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Limited Feedback Dynamic Spectrum Sharing In Cognitive Radio Systems

Posted on:2012-01-30Degree:DoctorType:Dissertation
Country:ChinaCandidate:W ZhongFull Text:PDF
GTID:1118330362958314Subject:Communication and Information System
Abstract/Summary:PDF Full Text Request
Recently, with the increasing size and capacity of the wireless communicationsystems, the exclusive use of spectrum makes the precious radio spectrum be-come scarce. Cognitive radio, which allow the idle spectrum dynamically sharedby unlicensed users, is a novel techniques that can improve the spectrum e?cien-cy. Multiple input and multiple output (MIMO) also can improve the spectrume?ciency signi?cantly. It is thus quit nature to combine these two techniquestogether to achieve overall spectrum e?ciency. And this technological combi-nation results in the so-called cognitive MIMO radio, which has attracted greatattentions in recent years.In general, cognitive radio systems are dynamical and heterogeneous , theglobal information of overall systems is usually unavailable. Thus, limited feed-back dynamic spectrum sharing for cognitive radio systems is challenging.The spectrum strategies are discrete in limited feedback cognitive radio sys-tems. To obtain the optimal spectrum sharing strategies, i.e., to get a solution ofa discrete optimization problem, the system usually requires brute-force searchalgorithm with very high complexity and results in a large number of overheads.Therefore, it is impractical to attain the optimal spectrum sharing strategies.Game theory, a powerful tool to analyze the distributed discrete non-convex op-timization problems, is thus appropriate to be used to study the limited feedbackdynamic spectrum sharing. In our thesis, we provide the frameworks for the lim-ited feedback dynamic spectrum sharing and study the frameworks through agame theoretic approach. Our works are brie?y introduced as blows.First, we study the auction based limited feedback dynamic spectrum shar-ing for multi-type buyers (i.e., the secondary users that have di?erent risk prefer- ence) with adaptive non-standard sealed-bid parameterized auction mechanismsdesign. Based on learning automata, we propose a distributed algorithm wherethe auction mechanism can be adaptively designed to maximize the revenue ofauctioneer and the secondary users can dynamically select the bidding strategiesand maximize their own utilities. It is shown that our propose algorithm is ef-?cient and can achieve higher revenue for the base station than the ?xed andrandom auction mechanism design schemes.We then study the decentralized selection of spectrum sharing strategy (i.e.,multimode precoding strategy) for cognitive multiple-input multiple-output (MI-MO) multiple access channels without any constraints. We formulate it as adiscrete noncooperative game. This game is shown to possess at least one pure s-trategy Nash equilibrium (NE) and the optimal strategy pro?le which maximizesthe sum rate constitutes a pure strategy NE. Then we propose a decentralizedalgorithm based on learning automata to achieve the NE. A repeated mechanismis introduced to improve the sum rate performance and a mechanism for adaptingstep size is designed to control the convergence speed. The proposed algorithm,which only requires limited feedback, can achieve near optimal or optimal sumrate performance.After that, we study opportunistic spectrum sharing strategy (i.e., quantizedprecoding strategy) selection for cognitive multiple-input multiple-output mul-tiple access channels with limited feedback under interference power constraintand maximum transmission stream number constraint. We put forward a game-theoretic framework to model the precoding strategy selection behaviors of thesecondary users under the speci?ed constraints. First, we prove the formulateddiscrete game is a potential game which possesses at least one feasible pure strat-egy Nash equilibrium. The feasibility and optimality of the Nash equilibrium arealso analyzed. Then we prove that the solution to the sum rate maximizationproblem constitutes a feasible pure strategy Nash equilibrium of our formulatedgame. Furthermore, we design two algorithms. The iterative precoding strategyselection algorithm based on the best response rule is designed to attain a feasibleNash equilibrium. The modi?ed algorithm is designed to improve the sum rateperformance. Our designed algorithms can achieve optimal or near optimal sum rate performance with low complexity.Finally, we provide a promising game-theoretic framework for the distribut-ed energy e?cient sharing strategy selection (i.e., joint discrete power controland multimode precoding strategy selection) with limited feedback for MIMOinterference channels. We assume that the cognitive users are sel?sh and nonco-operative. The goal of each cognitive user is to maximize its individual energye?ciency under the constraints of the minimum data rate and the interferencetemperature. We de?ne a payo? function to guarantee the feasibility of the pureNash equilibrium of the game without knowing the infeasible strategy pro?lesin advance. Furthermore, we analyze the existence and the feasibility of thepure Nash equilibrium. We design a distributed game-theoretic spectrum strat-egy selection algorithm and prove that this algorithm always attains the feasiblestrategy pro?les. Our results show that, compared to the random selection algo-rithm, the designed algorithm signi?cantly reduces the transmission power levelsand improves the transmission rates of the secondary users. Moreover, the pric-ing mechanism can further improve the energy e?ciency of the cognitive usersdramatically.
Keywords/Search Tags:Cognitive radio, Limited feedback, Dynamic spectrum shar-ing, MIMO, Game theory, Auction mechanism design, Multimode precoding, Learning automata, Nash equilibrium
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