Spectrum Access Strategies With User Participation In Heterogeneous Environments

The last decade witnesses the increasing popularity of wireless communication applications. Meanwhile, the traditional spectrum allocation mechanism results in several problems, such as spectrum scarcity, spectrum inefficient use, and isolation of systems. Cognitive radio(CR) is viewed as a promising paradigm to deal with the problems mentioned above by allowing users to access the spectrum dynamically.In this thesis, we study the distributed spectrum access strategies in heterogenous environment. We design strategies from the perspective of CR users. Our target is to maximize the users’ utilities without neglecting their selfishness. The main contributions of the thesis are as follows.1. Partially observed Markov decision process-based opportunistic spectrum access We study the problem of dynamically sensing and transmission over multiple heterogeneous licensed channels. By considering the energy consumption in sensing, transmission and operating, we design energy-efficient strategies of joint sensing and transmission for an SU. We consider that the SU has only limited information of channel states, and formulate the problem as a partially observed Markov decision process(POMDP). Then we explore the relationship between the energy-efficient problem and a parametric problem, and establish the optimal threshold structure of the strategy. Accordingly, we design both optimal and approximate algorithms, by which the SU determines which channel to sense(in what sequence) and which channel to access based on the sensing history to achieve high energy efficiency.2. Congestion game-based distributed cell selection We design the cell selection mechanisms to allow the users being always best connected in heterogeneous wireless access networks. We formulate the cell selection problem as an asymmetric congestion game with consideration of users’ heterogeneity in their locations and their data rates to various cells, and prove the existence of pure Nash equilibria(PNE). In addition, we study the distributed learning algorithms which allow the users to do synchronous updates and make mistakes like in a practical system. We propose two algorithms, i.e., the sequential one and the concurrent one, to converge to PNE by the local one-step observations of users.Furthermore, we show that our algorithms can lead the users to a more satisfactory PNE.3. Matching game-based cooperative spectrum sharing We concentrate on the relay-based cooperative spectrum sharing in CR relay networks. Both the PUs and the SUs are modeled as selfish individuals, who compete for their own benefits. We model the relay network with multiple PUs and SUs as a matching market, in which the PUs have distinct preference. Then we derive the sufficient and necessary conditions for a stable matching in complete, partially incomplete and incomplete information scenarios. Furthermore, we establish two distributed algorithms by which the power-time-transferred processes among PUs and SUs are formulated. Specifically, we prove that the proposed algorithms converge to the Pareto optimal equilibrium for PUs in partially incomplete information scenario.