Power Control with Random Delays: Robust Feedback Averaging

International audienceDistributed power control schemes in wireless networks have been well-examined, but standard methods rarely consider the effect of potentially random delays, which occur in almost every real-world network. We present Robust Feedback Averaging, a novel power control algorithm that is capable of operating in delay-ridden and noisy environments. We prove optimal convergence of this algorithm in the presence of random, time-varying delays, and present numerical simulations that indicate that Robust Feedback Averaging outperforms the ubiquitous Foschini-Miljanic algorithm in several regimes

OPTIMIZING RADIO RESOURCE MANAGEMENT IN VERY BAD CHANNEL CONDITIONS

Radio resource management is one of the most important parts of modern multi-user wireless communication systems. The main reason for this importance comes from the fact that the radio resources, such as bandwidth and power, are scarce. For instance, UMTS systems use 5MHz bandwidth for voice as well as data services. The optimum usage of the radio resource guarantees the highest efficient utilization of wireless networks. To optimize the radio resources, the transmitters need to estimate the channel conditions. This channel estimation is done by using pilot signal from the receiver. There are usually small delays between the measurements and the radio resource allocation. When the channel is highly correlated, this delay will not affect the performance, because the channel will not be significantly changed between the time of measurement and the time of transmission. However, if the mobile speed is high or the channel is very high dynamic, the correlation becomes very low. This is due to the timevarying nature of the channel. We call channels with very low correlation in time as bad condition channels. In this thesis we discuss this extremely important topic. The tools for analyzing bad condition channels are also proposed and discussed. Two power control algorithms to mitigate the low correlation of channels have been proposed. Our algorithms are validated through several simulations.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Distributed Power Control Techniques Based on Game Theory for Wideband Wireless Networks

This thesis describes a theoretical framework for the design and the analysis of distributed (decentralized) power control algorithms for high-throughput wireless networks using ultrawideband (UWB) technologies. The tools of game theory are shown to be expedient for deriving scalable, energy-efficient, distributed power control schemes to be applied to a population of battery-operated user terminals in a rich multipath environment. In particular, the power control issue is modeled as a noncooperative game in which each user chooses its transmit power so as to maximize its own utility, which is defined as the ratio of throughput to transmit power. Although distributed (noncooperative) control is known to be suboptimal with respect to the optimal centralized (cooperative) solution, it is shown via large-system analysis that the game-theoretic distributed algorithm based on Nash equilibrium exhibits negligible performance degradation with respect to the centralized socially optimal configuration. The framework described here is general enough to also encompass the analysis of code division multiple access (CDMA) systems and to show that UWB slightly outperforms CDMA in terms of achieved utility at the Nash equilibrium

Resource allocation methods for quality-of-service provisioning in heterogeneous wireless networks

The increased use of mobile wireless devices that we have recently been witnessing, such as smartphones, tablets, e-readers, and WiFi enabled devices in general, is driving an unprecedented increase in the amount of data traffic. This fast market adoption of the wireless technology along with the tremendous success of multimedia applications brought about higher capacity, connectivity, and Quality of Service (QoS) requirements that can no longer be met with traditional networking paradigms. As a result, heterogeneous wireless networks have recently emerged as a potential solution for meeting such new requirements. Hybrid wireless mesh networks and femtocell/macrocell networks are examples of these newly emerging heterogeneous networks. While mesh networks are viewed as the backbone/core network, femtocell and cellular networks are viewed as the access networks linking end-users with the backbone networks. In this dissertation, we address the problem of resource allocation in heterogeneous networks. We investigate both types of networks/architectures: next-generation wireless backbone networks or simply wireless mesh networks (WMNs) and next-generation wireless access networks or simply femtocell (FC) networks. WMNs were first introduced to foster the availability of Internet services anywhere and at anytime. However, capacity limitation has been a fundamental challenge to WMNs, mainly due to the interference arising from the wireless nature of the environment as well as to the scarcity of the radio/channel resources. To overcome this problem, we propose in this dissertation an efficient scheduling scheme that reduces interference among active links via wise time and frequency assignments to the wireless mesh routers. The developed scheme is traffic aware in that it maximizes the capacity of wireless links but while accounting for their traffic loads, thus meeting the end-to-end bandwidth requirements as much as possible. In the second part of this thesis, we focus on developing power allocation techniques for FC networks. FCs have recently emerged as a key networking solution that has great potential for improving the capacity and coverage of traditional macrocell (MC) networks through high-speed indoor coverage. Their deployment, however, has given rise to new interference challenges which are mainly due to the FCs' autonomous nature and to the unreliability of the wireless medium. Driven by this fact, in the second part of this thesis, we first design a fully-distributed estimation-based power allocation scheme that aims at fairly maximizing the capacity of FC networks. Second, we propose a novel distributed stochastic power control scheme that aims at maintaining the users' minimum= required QoS. Finally, we provide cross-layer performance analysis of two-tier FC networks, in which we characterize the uplink interference and study its impact on the data-link layer QoS performance in FC networks

Théorie des jeux et apprentissage pour les réseaux sans fil distribués

Dans cette thèse, nous étudions des réseaux sans fil dans lesquels les terminaux mobiles sont autonomes dans le choix de leurs configurations de communication. Cette autonomie de décision peut notamment concerner le choix de la technologie d'accès au réseau, le choix du point d'accès, la modulation du signal, les bandes de fréquences occupées, la puissance du signal émis, etc. Typiquement, ces choix de configuration sont réalisés dans le but de maximiser des métriques de performances propres à chaque terminal. Sous l'hypothèse que les terminaux prennent leurs décisions de manière rationnelle afin de maximiser leurs performances, la théorie des jeux s'applique naturellement pour modéliser les interactions entre les décisions des différents terminaux. Plus précisément, l'objectif principal de cette thèse est d'étudier des stratégies d'équilibre de contrôle de puissance d'émission afin de satisfaire des considérations d'efficacité énergétique. Le cadre des jeux stochastiques est particulièrement adapté à ce problème et nous permet notamment de caractériser la région de performance atteignable pour toutes les stratégies de contrôle de puissance qui mènent à un état d'équilibre. Lorsque le nombre de terminaux en jeu est grand, nous faisons appel à la théorie des jeux à champ moyen pour simplifier l'étude du système. Cette théorie nous permet d'étudier non pas les interactions individuelles entre les terminaux, mais l'interaction de chaque terminal avec un champ moyen qui représente l'état global des autres terminaux. Des stratégies de contrôle de puissance optimales du jeu à champ moyen sont étudiées. Une autre partie de la thèse a été consacrée à des problématiques d'apprentissage de points d'équilibre dans les réseaux distribués. En particulier, après avoir caractérisé les positions d'équilibre d'un jeu de positionnement de points d'accès, nous montrons comment des dynamiques de meilleures réponses et d'apprentissage permettent de converger vers un équilibre. Enfin, pour un jeu de contrôle de puissance, la convergence des dynamiques de meilleures réponses vers des points d'équilibre a été étudiée. Il est notamment proposé un algorithme d'adaptation de puissance convergeant vers un équilibre avec une faible connaissance du réseau.In this thesis, we study wireless networks in which mobile terminals are free to choose their communication configuration. Theses configuration choices include access wireless technology, access point association, coding-modulation scheme, occupied bandwidth, power allocation, etc. Typically, these configuration choices are made to maximize some performance metrics associated to every terminals. Under the assumption that mobile terminals take their decisions in a rational manner, game theory can be applied to model the interactions between the terminals. Precisely, the main objective of this thesis is to study energy-efficient power control policies from which no terminal has an interest to deviate. The framework of stochastic games is particularly suited to this problem and allows to characterize the achievable utility region for equilibrium power control strategies. When the number of terminals in the network is large, we invoke mean field game theory to simplify the study of the system. Indeed, in a mean field game, the interactions between a player and all the other players are not considered individually. Instead, one only studies the interactions between each player and a mean field, which is the distribution of the states of all the other players. Optimal power control strategies from the mean field formulation are studied. Another part of this thesis has been focused on learning equilibria in distributed games. In particular, we show how best response dynamics and learning algorithms can converge to an equilibrium in a base station location game. For another scenario, namely a power control problem, we study the convergence of the best response dynamics. In this case, we propose a power control behavioral rule that converges to an equilibrium with very little information about the network.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF