认知无线电网络的分布式路由协议的研究

Study on distributed routing protocol for cognitive radio networks

分布式路由算法广泛应用于认知无线电网络(CRNs)。为此,分析多跳CRNs的路由问题,利用无中心的Markov决策过程(DEC-POMDP)建立问题模型,并确保次级用户对主级用户的干扰少于预定阈值,进而控制端到端时延。最后引用多智能体学习算法解决此问题模型,进而形成基于多智能体学习的路由(MALR)。实验结果表明,提出的路由能够控制时延,并降低了干扰率。

The distributed routing algorithm is widely used in cognitive radio networks(CRNs). The distributed cooperative multi-agent routing problem in multi-hop CRNs is analyzed. The decentralized partially observable Markov decision process(DEC-POMDP)is used to establish the problem model,which can guarantee that the interference from secondary user to primary user is lower than the predefined threshold,and control the end-to-end delay. The multi-agent learning algorithm is introduced to deal with the problem model,so as to form the multi-agent learning-based routing(MALR). The experimental results show that the proposed routing can control the delay and reduce interference probability.