The Experts below are selected from a list of 8589 Experts worldwide ranked by ideXlab platform
Wei Zhong - One of the best experts on this subject based on the ideXlab platform.
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Relay precoding Strategy Selection in cognitive MIMO relay networks with limited feedback
2015 International Conference on Wireless Communications & Signal Processing (WCSP), 2015Co-Authors: Wei Zhong, Zuping QianAbstract:In this paper, we study the relay precoding Strategy Selection for cognitive multiple-input multiple-output (MIMO) relay networks using limited feedback quantized precoding technique. It is assumed that the codebook of each secondary relay node is locally designed. The problem is formulated as a potential game. Then, we analyze the property of the pure Strategy Nash equilibrium of the proposed game. The feasibility of the pure Strategy NE is also studied. Furthermore, we propose a best response dynamic algorithm to find the feasible pure Strategy Nash equilibrium of the formulated game. Numerical results show that our proposed algorithm, whose convergence speed is very fast, can achieve satisfied performance.
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Energy Efficient Spectrum Sharing Strategy Selection for Cognitive MIMO Interference Channels
IEEE Transactions on Signal Processing, 2013Co-Authors: Wei Zhong, Jiaheng WangAbstract:In this paper, we propose a promising discrete game-theoretic framework for distributed energy efficient discrete spectrum sharing Strategy Selection (i.e., joint discrete power control and multimode precoding Strategy Selection) with limited feedback for cognitive MIMO interference channels. Given the competitive nature, the secondary users are assumed to be selfish and noncooperative, each of whom attempts to maximize its individual energy efficiency under a minimum data rate constraint and an interference power constraint. A mechanism for shutting down links is proposed to reduce interference and save energy. A payoff function is designed to guarantee the feasibility of the pure Strategy Nash equilibrium with no need to know the infeasible Strategy profiles (a spectrum sharing Strategy profile is said to be feasible if the stated constraints are satisfied; otherwise, the spectrum sharing Strategy profile is said to be infeasible, i.e., they may not satisfy the interference power constraint and minimum data rate constraint) in advance. We then investigate the existence and the feasibility of the pure Strategy Nash equilibrium, and further devise a pricing-based distributed algorithm for spectrum Strategy Selection. The proposed algorithm is proved to converge to a feasible pure Strategy Nash equilibrium under specific conditions. Moreover, by studying the relationship between our proposed game and the social optimum, we find that the pricing mechanism can result in Pareto improvement and lead to better convergence for the proposed distributed algorithm. Numerical results show that our designed algorithm significantly outperforms the random Selection algorithm and the pricing mechanism has a dramatic effect in improving the system performance.
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Game theoretic precoding Strategy Selection for MIMO relay networks
2012 IEEE 14th International Conference on Communication Technology, 2012Co-Authors: Gang Chen, Hua Tian, Wei Zhong, Zhensong ZhangAbstract:In this paper, we study joint source and relay precoding Strategy Selection for multiple-input multiple-output (MIMO) relay networks from a game theoretic approach. We appropriately formulate it as a potential game in which the rate is the common utility and also the potential function. This game is shown to possess at least one pure Strategy Nash equilibrium (NE) and the optimal Strategy profile which maximizes the rate constitutes a pure Strategy NE. Then we design an iterative precoding Strategy Selection algorithm based on best response rule to achieve the NE. Finally, it is shown that our designed algorithm can achieve optimal or near optimal rate performance with much lower complexity compared to the exhaustive search algorithm and the random Selection algorithm.
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Game-Theoretic Opportunistic Spectrum Sharing Strategy Selection for Cognitive MIMO Multiple Access Channels
IEEE Transactions on Signal Processing, 2011Co-Authors: Wei Zhong, Youyun Xu, Huaglory TianfieldAbstract:This paper studies opportunistic spectrum sharing Strategy (i.e., quantized precoding Strategy) Selection for cognitive multiple-input multiple-output multiple access channels with limited feedback under interference power constraint and maximum transmission stream number constraint. We put forward a game-theoretic framework to model the precoding Strategy Selection behaviors of the secondary users under the specified constraints. First, we prove the formulated discrete game is a potential game which possesses at least one feasible pure Strategy Nash equilibrium. The feasibility and optimality of the Nash equilibrium are also analyzed. Then we prove that the solution to the sum rate maximization problem constitutes a feasible pure Strategy Nash equilibrium of our formulated game. Furthermore, we design two algorithms. The iterative precoding Strategy Selection algorithm based on the best response rule is designed to attain a feasible Nash equilibrium. The modified algorithm is designed to improve the sum rate performance. Simulation results show that our designed algorithms can achieve optimal or near optimal sum rate performance with low complexity.
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GLOBECOM - Distributed Energy Efficient Spectrum Sharing Strategy Selection with Limited Feedback in MIMO Interference Channels
2010 IEEE Global Telecommunications Conference GLOBECOM 2010, 2010Co-Authors: Wei Zhong, Youyun XuAbstract:In this paper, we study the distributed energy efficient spectrum sharing Strategy Selection (i.e., joint discrete power control and multimode precoding Strategy Selection) with limited feedback in MIMO interference channels. We assume that the users are selfish and noncooperative. The goal of each user is to maximize its individual energy efficiency under the constraints of the minimum data rate and the interference temperature. Game theory is used to model the spectrum sharing Strategy Selection. We design a payoff function to guarantee the feasibility of the pure Nash equilibrium of the game without knowing the infeasible Strategy profiles in advance. Then we propose a distributed game-theoretic spectrum Strategy Selection algorithm and prove that this algorithm always attains the feasible Strategy profiles. Numerical results show that the proposed algorithm can enhance the energy efficiency of the users and significantly outperforms the random Selection algorithm.
Youyun Xu - One of the best experts on this subject based on the ideXlab platform.
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Game-Theoretic Opportunistic Spectrum Sharing Strategy Selection for Cognitive MIMO Multiple Access Channels
IEEE Transactions on Signal Processing, 2011Co-Authors: Wei Zhong, Youyun Xu, Huaglory TianfieldAbstract:This paper studies opportunistic spectrum sharing Strategy (i.e., quantized precoding Strategy) Selection for cognitive multiple-input multiple-output multiple access channels with limited feedback under interference power constraint and maximum transmission stream number constraint. We put forward a game-theoretic framework to model the precoding Strategy Selection behaviors of the secondary users under the specified constraints. First, we prove the formulated discrete game is a potential game which possesses at least one feasible pure Strategy Nash equilibrium. The feasibility and optimality of the Nash equilibrium are also analyzed. Then we prove that the solution to the sum rate maximization problem constitutes a feasible pure Strategy Nash equilibrium of our formulated game. Furthermore, we design two algorithms. The iterative precoding Strategy Selection algorithm based on the best response rule is designed to attain a feasible Nash equilibrium. The modified algorithm is designed to improve the sum rate performance. Simulation results show that our designed algorithms can achieve optimal or near optimal sum rate performance with low complexity.
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GLOBECOM - Distributed Energy Efficient Spectrum Sharing Strategy Selection with Limited Feedback in MIMO Interference Channels
2010 IEEE Global Telecommunications Conference GLOBECOM 2010, 2010Co-Authors: Wei Zhong, Youyun XuAbstract:In this paper, we study the distributed energy efficient spectrum sharing Strategy Selection (i.e., joint discrete power control and multimode precoding Strategy Selection) with limited feedback in MIMO interference channels. We assume that the users are selfish and noncooperative. The goal of each user is to maximize its individual energy efficiency under the constraints of the minimum data rate and the interference temperature. Game theory is used to model the spectrum sharing Strategy Selection. We design a payoff function to guarantee the feasibility of the pure Nash equilibrium of the game without knowing the infeasible Strategy profiles in advance. Then we propose a distributed game-theoretic spectrum Strategy Selection algorithm and prove that this algorithm always attains the feasible Strategy profiles. Numerical results show that the proposed algorithm can enhance the energy efficiency of the users and significantly outperforms the random Selection algorithm.
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Precoding Strategy Selection for Cognitive MIMO Multiple Access Channels Using Learning Automata
2010 IEEE International Conference on Communications, 2010Co-Authors: W. Zhong, Youyun XuAbstract:In this paper, we study the quantized precoding Strategy Selection for multiple-input multiple-output (MIMO) multiple access channels (MAC) in cognitive radio (CR) networks through a game-theoretic perspective. Since the secondary users in such system are difficult to be coordinated by a centralized authority, they are noncooperative and attempt to maximize their own payoffs selfishly in a distributed method. We propose a noncooperative precoding Strategy Selection game and find that it is a potential game which possesses at least one pure Strategy Nash equilibrium. A decentralized learning algorithm with a small amount of feedback is proposed to obtain Nash equilibrium. We prove that the proposed algorithm can converge to a pure Strategy Nash equilibrium. Simulation results are provided to verify our analysis.
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Distributed Energy Efficient Spectrum Sharing Strategy Selection with Limited Feedback in MIMO Interference Channels
2010 IEEE Global Telecommunications Conference GLOBECOM 2010, 2010Co-Authors: Wei Zhong, Youyun XuAbstract:In this paper, we study the distributed energy efficient spectrum sharing Strategy Selection (i.e., joint discrete power control and multimode precoding Strategy Selection) with limited feedback in MIMO interference channels. We assume that the users are selfish and noncooperative. The goal of each user is to maximize its individual energy efficiency under the constraints of the minimum data rate and the interference temperature. Game theory is used to model the spectrum sharing Strategy Selection. We design a payoff function to guarantee the feasibility of the pure Nash equilibrium of the game without knowing the infeasible Strategy profiles in advance. Then we propose a distributed game-theoretic spectrum Strategy Selection algorithm and prove that this algorithm always attains the feasible Strategy profiles. Numerical results show that the proposed algorithm can enhance the energy efficiency of the users and significantly outperforms the random Selection algorithm.
Zhihai Rong - One of the best experts on this subject based on the ideXlab platform.
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coevolution of Strategy Selection time scale and cooperation in spatial prisoner s dilemma game
EPL, 2013Co-Authors: Zhihai Rong, Zhixi Wu, Guanrong ChenAbstract:In this paper, we investigate a networked prisoner's dilemma game where individuals' Strategy-Selection time scale evolves based on their historical learning information. We show that the more times the current Strategy of an individual is learnt by his neighbors, the longer time he will stick on the successful behavior by adaptively adjusting the lifetime of the adopted Strategy. Through characterizing the extent of success of the individuals with normalized payoffs, we show that properly using the learned information can form a positive feedback mechanism between cooperative behavior and its lifetime, which can boost cooperation on square lattices and scale-free networks.
Hong-qi Zhang - One of the best experts on this subject based on the ideXlab platform.
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Optimal Strategy Selection approach to moving target defense based on Markov robust game
Computers & Security, 2019Co-Authors: Hong-qi Zhang, Yu-qiao ChengAbstract:Abstract Moving target defense, as a “game-changing” security technique for network warfare, thwarts the apparent certainty of attackers by transforming the network resource vulnerabilities. In order to enhance the defense of unknown security threats, a novel of optimal Strategy Selection approach to moving target defense based on Markov robust game is first proposed in this paper. Firstly, moving target defense model based on moving attack and exploration surfaces is defined. Thus, the random emerging of vulnerabilities is described, as well as the cognitive and behavioral difference of offensive and defensive sides caused by defensive transformation. Based on it, Markov robust game model is constructed to depict the multistage and multistate features of moving target defense confrontation, in which the unknown prior information in incomplete information assumption are illustrated by combining Markov decision process with robust game theory. Further, the existence of optimal Strategy of Markov robust game is proved. Additionally, by equivalent converting optimal Strategy Selection into a nonlinear programming problem, an efficient optimal defensive Strategy Selection algorithm is designed. Finally, simulation and deduction of the proposed approach are given in the case study so as to demonstrate the feasibility of constructed game model and effectiveness of the proposed approach.
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Optimal Strategy Selection for Moving Target Defense Based on Markov Game
IEEE Access, 2017Co-Authors: Hong-qi ZhangAbstract:With the evolution of the research on network moving target defense (MTD), the Selection of optimal Strategy has become one of the key problems in current research. Directed to the problem of the improper defensive Strategy Selection caused by inaccurately characterizing the attack and defense game in MTD, optimal Strategy Selection for MTD based on Markov game (MG) is proposed to balance the hopping defensive revenue and network service quality. On the one hand, traditional matrix game structure often fails to describe MTD confrontation accurately. To deal with this inaccuracy, MTD based on MG is constructed. Markov decision process is used to characterize the transition among network multi-states. Dynamic game is used to characterize the multi-phases of attack and defense in MTD circumstances. Besides, it converts all the attack and defense actions into the changes in attack surface or the ones in exploration surface, thus improving the universality of the proposed model. On the other hand, traditional models care little about defense cost in the process of optimal Strategy Selection. After comprehensively analyzing the impact of defense cost and defense benefit on the Strategy Selection, an optimal Strategy Selection algorithm is designed to prevent the deviation of the selected strategies from actual network conditions, thus ensuring the correctness of optimal Strategy Selection. Finally, the simulation and the deduction of the proposed approach are given in case study so as to demonstrate the feasibility and effectiveness of the proposed Strategy optimal Selection approach.
Robert Schober - One of the best experts on this subject based on the ideXlab platform.
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Downlink Scheduling with Transmission Strategy Selection for Multi-Cell MIMO Systems
IEEE Transactions on Wireless Communications, 2013Co-Authors: Vincent W.s. Wong, Robert SchoberAbstract:In this paper, we study downlink scheduling with transmission Strategy Selection in multi-cell multiple-input multiple-output (MIMO) systems. Depending on the level of inter-cell interference experienced by a user, the scheduler can choose between two MIMO transmission strategies, namely, spatial multiplexing and interference alignment. We formulate an optimization problem which aims to jointly select a user and the corresponding transmission Strategy for each base station in order to maximize the overall system utility while stabilizing all transmission queues. We first develop a centralized dynamic scheduling scheme with transmission Strategy Selection by using a stochastic network optimization approach. To reduce the communication overhead, we then propose a distributed scheduling algorithm which only requires limited message exchange between the base stations. We also consider the impact of imperfect channel state information on the scheduling schemes and propose an efficient rate adjustment method to improve the performance for this case. Simulation results show that the performance of the proposed distributed scheduling scheme is close to that of the centralized scheduling scheme, and both schemes achieve a better performance than schemes employing a single transmission Strategy.
