n2y2018.pp37-38.

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Анотація:

It is expected that the 5G will change the landscape of the communication paradigm as it will offer huge number of device connections, high data rate, evolutionary channel modulation, etc. The 5G predicts to have billions of devices connected through its new scenarios involving Internet of Things (IoT), Machine Type Communications (MTC), Machine- to- Machine Communications (M2M) via the use of different types of devices including but not restricted to smartphones based IP packet.

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Iivari, Antti, Teemu Väisänen, Mahdi B. Alaya, Tero Riipinen, and Thierry Monteil. "Harnessing XMPP for Machine-to-Machine Communications & Pervasive Applications." Journal of Communications Software and Systems 10, no. 3 (March 16, 2017): 163. http://dx.doi.org/10.24138/jcomss.v10i3.121.

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Анотація:

An ever increasing number of interconnected embedded devices, or Machine-to-Machine (M2M) systems, are changing the way we live, work and play. M2M systems as a whole are typically characterized by the diversity in both the type of device and type of network access technology employed, and such systems are often still today task-specific and built for just one specific application. Smart lighting, remote monitoring and control of all kinds of consumer devices and industrial equipment, safety and security monitoring devices and smart health and fitness products, exemplify this revolution of intercommunicating machines. However, the differences in communication technologies and data formats among such devices and systems are leading to a huge complexity explosion problem and a strongly fragmented market, with no true interoperability. Due to these problems, the full potential of M2M technology has yet to be fulfilled. In this paper, we examine the suitability of the Extensible Messaging and Presence Protocol (XMPP) and experiment with its potential to rise to the challenge of machine-to-machine communications and meet the needs of modern pervasive applications. Experimental implementations and some proof-of-concept solutions are also presented.

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Liu, Liang, Erik G. Larsson, Petar Popovski, Giuseppe Caire, Xiaoming Chen, and Saeed R. Khosravirad. "Guest Editorial: Massive Machine-Type Communications for IoT." IEEE Wireless Communications 28, no. 4 (August 2021): 56. http://dx.doi.org/10.1109/mwc.2021.9535445.

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Дисертації з теми "Communications de type machine"

Abbas, Rana. "Multiple Access for Massive Machine Type Communications." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18094.

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Анотація:

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

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Becirovic, Ema. "On Massive MIMO for Massive Machine-Type Communications." Licentiate thesis, Linköpings universitet, Kommunikationssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162586.

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Анотація:

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.

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Wang, Shendi. "Efficient transmission design for machine type communications in future wireless communication systems." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/23647.

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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.

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Zhou, Kaijie. "Technique d'accès pour la communication machine-à-machine dans LTE/LTE-A." Thesis, Paris, ENST, 2013. http://www.theses.fr/2013ENST0076/document.

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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

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Abrignani, Melchiorre Danilo <1986&gt. "Heterogeneous Networks for the IoT and Machine Type Communications." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amsdottorato.unibo.it/7539/1/Thesis.pdf.

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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.

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De, Boni Rovella Gastón. "Solutions de décodage canal basées sur l'apprentissage automatique pour les communications de type machine-à-machine." Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0065.

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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

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Qasmi, F. (Fahad). "On the performance of machine-type communications networks under Markovian arrival sources." Master's thesis, University of Oulu, 2018. http://jultika.oulu.fi/Record/nbnfioulu-201806052451.

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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.

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Zhou, Kaijie. "Technique d'accès pour la communication machine-à-machine dans LTE/LTE-A." Electronic Thesis or Diss., Paris, ENST, 2013. http://www.theses.fr/2013ENST0076.

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Azari, Amin. "Energy Efficient Machine-Type Communications over Cellular Networks : A Battery Lifetime-Aware Cellular Network Design Framework." Licentiate thesis, KTH, Kommunikationssystem, CoS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-194416.

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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

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Книги з теми "Communications de type machine"

Wang, Fanggang, and Guoyu Ma. Massive Machine Type Communications. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13574-4.

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Wang, Michael Mao, and Jingjing Zhang. Machine-Type Communication for Maritime Internet-of-Things. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77908-5.

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Jiang, Xiaolin, ed. Machine Learning and Intelligent Communications. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04409-0.

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Meng, Limin, and Yan Zhang, eds. Machine Learning and Intelligent Communications. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00557-3.

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Gu, Xuemai, Gongliang Liu, and Bo Li, eds. Machine Learning and Intelligent Communications. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73447-7.

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Gu, Xuemai, Gongliang Liu, and Bo Li, eds. Machine Learning and Intelligent Communications. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73564-1.

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Xin-lin, Huang, ed. Machine Learning and Intelligent Communications. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52730-7.

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Zhai, Xiangping Bryce, Bing Chen, and Kun Zhu, eds. Machine Learning and Intelligent Communications. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32388-2.

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Guan, Mingxiang, and Zhenyu Na, eds. Machine Learning and Intelligent Communications. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66785-6.

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Lam, Sinh Cong, Chiranji Lal Chowdhary, Tushar Hrishikesh Jaware, and Subrata Chowdhury. Machine Learning for Mobile Communications. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003306290.

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Частини книг з теми "Communications de type machine"

Braud, Tristan, Dimitris Chatzopoulos, and Pan Hui. "Machine Type Communications in 6G." In Computer Communications and Networks, 207–31. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72777-2_11.

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Lee, Chia-Peng, and Phone Lin. "Machine-Type Communication." In Encyclopedia of Wireless Networks, 754–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_190.

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Lee, Chia-Peng, and Phone Lin. "Machine-Type Communication." In Encyclopedia of Wireless Networks, 1–5. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-32903-1_190-1.

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Li, Zexian, and Rainer Liebhart. "Critical Machine Type Communication." In 5G for the Connected World, 337–75. Chichester, UK: John Wiley & Sons, Ltd, 2019. http://dx.doi.org/10.1002/9781119247111.ch8.

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Jacobsen, Thomas, István Z. Kovács, Mads Lauridsen, Li Hongchao, Preben Mogensen, and Tatiana Madsen. "Generic Energy Evaluation Methodology for Machine Type Communication." In Multiple Access Communications, 72–85. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51376-8_6.

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Wang, Fanggang, and Guoyu Ma. "Introduction on Massive Machine-Type Communications (mMTC)." In SpringerBriefs in Electrical and Computer Engineering, 1–3. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13574-4_1.

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Craciunescu, Razvan, Simona Halunga, and Octavian Fratu. "Enhanced Massive Machine Type Communications for 6G Era." In 6G Enabling Technologies, 95–116. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003360889-5.

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Zheng, Shilei, Fanggang Wang, and Xia Chen. "Preamble Design for Collision Detection and Channel Estimation in Machine-Type Communication." In Communications and Networking, 292–301. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66628-0_28.

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Ciou, Jian-Wei, Shin-Ming Cheng, and Yin-Hong Hsu. "Retransmission-Based Access Class Barring for Machine Type Communications." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 145–54. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00410-1_18.

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Le-Ngoc, Tho, and Atoosa Dalili Shoaei. "Multiple Access Schemes for Machine-Type Communications: A Literature Review." In Learning-Based Reconfigurable Multiple Access Schemes for Virtualized MTC Networks, 13–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60382-3_2.

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Тези доповідей конференцій з теми "Communications de type machine"

Amokrane, Ahmed, Adlen Ksentini, Yassine Hadjadj-Aoul, and Tarik Taleb. "Congestion control for machine type communications." In ICC 2012 - 2012 IEEE International Conference on Communications. IEEE, 2012. http://dx.doi.org/10.1109/icc.2012.6364152.

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Chang, Hui-Ling, Shang-Lin Lu, Tsung-Hui Chuang, Chia-Ying Lin, Meng-Hsun Tsai, and Sok-Ian Sou. "Optimistic DRX for machine-type communications." In ICC 2016 - 2016 IEEE International Conference on Communications. IEEE, 2016. http://dx.doi.org/10.1109/icc.2016.7510813.

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Cheng, Ray-Guang, Chia-Hung Wei, Shiao-Li Tsao, and Fang-Ching Ren. "RACH Collision Probability for Machine-Type Communications." In 2012 IEEE Vehicular Technology Conference (VTC 2012-Spring). IEEE, 2012. http://dx.doi.org/10.1109/vetecs.2012.6240129.

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Aissa, Mohamed, and Abdelfettah Belghith. "Overview of machine-type communications traffic patterns." In 2015 2nd World Symposium on Web Applications and Networking (WSWAN). IEEE, 2015. http://dx.doi.org/10.1109/wswan.2015.7210318.

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Sarigiannidis, Panagiotis, Theodoros Zygiridis, Antonios Sarigiannidis, Thomas D. Lagkas, Mohammad Obaidat, and Nikolaos Kantartzis. "Connectivity and coverage in machine-type communications." In ICC 2017 - 2017 IEEE International Conference on Communications. IEEE, 2017. http://dx.doi.org/10.1109/icc.2017.7996897.

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Ratasuk, Rapeepat, Nitin Mangalvedhe, and Amitava Ghosh. "Extending LTE coverage for machine type communications." In 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT). IEEE, 2015. http://dx.doi.org/10.1109/wf-iot.2015.7389051.

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Misic, Vojislav B., Jelena Misic, and Dragan Nerandzic. "Extending LTE to support machine-type communications." In ICC 2012 - 2012 IEEE International Conference on Communications. IEEE, 2012. http://dx.doi.org/10.1109/icc.2012.6364741.

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Arouk, Osama, Adlen Ksentini, and Tarik Taleb. "Group paging optimization for machine-type-communications." In 2015 IEEE International Conference on Signal Processing for Communications (ICC). IEEE, 2015. http://dx.doi.org/10.1109/icc.2015.7249360.

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Grankin, Maxim, and Marcin Rybakowski. "Model for analysis of Machine-Type Communications." In 2014 XIV International Symposium on Problems of Redundancy in Information and Control Systems. IEEE, 2014. http://dx.doi.org/10.1109/red.2014.7016704.

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Cheng, Ray-Guang. "Overload Control for Massive Machine Type Communications." In The 2nd World Congress on Electrical Engineering and Computer Systems and Science. Avestia Publishing, 2016. http://dx.doi.org/10.11159/eee16.2.

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Звіти організацій з теми "Communications de type machine"

Poussart, Denis. Le métavers : autopsie d’un fantasme Réflexion sur les limites techniques d’une réalité synthétisée, virtualisée et socialisée. Observatoire international sur les impacts sociétaux de l’intelligence artificielle et du numérique, February 2024. http://dx.doi.org/10.61737/sgkp7833.

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Lorsque Neal Stephenson a introduit le terme « métavers » dans son roman de science-fiction Snow Crash, en 1992, il était loin de se douter que le mot allait susciter autant de discussions. La notion d’une réalité d’un type nouveau, qui serait synthétisée, puis virtualisée et librement socialisée, est fascinante par ce qu’elle exigerait aux plans scientifique et technique. Fascinante surtout par ses retombées éventuelles aux niveaux culturel et social, y compris de nature éthique (qui ne sont pas abordées ici). Ce texte rappelle brièvement l’origine du concept avant de se consacrer à ses requis et défis techniques, abordés en l’examinant comme un système avancé d’information et communication. Le métavers revêt une complexité inédite alors que les capacités cognitives de l’humain et de la machine sont appelées à se fusionner avec synergie. L’analyse – qui demeure succincte compte tenu du format d’un article court – permettra de comprendre comment et pourquoi le métavers, dans la mouture originale proposée par Stephenson, demeure une utopie. Mais aussi comment l’élimination de certains requis peut permettre d’en retenir une saveur intéressante, laquelle apparait déjà dans une multitude d’applications.

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Shull, D. 9977 TYPE B PACKAGING INTERNAL DATA COLLECTION FEASIBILITY TESTING - MAGNETIC FIELD COMMUNICATIONS. Office of Scientific and Technical Information (OSTI), June 2012. http://dx.doi.org/10.2172/1044238.

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Hedyehzadeh, Mohammadreza, Shadi Yoosefian, Dezfuli Nezhad, and Naser Safdarian. Evaluation of Conventional Machine Learning Methods for Brain Tumour Type Classification. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, June 2020. http://dx.doi.org/10.7546/crabs.2020.06.14.

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Clunie, D., and E. Cordonnier. Digital Imaging and Communications in Medicine (DICOM) - Application/dicom MIME Sub-type Registration. RFC Editor, February 2002. http://dx.doi.org/10.17487/rfc3240.

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Christie, Lorna. Interpretable machine learning. Parliamentary Office of Science and Technology, October 2020. http://dx.doi.org/10.58248/pn633.

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Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that in some cases it may not be possible to explain completely how its outputs have been produced. This POSTnote gives an overview of ML and its role in decision-making. It examines the challenges of understanding how a complex ML system has reached its output, and some of the technical approaches to making ML easier to interpret. It also gives a brief overview of some of the proposed tools for making ML systems more accountable.

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SECRETARY OF THE AIR FORCE WASHINGTON DC. Communications and Information: Operational Instruction for the Secure Telephone Unit (STU-III) Type 1. Fort Belvoir, VA: Defense Technical Information Center, February 1998. http://dx.doi.org/10.21236/ada404995.

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Guerber, Mark R. The Modified Mission Type Order: A Vehicle for Strategic Communications Within the US Army. Fort Belvoir, VA: Defense Technical Information Center, April 2009. http://dx.doi.org/10.21236/ada539849.

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Adam, Gaelen P., Melinda Davies, Jerusha George, Eduardo Caputo, Ja Mai Htun, Erin L. Coppola, Haley Holmer, et al. Machine Learning Tools To (Semi-) Automate Evidence Synthesis. Agency for Healthcare Research and Quality (AHRQ), January 2025. https://doi.org/10.23970/ahrqepcwhitepapermachine.

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Introduction. Tools that leverage machine learning, a subset of artificial intelligence, are becoming increasingly important for conducting evidence synthesis as the volume and complexity of primary literature expands exponentially. In response, we have created a living rapid review and evidence map to understand existing research and identify available tools. Methods. We searched PubMed, Embase, and the ACM Digital Library from January 1, 2021, to April 3, 2024, for comparative studies, and identified older studies using the reference lists of existing evidence synthesis products (ESPs). We plan to update searches every 6 months. We included evaluations of machine learning or artificial intelligence tools to automate or semi-automate any stage of systematic review production. Two reviewers conducted title and abstract screening independently, with disagreements resolved through discussion or adjudication by a third reviewer. A single reviewer performed full-text screening and data extraction. We did not assess the quality of individual studies or the strength of evidence across studies. Extracted data included key characteristics of the tools (e.g., type of automation method, systematic review tasks automated), evaluation methods, and performance results (e.g., recall, measures of workload, accuracy, and the authors’ conclusions). The protocol was prospectively registered on the AHRQ website (https://effectivehealthcare.ahrq.gov/products/tools/protocol). Results. We included 56 studies, which evaluated the performance of tools primarily relative to standard human processes across various systematic review tasks. For search-related tools (7 studies), recall (the percent of relevant citations correctly identified) ranged from 0 to 97 percent (median 26%) compared to human-developed search strategies, while precision (the percent of identified citations that are relevant) ranged from 0 to 13.4 percent (median 4.3%). Tools designed to identify randomized controlled trials (RCTs) (6 studies) had recalls between 96 and 100 percent (median 98.5%), with precision ranging from 8 to 92 percent (median 44%), compared to either manual identification or PubMed’s “publication type” tags. Abstract screening tools (22 studies) had a median recall of 93 percent (range 1–100%) with human screening as the standard, while median burden reduction was 50 percent (range 1–93%), and median work saved over sampling to achieve 95 percent recall (WSS@95) was 54 percent (range 33–90%). Data extraction tools (9 studies) showed highly variable performance, with the percentage of data correctly extracted compared to manual extraction ranging from 0 to 99 percent (median 10%). Finally, tools used for risk of bias assessment (7 studies) showed modest agreement with human reviewers, with Cohen’s weighted kappa ranging from 0.11 to 0.48 (median 0.16). Discussion. Certain tools, particularly those for automatically identifying RCTs and prioritizing relevant abstracts in screening, show a high level of recall and precision, suggesting they are nearing widespread use with human oversight. However, other tools, such as those for searching and data extraction, show highly variable performance and are not yet reliable enough for semi-automation. This work revealed the importance of developing standardized evaluation frameworks for assessing the performance of machine learning and artificial intelligence tools in systematic review tasks. We did not assess the risk of bias or methodological quality of the included studies, which may affect the reliability and comparability of the reported performance outcomes. Additionally, the tools were evaluated in a variety of settings, tasks, and review questions, which introduces heterogeneity that makes direct comparisons across tools challenging. Lastly, the rapidly evolving nature of machine learning technologies means that our findings may quickly become outdated. Therefore, we have planned ongoing updates every 6 months.

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li, yihan, nan jin, qiuzhong zhan, aochaun sun, fenfen yin, and zhuangzhaung li. Machine Learning-Based Risk Predictive Models for Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2024. http://dx.doi.org/10.37766/inplasy2024.9.0038.

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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, July 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 samples of model input. The ﬁrst dataset consisted of transmission loss (TL) ﬁelds for single realizations of turbulence. The second dataset consisted of average TL ﬁelds for 64 realizations of turbulence. Three machine-learning algorithms were applied to each dataset, namely, ensemble decision trees, neural networks, and cluster-weighted models. Observational data come from a long-range (out to 8 km) sound propagation experiment. In comparison to the experimental observations, regression predictions have 5–7 dB in median absolute error. Surrogate data quality depends on an accurate characterization of refractive and scattering conditions. Predictions obtained through a single realization of turbulence agree better with the experimental observations.

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