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首页> 外文期刊>IEEE Transactions on Information Theory >Joint Sensing and Power Allocation in Nonconvex Cognitive Radio Games: Nash Equilibria and Distributed Algorithms
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Joint Sensing and Power Allocation in Nonconvex Cognitive Radio Games: Nash Equilibria and Distributed Algorithms

机译：非凸认知无线电游戏中的联合传感和功率分配：纳什均衡和分布式算法

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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.
机译：在本文中，我们为认知无线电（CR）网络提出了一类新颖的Nash问题，建模为高斯频率选择性干扰信道，其中，每个次要用户（SU）都通过与其他次要用户共同竞争，以最大化自己的机会吞吐量。检测持续时间，检测阈值和矢量功率分配。提出的一般公式使我们能够适应几个（发射）功率和（确定性/概率性）干扰约束，例如对主要接收器可容忍的最大单个和/或总体（概率性）干扰的约束。为了使优化尽可能分散，通过基于每个SU对总干扰的贡献以一组随时间变化的价格对每个SU进行惩罚来施加全局（耦合）干扰约束。因此价格是要优化的附加变量。由此产生的玩家优化问题是非凸的。此外，解决方案可能需要满足与全局约束相关的价格结算条件。所有这些使拟议游戏的分析成为一项艰巨的任务。博弈论文献中的经典结果都无法成功应用。本文的主要贡献是发展了一种新颖的基于优化的理论来研究所提出的非凸博弈。我们提供了标准纳什均衡存在性和唯一性的综合分析，设计了基于最佳响应的替代算法，并建立了它们的收敛性。所提出的一些算法是完全分布式且异步的，而另一些算法则需要SU之间有限的信令（以共识算法的形式），以实现更好的性能。总体而言，它们适用于合作或非合作的各种CR场景，这使SU可以探索信号和性能之间的现有折衷。

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