Random Access Game in Fading Channels with Capture: Equilibria and Braess-like Paradoxes

The Nash equilibrium point of the transmission probabilities in a slotted ALOHA system with selfish nodes is analyzed. The system consists of a finite number of heterogeneous nodes, each trying to minimize its average transmission probability (or power investment) selfishly while meeting its average throughput demand over the shared wireless channel to a common base station (BS). We use a game-theoretic approach to analyze the network under two reception models: one is called power capture, the other is called signal to interference plus noise ratio (SINR) capture. It is shown that, in some situations, Braess-like paradoxes may occur. That is, the performance of the system may become worse instead of better when channel state information (CSI) is available at the selfish nodes. In particular, for homogeneous nodes, we analytically present that Braess-like paradoxes occur in the power capture model, and in the SINR capture model with the capture ratio larger than one and the noise to signal ratio sufficiently small.Comment: 30 pages, 5 figure

Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

Medium Access Control and Network Coding for Wireless Information Flows

This dissertation addresses the intertwined problems of medium access control (MAC) and network coding in ad hoc wireless networks. The emerging wireless network applications introduce new challenges that go beyond the classical understanding of wireline networks based on layered architecture and cooperation. Wireless networks involve strong interactions between MAC and network layers that need to be jointly specified in a cross-layer design framework with cooperative and non-cooperative users. For multi-hop wireless networks, we first rediscover the value of scheduled access at MAC layer through a detailed foray into the questions of throughput and energy consumption. We propose a distributed time-division mechanism to activate dynamic transmitter-receiver assignments and eliminate interference at non-intended receivers for throughput and energy-efficient resource allocation based on stable operation with arbitrary single-receiver MAC protocols. In addition to full cooperation, we consider competitive operation of selfish users with individual performance objectives of throughput, energy and delay. We follow a game-theoretic approach to evaluate the non-cooperative equilibrium strategies at MAC layer and discuss the coupling with physical layer through power and rate control. As a cross-layer extension to multi-hop operation, we analyze the non-cooperative operation of joint MAC and routing, and introduce cooperation stimulation mechanisms for packet forwarding. We also study the impact of malicious transmitters through a game formulation of denial of service attacks in random access and power-controlled MAC. As a new networking paradigm, network coding extends routing by allowing intermediate transmitters to code over the received packets. We introduce the adaptation of network coding to wireless environment in conjunction with MAC. We address new research problems that arise when network coding is cast in a cross-layer optimization framework with stable operation. We specify the maximum throughput and stability regions, and show the necessity of joint design of MAC and network coding for throughput and energy-efficient operation of cooperative or competitive users. Finally, we discuss the benefits of network coding for throughput stability in single-hop multicast communication over erasure channels. Deterministic and random coding schemes are introduced to optimize the stable throughput properties. The results extend our understanding of fundamental communication limits and trade-offs in wireless networks

Optimisation des Systèmes Partiellement Observables dans les Réseaux Sans-fil (Théorie des jeux, Auto-adaptation et Apprentissage)

La dernière décennie a vu l'émergence d'Internet et l'apparition des applications multimédia qui requièrent de plus en plus de bande passante, ainsi que des utilisateurs qui exigent une meilleure qualité de service. Dans cette perspective, beaucoup de travaux ont été effectués pour améliorer l'utilisation du spectre sans fil.Le sujet de ma thèse de doctorat porte sur l'application de la théorie des jeux, la théorie des files d'attente et l'apprentissage dans les réseaux sans fil,en particulier dans des environnements partiellement observables. Nous considérons différentes couches du modèle OSI. En effet, nous étudions l'accès opportuniste au spectre sans fil à la couche MAC en utilisant la technologie des radios cognitifs (CR). Par la suite, nous nous concentrons sur le contrôle de congestion à la couche transport, et nous développons des mécanismes de contrôle de congestion pour le protocole TCP.Since delay-sensitive and bandwidth-intense multimedia applications have emerged in the Internet, the demand for network resources has seen a steady increase during the last decade. Specifically, wireless networks have become pervasive and highly populated.These motivations are behind the problems considered in this dissertation.The topic of my PhD is about the application of game theory, queueing theory and learning techniques in wireless networks under some QoS constraints, especially in partially observable environments.We consider different layers of the protocol stack. In fact, we study the Opportunistic Spectrum Access (OSA) at the Medium Access Control (MAC) layer through Cognitive Radio (CR) approaches.Thereafter, we focus on the congestion control at the transport layer, and we develop some congestion control mechanisms under the TCP protocol.The roadmap of the research is as follows. Firstly, we focus on the MAC layer, and we seek for optimal OSA strategies in CR networks. We consider that Secondary Users (SUs) take advantage of opportunities in licensed channels while ensuring a minimum level of QoS. In fact, SUs have the possibility to sense and access licensed channels, or to transmit their packets using a dedicated access (like 3G). Therefore, a SU has two conflicting goals: seeking for opportunities in licensed channels, but spending energy for sensing those channels, or transmitting over the dedicated channel without sensing, but with higher transmission delay. We model the slotted and the non-slotted systems using a queueing framework. Thereafter, we analyze the non-cooperative behavior of SUs, and we prove the existence of a Nash equilibrium (NE) strategy. Moreover, we measure the gap of performance between the centralized and the decentralized systems using the Price of Anarchy (PoA).Even if the OSA at the MAC layer was deeply investigated in the last decade, the performance of SUs, such as energy consumption or Quality of Service (QoS) guarantee, was somehow ignored. Therefore, we study the OSA taking into account energy consumption and delay. We consider, first, one SU that access opportunistically licensed channels, or transmit its packets through a dedicated channel. Due to the partial spectrum sensing, the state of the spectrum is partially observable. Therefore, we use the Partially Observable Markov Decision Process (POMDP) framework to design an optimal OSA policy for SUs. Specifically, we derive some structural properties of the value function, and we prove that the optimal OSA policy has a threshold structure.Thereafter, we extend the model to the context of multiple SUs. We study the non-cooperative behavior of SUs and we prove the existence of a NE. Moreover, we highlight a paradox in this situation: more opportunities in the licensed spectrum may lead to worst performances for SUs. Thereafter, we focus on the study of spectrum management issues. In fact, we introduce a spectrum manager to the model, and we analyze the hierarchical game between the network manager and SUs.Finally, we focus on the transport layer and we study the congestion control for wireless networks under some QoS and Quality of Experience (QoE) constraints. Firstly, we propose a congestion control algorithm that takes into account applications' parameters and multimedia quality. In fact, we consider that network users maximize their expected multimedia quality by choosing the congestion control strategy. Since users ignore the congestion status at bottleneck links, we use a POMDP framework to determine the optimal congestion control strategy.Thereafter, we consider a subjective measure of the multimedia quality, and we propose a QoE-based congestion control algorithm. This algorithm bases on QoE feedbacks from receivers in order to adapt the congestion window size. Note that the proposed algorithms are designed based on some learning methods in order to face the complexity of solving POMDP problems.AVIGNON-Bib. numérique (840079901) / SudocSudocFranceF

The Internet of Humans: Optimal Resource Allocation and Wireless Channel Prediction

Recent advances in information and communications technologies (ICT) have accelerated the realization of the Internet of Humans (IoH). Among the many IoH applications, Wireless Body Area Networks (BANs) are a remarkable solution that are revolutionising the health care industry. However, many challenges must be addressed, including: a) unavoidable inter-BAN interference severely degrading system performance. b) The non-stationarity and atypical dynamics of BAN channels make it extremely challenging to apply predictive transmit power control that improves the energy efficiency of the network. In this context, this thesis investigates the use of intelligent and adaptive resource allocation algorithms and effective channel prediction to achieve reliable, energy-efficient communications in BAN-enabled IoH. Firstly, we investigate the problem of co-channel interference amongst coexisting BANs by proposing a socially optimal finite repeated non-cooperative transmit power control game. The proposed method improves throughput, reduces overall power consumption and suppress interference. The game is shown to have a unique Nash equilibrium. We also prove that the aggregate outcome of the game is socially efficient across all players at the unique Nash equilibrium, given reasonable constraints for both static and slowly time-varying channels. Secondly, we address the problem of overlapping transmissions among non-coordinated BANs with multiple access schemes through intelligent link resource allocation methods. We present two non-cooperative games, employed with a time-division multiple access (TDMA) based MAC layer scheme that has a novel back-off mechanism. The Link Adaptation game jointly adjusts the sensor node's transmit power and data rate, which provides robust transmission under strong inter-BAN interference. Moreover, by adaptively tuning contention windows size an alternative game, namely a Contention Window game is developed, which significantly reduces latency. The uniqueness and existence of the games' Nash Equilibrium (NE) over the action space are proved using discrete concavity. The NE solution is further analysed and shown to be socially efficient. Motivated by the emergence of deep learning technology, we address the challenge of long-term channel predictions in BANs by using neural networks. Specifically, we propose Long Short-term Memory (LSTM)-based neural network (NN) prediction methods that provide long-term accurate channel gain prediction of up to 2s over non-stationary BAN on-body channels. An incremental learning scheme, which provides continuous and robust predictions, is also developed. We also propose a lightweight NN predictor, namely 'LiteLSTM', that has a compact structure and higher computational efficiency. When implemented on hand-held devices, 'LiteLSTM' remains functional with comparable performance. Finally, we explore the theoretical connections between BAN on-body channels' characteristics and the performance of NN-based power control. To analyse wide-sense stationarity (WSS) characteristics, different stationarity tests are performed for a range of window lengths for on-body channels. Following from this, we develop test benches for NN-based methods at corresponding window lengths using empirical channel measurements. It is observed that WSS characteristics of the BAN on-body channels have a significant impact on the performance of NN-based methods

Optimal Channel-Switching Strategies in Multi-channel Wireless Networks.

The dual nature of scarcity and under-utilization of spectrum resources, as well as recent advances in software-defined radio, led to extensive study on the design of transceivers that are capable of opportunistic channel access. By allowing users to dynamically select which channel(s) to use for transmission, the overall throughput performance and the spectrum utilization of the system can in general be improved, compared to one with a single channel or more static channel allocations. The reason for such improvement lies in the exploitation of the underlying temporal, spatial, spectral and congestion diversity. In this dissertation, we focus on the channel-switching/hopping decision of a (group of) legitimate user(s) in a multi-channel wireless communication system, and study three closely related problems: 1) a jamming defense problem against a no-regret learning attacker, 2) a jamming defense problem with minimax (worst-case) optimal channel-switching strategies, and 3) the throughput optimal strategies for a group of competing users in IEEE 802.11-like medium access schemes. For the first problem we study the interaction between a user and an attacker from a learning perspective, where an online learner naturally adapts to the available information on the adversarial environment over time, and evolves its strategy with certain payoff guarantee. We show how the user can counter a strong learning attacker with knowledge on its learning rationale, and how the learning technique can itself be considered as a countermeasure with no such prior information. We further consider in the second problem the worst-case optimal strategy for the user without prior information on the attacking pattern, except that the attacker is subject to a resource constraint, which models its energy consumption and replenishment process. We provide explicit characterization for the optimal strategies and show the most damaging attacker, interestingly, behaves randomly in an i.i.d. fashion. In the last problem, we consider a group of competing users in a non-adversarial setting. We place the interaction among users in the context of IEEE 802.11-like medium access schemes, and derive decentralized channel allocation for overall throughput improvement. We show the typically rule-of-thumb load balancing principle in spectrum resource sharing can be indeed throughput optimal.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108949/1/qingsi_1.pd