Cognitive Radio Resource Allocation by Clustering Multi-Agent Enforcement Learning
Chu Lee Wu · Beijing Youdian Xueyuan xuebao · 2014
A multi-agent enforcement learning method based on user clustering as well as a variable learning rate was proposed for solving the problem of channel allocation and power control within multi cognitive radio users. Firstly,a hierarchy processing method was used to separate channel selection and power control. The channel allocation was implemented by fast optimal search combined with user-number balance. Secondly,stochastic game framework was adopted to model the multiuser power control issue. In subsequent multi-agent enforcement learning,K-means user clustering method was employed to reduce the user number in game and single user's environment complexity,and a variable learning rate scheme for Q learning and policy learning was proposed to promote the convergence of multiuser learning. Simulation shows that the method can make multiuser's power status and global reward converging effectively,moreover the whole performance can reach sub-optimal.