动态环境下基于随机博弈的认知无线网络频率分配

2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery Pub Date : 2011-10-10 DOI:10.1109/CyberC.2011.85

Xin Liu, Jinlong Wang, Qi-hui Wu, Yang Yang

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引用次数: 3

摘要

利用随机博弈模型研究了动态环境下认知无线电(CR)网络的分布频率分配问题。传统的多智能体强化学习(MARL)算法作为SG博弈的主要在线策略学习算法，不适合无线通信的应用。因此，本文提出了一种新的MARL算法——最大化平均Q函数算法(MAQ)。MAQ不仅减少了交互，而且实现了agent间的间接协调。仿真结果表明，该方法的学习效率与中心学习方法接近。

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Frequency Allocation in Dynamic Environment of Cognitive Radio Networks Based on Stochastic Game

We investigate distributed frequency allocation problem in dynamic environment of cognitive radio (CR) networks with a stochastic game (SG) model. Traditional multi-agent reinforcement learning (MARL) algorithms, as the primary online strategy learning algorithms of SG game, are not suitable for the application of wireless communication. Hence, a new MARL algorithm, maximizing the average Q function algorithm (MAQ), is proposed in this article. MAQ not only reduces the intercommunications but also realizes indirect coordination among agents. Simulation results show the learning efficiency of MAQ which is close to that of centric learning method.

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2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

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