Joint Sensing and Power Allocation in Nonconvex Cognitive Radio Games: Nash Equilibria and Distributed Algorithms
Joint Sensing and Power Allocation in Nonconvex Cognitive Radio Games: Nash Equilibria and Distributed Algorithms
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DOI：
10.1109/tit.2013.2239354
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发表时间：
2012-12
影响因子：
2.5
通讯作者：
G. Scutari;J. Pang
中科院分区：
文献类型：
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作者：
G. Scutari;J. Pang
本文提出了一类新型的认知无线电（CR）网络纳什问题，模型为高斯频率选择性干扰信道，其中每个二级用户（SU）通过联合选择感知持续时间、检测阈值和矢量功率分配来与其他用户竞争，以最大化自己的机会吞吐量。所提出的一般配方允许我们容纳几个（发射）功率和（确定性/概率性）干扰的约束，如约束的最大个人和/或聚合（概率）在主接收器的干扰容忍。为了保持优化尽可能分散，全局（耦合）干扰约束是通过惩罚每个SU与一组时变价格的基础上，他的总干扰的贡献;价格是额外的变量优化。由此产生的球员的优化问题是非凸的，而且，有可能是价格清算条件相关联的全球约束要满足的解决方案。所有这一切都使得所提出的游戏的分析是一项具有挑战性的任务，没有一个经典的结果在博弈论文献中可以成功地应用。本文的主要贡献是发展了一种新型的基于优化的理论来研究所提出的非凸博弈;我们对标准纳什均衡的存在性和唯一性进行了全面分析，设计了替代的基于最佳响应的算法，并建立了它们的收敛性。所提出的算法中的一些是完全分布式和异步的，而另一些则需要SU之间的有限信令（以共识算法的形式）以有利于更好的性能;总体而言，它们因此适用于各种CR场景，无论是合作还是非合作，这使得SU能够探索信令和性能之间的现有权衡。
In this paper, we propose a novel class of Nash problems for cognitive radio (CR) networks, modeled as Gaussian frequency-selective interference channels, wherein each secondary user (SU) competes against the others to maximize his own opportunistic throughput by choosing jointly the sensing duration, the detection thresholds, and the vector power allocation. The proposed general formulation allows us to accommodate several (transmit) power and (deterministic/probabilistic) interference constraints, such as constraints on the maximum individual and/or aggregate (probabilistic) interference tolerable at the primary receivers. To keep the optimization as decentralized as possible, global (coupling) interference constraints are imposed by penalizing each SU with a set of time-varying prices based upon his contribution to the total interference; the prices are thus additional variable to optimize. The resulting players' optimization problems are nonconvex; moreover, there are possibly price clearing conditions associated with the global constraints to be satisfied by the solution. All this makes the analysis of the proposed games a challenging task; none of classical results in the game theory literature can be successfully applied. The main contribution of this paper is to develop a novel optimization-based theory for studying the proposed nonconvex games; we provide a comprehensive analysis of the existence and uniqueness of a standard Nash equilibrium, devise alternative best-response based algorithms, and establish their convergence. Some of the proposed algorithms are totally distributed and asynchronous, whereas some others require limited signaling among the SUs (in the form of consensus algorithms) in favor of better performance; overall, they are thus applicable to a variety of CR scenarios, either cooperative or noncooperative, which allows the SUs to explore the existing tradeoff between signaling and performance.