关键词: 认知无线电, 多Agent强化学习, 聚类, 功率控制, 可变学习速率

Abstract:

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.

Key words: cognitive radio, multi-agent enforcement learning, clustering, power control, variable learning rate