基于启发式强化学习的动态CRE偏置选择算法

随着通信用户数量的不断增长,低功率基站逐渐出现负载不均衡问题,小区边缘用户受到的干扰逐步增加,从而导致整个小区的通信质量降低。为解决该问题,针对双层异构网络场景,提出一种基于启发函数进行小区范围扩展(CRE)偏置值动态选择的HSARSA(λ)算法。利用启发函数改进强化学习中的SARSA(λ)算法,通过该算法寻找出最优CRE偏置值,以缓解宏基站高热点负载压力并提高网络容量。仿真结果表明,相比SARSA(λ)和Q-Learning算法,HSARSA(λ)算法的边缘用户吞吐量分别提高约7%和12%,系统能效分别提高约11%与13%,系统通信质量得到较大提升。

Dynamic CRE Bias Selection Algorithm Based on Heuristic Reinforcement Learning

With the number of communication users increasing,the load of low-power base stations gets unbalanced,resulting in the gradually rising interference of cell edge users followed by reduced communication quality of the whole cell.To address the problem,this paper proposes a HSARSA(λ)algorithm based on heuristic function for dynamic selection of Cell Range Extension(CRE)bias value in dual-layer heterogeneous networks.The heuristic function is used to improve the SARSA(λ)algorithm in Reinforcement Learning(RL),and the algorithm is adopted to find out the optimal CRE bias value,so as to relieve the high hot spot load pressure of the macro base station and improve the network capacity.Simulation results show that compared with the SARSA(λ)and Q-Learning algorithms,the throughput of edge users of the system obtained by the proposed algorithm is improved by 7%and 12%respectively,and the energy efficiency of the system is improved by 11%and 13%,which indicates a significant increase in the communication quality of the system.