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首页> 外文期刊>Wireless Communications, IEEE Transactions on >A Generalized Nash Equilibrium Approach for Robust Cognitive Radio Networks via Generalized Variational Inequalities
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A Generalized Nash Equilibrium Approach for Robust Cognitive Radio Networks via Generalized Variational Inequalities

机译：通过广义变分不等式的鲁棒认知无线电网络的广义纳什均衡方法

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摘要

Resource sharing between primary users (PUs) and secondary users (SUs) in cognitive radio (CR) networks is built on strict interference limitations. However, such limitations may be easily violated by SUs using imperfect SU-to-PU channel state information (CSI). In this paper, we propose a robust decentralized CR network design by explicitly taking into account imperfect SU-to-PU CSI from a game theoretical perspective. We formulate the CR network design as a generalized Nash equilibrium problem (GNEP), where the SUs compete with each other over the resources made available by the PUs, who are protected by the robust aggregate interference constraints. We establish a framework-based generalized variational inequality (GVI) theory to analyze the formulated robust GNEP. It is shown that the solution to the robust GNEP can be obtained by solving a GVI, which can be addressed by a distributed pricing mechanism in the CR network, where the SUs play a priced NEP with given prices and the PUs are in charge of setting prices. Then, we propose distributed algorithms, along with their convergence properties, for the SUs to solve the priced NEP and for the PUs to update prices, respectively. We also provide an efficient method to compute the optimal transmit strategy of each SU via convex optimization.
机译：认知无线电（CR）网络中的主要用户（PU）和次要用户（SU）之间的资源共享是建立在严格的干扰限制之上的。但是，使用不完善的SU到PU通道状态信息（CSI）的SU可能容易违反这些限制。在本文中，我们从博弈论的角度明确考虑了不完善的SU-to-PU CSI，提出了一种健壮的分散式CR网络设计。我们将CR网络设计公式化为广义Nash均衡问题（GNEP），其中SU之间在PU可用的资源上相互竞争，而这些PU受到强大的聚合干扰约束的保护。我们建立了基于框架的广义变分不等式（GVI）理论，以分析制定的鲁棒GNEP。结果表明，可以通过求解GVI来获得针对健壮GNEP的解决方案，该解决方案可以通过CR网络中的分布式定价机制解决，其中SU以给定价格播放定价的NEP，而PU负责设置价格。然后，我们提出了分布式算法及其收敛性，分别用于SU解决定价的NEP和PU更新价格。我们还提供了一种有效的方法，通过凸优化来计算每个SU的最佳传输策略。

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