摘要：

为提高分布式认知无线网络认知用户信道与功率分配算法的能量效率和收敛速度,将单位能量的平均比特数作为通信效率指标,平衡用户通信质量和系统能量消耗,提出一种基于多Agent协作强化学习的分布式信道与功率分配算法。在多Agent独立Q学习的基础上引入协作学习,各用户通过独立Q学习后,共享Q值并进行融合再学习。仿真结果表明,与基于能效的独立Q学习算法、独立Q学习算法以及随机功率分配算法相比,该算法能够有效提高认知用户发射功率和信道分配时的收敛速度。

关键词: 信道与功率分配, 协作Q学习, 认知无线电, 能量效率, 冲突概率

Abstract:

In order to improve the energy efficiency and convergence speed of cognitive user channel and power allocation algorithms in distributed cognitive wireless networks,use the average number of bits per unit of energy as a communication efficiency indicator,and balance user communication quality and system energy consumption,this paper proposes a distributed channel and power allocation algorithm based on multi-Agent cooperative reinforcement learning.The collaborative learning is introduced on the basis of multi-agent independent Q-learning is introduced,and users share Q values and fuse after independent Q-learning.Simulation results show that the algorithm can effectively improve the convergence speed of cognitive users in transmitting power and channel allocation compared with energy efficiency-based independent Q-learning algorithm,independent Q-learning algorithm and random power allocation algorithm.

Key words: channel and power allocation, cooperative Q learning, cognitive radio, energy efficiency, collision probability

中图分类号: 

• TP925.5