Academic literature on the topic 'Heterogeneous Radio Network'

Today, the radio spectrum is rarely fully utilized. This problem is valid in more domains, e.g., time, frequency and geographical location. To provide an efficient utilization of the radio spectrum, the Cognitive Radio Networks (CRNs) have been advanced. The key idea is to open up the licensed spectrum to unlicensed users, thus allowing them to use the so-called spectrum opportunities as long as they do not harmfully interfere with licensed users. An important focus is laid on the limitation of previously reported research efforts, which is due to the limited consideration of the problem of competition among unlicensed users for spectrum access in heterogeneous CRNs. A software framework is introduced, which is called PRioritized Opportunistic spectrum Access System (PROAS). In PROAS, the heterogeneity aspects of CRNs are specifically expressed in terms of cross-layer design and various wireless technologies. By considering factors like ease of implementation and efficiency of control, PROAS provides priority scheduling based solutions to alleviate the competition problem of unlicensed users in heterogenous CRNs. The advanced solutions include theoretical models, numerical analysis and experimental simulations for performance evaluation. By using PROAS, three particular CRN models are studied, which are based on ad-hoc, mesh-network and cellular-network technologies. The reported results show that PROAS has the ability to bridge the gap between research results and the practical implementation of CRNs.

Cognitive radio networks (CRN) have been tipped as one of the most promising paradigms for next generation wireless communication, due primarily to its huge promise of mitigating the spectrum scarcity challenge. To help achieve this promise, CRN develop mechanisms that permit spectrum spaces to be allocated to, and used by more than one user, either simultaneously or opportunistically, under certain preconditions. However, because of various limitations associated with CRN, spectrum and other resources available for use in CRN are usually very scarce. Developing appropriate models that can efficiently utilise the scarce resources in a manner that is fair, among its numerous and diverse users, is required in order to achieve the utmost for CRN. 'Resource allocation (RA) in CRN' describes how such models can be developed and analysed. In developing appropriate RA models for CRN, factors that can limit the realisation of optimal solutions have to be identified and addressed; otherwise, the promised improvement in spectrum/resource utilisation would be seriously undermined. In this thesis, by a careful examination of relevant literature, the most critical limitations to RA optimisation in CRN are identified and studied, and appropriate solution models that address such limitations are investigated and proffered. One such problem, identified as a potential limitation to achieving optimality in its RA solutions, is the problem of heterogeneity in CRN. Although it is indeed the more realistic consideration, introducing heterogeneity into RA in CRN exacerbates the complex nature of RA problems. In the study, three broad classifications of heterogeneity, applicable to CRN, are identified; heterogeneous networks, channels and users. RA models that incorporate these heterogeneous considerations are then developed and analysed. By studying their structures, the complex RA problems are smartly reformulated as integer linear programming problems and solved using classical optimisation. This smart move makes it possible to achieve optimality in the RA solutions for heterogeneous CRN. Another serious limitation to achieving optimality in RA for CRN is the strictness in the level of permissible interference to the primary users (PUs) due to the activities of the secondary users (SUs). To mitigate this problem, the concept of cooperative diversity is investigated and employed. In the cooperative model, the SUs, by assisting each other in relaying their data, reduce their level of interference to PUs significantly, thus achieving greater results in the RA solutions. Furthermore, an iterative-based heuristic is developed that solves the RA optimisation problem timeously and efficiently, thereby minimising network complexity. Although results obtained from the heuristic are only suboptimal, the gains in terms of reduction in computations and time make the idea worthwhile, especially when considering large networks. The final problem identified and addressed is the limiting effect of long waiting time (delay) on the RA and overall productivity of CRN. To address this problem, queueing theory is investigated and employed. The queueing model developed and analysed helps to improve both the blocking probability as well as the system throughput, thus achieving significant improvement in the RA solutions for CRN. Since RA is an essential pivot on which the CRN's productivity revolves, this thesis, by providing viable solutions to the most debilitating problems in RA for CRN, stands out as an indispensable contribution to helping CRN realise its much-proclaimed promises.<br>Thesis (PhD)--University of Pretoria, 2017.<br>Electrical, Electronic and Computer Engineering<br>PhD<br>Unrestricted

Dans un scénario avec plusieurs réseaux sans fil de différentes technologies, ce travail a comme objectif la conception d'un moteur cognitif capable de reconnaitre l'environnement radio et de sélectionner un réseau avec le but final de maximiser la "qualité d'expérience" (QoE) de l'utilisateur. Un accent particulier est mis sur la simplicité de tous les éléments impliqués, du hardware aux algorithmes, afin de garder la faisabilité pratique de ce dispositif.Deux aspects ont été étudiés. Pour la reconnaissance de l'environnement radio une identification de réseau et une classification automatique sur la base de caractéristiques de la couche MAC a été proposée et testée. En ce qui concerne la sélection du réseau, des "Key Performance Indicators" (KPIs), qui sont des paramètres de la couche application, ont étés pris en compte afin d'obtenir la QoE désirée. Un modèle général pour la sélection du réseau a été proposé et testé avec de différents types de trafic par des simulations et par la réalisation d'un démonstrateur (application pour Android). De plus, comme il y a le problème de quand mesurer pour estimer la performance d'un réseau et quand l'utiliser effectivement pour transmettre et recevoir, le problème du bandit manchot ("Multi-armed bandit", MAB) a été appliqué à ce contexte et un nouveau modèle de MAB a été proposé afin de mieux répondre aux cas réels considérés. L'impact du nouveau modèle, qui introduit la distinction de deux actions différentes, mesurer et utiliser, a été testé par des simulations en utilisant des algorithmes déjà disponibles dans la littérature et deux algorithmes conçus spécifiquement<br>In a scenario where multiple wireless networks of different technologies are available, this work addresses the problem of the design of a cognitive engine, core of a cognitive radio device, able to perform the surrounding radio environment recognition and the network selection with the final goal of maximization of final user Quality of Experience (QoE). Particular focus is put on the requirement of simplicity of all the elements involved, from hardware to algorithms, in order to keep in mind the importance of its practical realizability.Two aspects were investigated. For the surrounding radio environment recognition step, a network identification and automatic classification method based on MAC layer features was proposed and tested. As regards the network selection, Key Performance Indicators (KPIs), i.e. application layer parameters, were considered in order to obtain the desired goal of QoE. A general model for network selection was proposed and tested for different traffic types, both with simulations and a practical realization of a demonstrator (implemented as an application for Android OS). Moreover, as a consequence of the originated problem of when measuring to estimate a network performance and when effectively using the network for data transmission and reception purposes, the multi-armed bandit problem (MAB) was applied to this context and a new MAB model was proposed, in order to better fit the considered real cases scenarios. The impact of the new model, that introduces the distinction of two different actions, to measure and to use, was tested through simulations using algorithms already available in literature and two specifically designed algorithms

The overlapping of the different wireless network technologies creates heterogeneous communication environments. Future mobile communication system considers the technological and operational services of heterogeneous communication environments. Based on its packet switched core, the access to future mobile communication system will not be restricted to the mobile cellular networks but may be via other wireless or even wired technologies. Such universal access can enable service convergence, joint resource management, and adaptive quality of service. However, in order to realise the universal access, there are still many pending challenges to solve. One of them is the selection of the most appropriate radio access network. Previous work on the network selection has concentrated on serving the requesting user, but the existing users and the consumption of the network resources were not the main focus. Such network selection decision might only be able to benefit a limited number of users while the satisfaction levels of some users are compromised, and the network resources might be consumed in an ineffective way. Solutions are needed to handle the radio access network selection in a manner that both of the satisfaction levels of all users and the network resource consumption are considered. This thesis proposes an intelligent radio access network selection and optimisation system. The work in this thesis includes the proposal of an architecture for the radio access network selection and optimisation system and the creation of novel adaptive algorithms that are employed by the network selection system. The proposed algorithms solve the limitations of previous work and adaptively optimise network resource consumption and implement different policies to cope with different scenarios, network conditions, and aims of operators. Furthermore, this thesis also presents novel network resource availability evaluation models. The proposed models study the physical principles of the considered radio access network and avoid employing assumptions which are too stringent abstractions of real network scenarios. They enable the implementation of call level simulations for the comparison and evaluation of the performance of the network selection and optimisation algorithms.

ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV<br>Among the rich infrastructure of a Telemetry/Ground Station Center dwells the subset dedicated to radio communications. Radios are mainly used to communicate with the aircraft under test in order to give guidance and feedback from ground specialists. Sometimes, however, radios themselves become the subject of the test, requiring a full set of them with all their features and capabilities (Military Modes, HF ALE, SELCAL, etc). Remote control (and audio routing) of these radios is a critical as infrastructures scale over tens of radios, distributed amid different test centers separated by hundreds of kilometers. Addition of a remote touch user interface, MIL COMSEC and TRANSEC modes, automatic audio routing, together with a maintenance free requirement, makes the whole issue far more difficult to manage. Airbus Defense & Space has developed a Smart Radio Control System allowing to profit from those advantages and more benefits: *Intuitive Touch UI *Automatic Audio Routing *Distributed infrastructure (network based) *Autonomous and service free (no one, other than FTC needed to operate it) *Heterogeneous (any radio can be controlled by creating a plug & play library) *Special Modes support (COMSEC, TRANSEC, HF ALE, and SELCAL) Future additions will include, amongst others, VoIP integration and tablet use.

En vue d’aider les opérateurs mobiles avec la gestion de leurs réseaux d’accès radio, trois modèles sont proposés. Le premier modèle est une approche supervisée pour une prévention des anomalies. Son objectif est de détecter les dysfonctionnements futurs d’un ensemble de cellules en observant les indicateurs clés de performance considérés comme des données fonctionnelles. Par conséquent, en alertant les ingénieurs et les réseaux auto-organisés, les opérateurs mobiles peuvent être sauvés d’une dégradation de performance de leurs réseaux. Le modèle a prouvé son efficacité avec une application sur données réelles qui vise à détecter la dégradation de capacité, les problèmes d’accessibilités et les coupures d’appel dans des réseaux LTE.A cause de la diversité des technologies mobiles, le volume de données qui doivent être quotidiennement observées par les opérateurs mobiles devient énorme. Ce grand volume a devenu un obstacle pour la gestion des réseaux mobiles. Le second modèle vise à fournir une représentation simplifiée des indicateurs clés de performance pour une analyse plus facile. Du coup, un modèle de classification croisée pour données fonctionnelles est proposé. L’algorithme est basé sur un modèle de blocs latents dont chaque courbe est identifiée par ses composantes principales fonctionnelles. Ces dernières sont modélisées par une distribution Gaussienne dont les paramètres sont spécifiques à chaque bloc. Les paramètres sont estimés par un algorithme EM stochastique avec un échantillonnage de Gibbs. Ce modèle est le premier modèle de classification croisée pour données fonctionnelles et il a prouvé son efficacité sur des données simulées et aussi sur une application réelle qui vise à aider dans l’optimisation de la topologie des réseaux mobiles 4G.Le troisième modèle vise à résumer l’information issue des indicateurs clés de performance et aussi des alarmes réseaux. Un modèle de classification croisée des données mixtes : fonctionnelles et binaires est alors proposé. L’approche est basé sur un modèle de blocs latents et trois algorithmes sont comparés pour son inférence : EM stochastique avec un échantillonneur de Gibbs, EM de classification et EM variationnelle. Le modèle proposé est le premier algorithme de classification croisée pour données fonctionnelles et binaires. Il a prouvé son efficacité sur des données simulées et sur des données réelles extraites à partir de plusieurs réseaux mobiles 4G<br>In order to help the mobile operators with the management of their radio access networks, three models are proposed. The first model is a supervised approach for mobile anomalies prevention. Its objective is to detect future malfunctions of a set of cells, by only observing key performance indicators (KPIs) that are considered as functional data. Thus, by alerting the engineers as well as self-organizing networks, mobile operators can be saved from a certain performance degradation. The model has proven its efficiency with an application on real data that aims to detect capacity degradation, accessibility and call drops anomalies for LTE networks.Due to the diversity of mobile network technologies, the volume of data that has to be observed by mobile operators in a daily basis became enormous. This huge volume became an obstacle to mobile networks management. The second model aims to provide a simplified representation of KPIs for an easier analysis. Hence, a model-based co-clustering algorithm for functional data is proposed. The algorithm relies on the latent block model in which each curve is identified by its functional principal components that are modeled by a multivariate Gaussian distribution whose parameters are block-specific. These latter are estimated by a stochastic EM algorithm embedding a Gibbs sampling. This model is the first co-clustering approach for functional data and it has proven its efficiency on simulated data and on a real data application that helps to optimize the topology of 4G mobile networks.The third model aims to resume the information of data issued from KPIs and also alarms. A model-based co-clustering algorithm for mixed data, functional and binary, is therefore proposed. The approach relies on the latent block model, and three algorithms are compared for its inference: stochastic EM within Gibbs sampling, classification EM and variational EM. The proposed model is the first co-clustering algorithm for mixed data that deals with functional and binary features. It has proven its efficiency on simulated data and on real data extracted from live 4G mobile networks