认知无线电网络中基于随机学习博弈的信道分配与功率控制

传统的认知无线电频谱分配算法往往忽略节点的传输功率对网络干扰的影响，且存在节点间交互成本高的问题.为此，通过量化传输功率等级，以最大化弹性用户收益为目标，构建联合频谱分配与功率控制非合作博弈模型，证明了该博弈为严格潜在博弈且收敛到纳什均衡点.进一步，将随机学习理论引入博弈模型，提出了基于随机学习的策略选择算法，并给出了该算法收敛到纯策略纳什均衡点的充分条件及严格证明.仿真结果表明，所提算法在少量信息交互前提下能获得较高的传输速率，并提升用户满意度.

Channel Assignment and Power Control Based on Stochastic Learning Game in Cognitive Radio Networks

Traditional cognitive radio spectrum allocation algorithms tend to ignore the influence of transmission power on network interference and have the drawback of high interaction cost between nodes.In response to these problems,by quantifying transmission power levels,we formulate the channel assignment and power control problem as a distributed non-cooperative game,in which each second user's purpose is to maximize the elastic traffic rewards.Formally,the formulated game is proved to be an exact potential game and converges to Nash equilibrium (NE) point.Furthermore,introducing the stochastic learning theory into game model,we propose a strategy selection algorithm based on stochastic learning,then the sufficient condition and strict proof for the convergence of this algorithm to pure strategy NE point are given.Finally,Simulation results show that the proposed algorithm can achieve high system throughput and improve users' satisfaction with a small amount of interactions.