Distributed Opportunistic Spectrum Access in an Unknown and Dynamic Environment: A Stochastic Learning Approach
Distributed Opportunistic Spectrum Access in an Unknown and Dynamic Environment: A Stochastic Learning Approach
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DOI：
10.1109/tvt.2018.2789344
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发表时间：
2018-05-01
影响因子：
6.8
通讯作者：
Cai, Jun
中科院分区：
文献类型：
--
作者：
Cao, Huijin;Cai, Jun
研究了机会频谱接入网络中多个次用户和多个主信道的分布式吞吐量最大化问题。为了解决在动态和未知的环境中设计有效的解决方案的挑战，我们制定的优化问题作为一个非合作的游戏，这是进一步证明是一个有序的潜在的游戏。然后，我们提出了一个基于最佳响应的算法，以实现制定的游戏的纳什均衡点（NEPs），鉴于存在一个协调员的SU工作在一个循环的方式和一个共同的控制信道的SU交换信息。为了进一步减轻系统开销，由于SU之间的信息交换，我们设计了一个新的随机学习自动机（SLA）为基础的算法，称为N-SLA，它可以收敛到纯策略的NEP制定序数势博弈在一个完全分布式的方式。据我们所知，我们是第一个解决基于SLA的一般有序势博弈算法收敛问题的人。仿真结果验证了所提算法的有效性。
In this paper, the problem of distributed throughput maximization in an opportunistic spectrum access network with multiple secondary users (SUs) and multiple primary channels is investigated. To address the challenges in designing efficient solutions in a dynamic and unknown environment, we formulate the optimization problem as a noncooperative game, which is further proved to be an ordinal potential game. We then propose a best-response-based algorithm to achieve the Nash equilibrium points (NEPs) of the formulated game, given that there exists a coordinator for SUs to work in a round-robin fashion and a common control channel for SUs to exchange their information. To further relieve the system overhead due to information exchange among SUs, we design a new stochastic learning automata (SLA)-based algorithm, called N-SLA, which can converge to the pure-strategy NEPs of the formulated ordinal potential game in a fully distributed way. To our best knowledge, we are the first to address the convergence issue of the SLA-based algorithms for general ordinal potential games. Simulation results validate the effectiveness of our proposed algorithms.