On the Asymptotic Validity of the Decoupling Assumption for Analyzing 802.11 MAC Protocol

Performance evaluation of the 802.11 MAC protocol is classically based on the decoupling assumption, which hypothesizes that the backoff processes at different nodes are independent. This decoupling assumption results from mean field convergence and is generally true in transient regime in the asymptotic sense (when the number of wireless nodes tends to infinity), but, contrary to widespread belief, may not necessarily hold in stationary regime. The issue is often related with the existence and uniqueness of a solution to a fixed point equation; however, it was also recently shown that this condition is not sufficient; in contrast, a sufficient condition is a global stability property of the associated ordinary differential equation. In this paper, we give a simple condition that establishes the asymptotic validity of the decoupling assumption for the homogeneous case. We also discuss the heterogeneous and the differentiated service cases and formulate a new ordinary differential equation. We show that the uniqueness of a solution to the associated fixed point equation is not sufficient; we exhibit one case where the fixed point equation has a unique solution but the decoupling assumption is not valid in the asymptotic sense in stationary regime.Comment: 16 pages, 4 figures, accepted for publication in IEEE Transactions on Information Theor

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

A Markov Chain Approach to IEEE 802.11WLAN Performance Analysis

Wireless communication always attracts extensive research interest, as it is a core part of modern communication technology. During my PhD study, I have focused on two research areas of wireless communication: IEEE 802.11 network performance analysis, and wireless cooperative retransmission. The first part of this thesis focuses on IEEE 802.11 network performance analysis. Since IEEE 802.11 technology is the most popular wireless access technology, IEEE 802.11 network performance analysis is always an important research area. In this area, my work includes the development of three analytical models for various aspects of IEEE 802.11 network performance analysis. First, a two-dimensional Markov chain model is proposed for analysing the performance of IEEE 802.11e EDCA (Enhanced Distributed Channel Access). With this analytical model, the saturated throughput is obtained. Compared with the existing analytical models of EDCA, the proposed model includes more correct details of EDCA, and accordingly its results are more accurate. This better accuracy is also proved by the simulation study. Second, another two-dimensional Markov chain model is proposed for analysing the coexistence performance of IEEE 802.11 DCF (Distributed Coordination Function) and IEEE 802.11e EDCA wireless devices. The saturated throughput is obtained with the proposed analytical model. The simulation study verifies the proposed analytical model, and it shows that the channel access priority of DCF is similar to that of the best effort access category in EDCA in the coexistence environment. The final work in this area is a hierarchical Markov chain model for investigating the impact of data-rate switching on the performance of IEEE 802.11 DCF. With this analytical model，the saturated throughput can be obtained. The simulation study verifies the accuracy of the model and shows the impact of the data-rate switching under different network conditions. A series of threshold values for the channel condition as well as the number of stations are obtained to decide whether the data-rate switching should be active or not. The second part of this thesis focuses on wireless cooperative retransmission. In this thesis, two uncoordinated distributed wireless cooperative retransmission strategies for single-hop connection are presented. In the proposed strategies, each uncoordinated cooperative neighbour randomly decide whether it should transmit to help the frame delivery depending on some pre-calculated optimal transmission probabilities. In Strategy 1, the source only transmits once in the first slot, and only the neighbours are involved in the retransmission attempts in the subsequent slots. In Strategy 2, both the source and the neighbours participate in the retransmission attempts. Both strategies are first analysed with a simple memoryless channel model, and the results show the superior performance of Strategy 2. With the elementary results for the memoryless channel model, a more realistic two-state Markov fading channel model is used to investigate the performance of Strategy 2. The simulation study verifies the accuracy of our analysis and indicates the superior performance of Strategy 2 compared with the simple retransmission strategy and the traditional two-hop strategy

Random Access Game and Medium Access Control Design

Motivated partially by a control-theoretic viewpoint, we propose a game-theoretic model, called random access game, for contention control. We characterize Nash equilibria of random access games, study their dynamics, and propose distributed algorithms (strategy evolutions) to achieve Nash equilibria. This provides a general analytical framework that is capable of modeling a large class of system-wide quality-of-service (QoS) models via the specification of per-node utility functions, in which system-wide fairness or service differentiation can be achieved in a distributed manner as long as each node executes a contention resolution algorithm that is designed to achieve the Nash equilibrium. We thus propose a novel medium access method derived from carrier sense multiple access/collision avoidance (CSMA/CA) according to distributed strategy update mechanism achieving the Nash equilibrium of random access game. We present a concrete medium access method that adapts to a continuous contention measure called conditional collision probability, stabilizes the network into a steady state that achieves optimal throughput with targeted fairness (or service differentiation), and can decouple contention control from handling failed transmissions. In addition to guiding medium access control design, the random access game model also provides an analytical framework to understand equilibrium and dynamic properties of different medium access protocols

A Markovian Approach to Multipath Data Transfer in Overlay Networks

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A measurement-based approach to service modeling and bandwidth estimation in IEEE 802.11 wireless networks

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