An efficient energy saving scheme using reinforcement learning for 5G and beyond in H-CRAN

Abstract

Maximizing the energy saving is one of the most important metrics in 5G and Beyond (B5G) cellular mobile networks. In order to satisfy the diverse requirements of 5G/B5G in dynamic environments, Reinforcement Learning (RL) is proven as a viable approach for solving resource management problems, especially for 5G energy resources. In this paper, we propose to apply the Q-Learning (QL) Reinforcement technique in the Heterogeneous Cloud 5G Radio Access Network (H-CRAN) architecture in order to optimize the energy efficiency in 5G/B5G networks. We compare its results with the Genetic Algorithm variant using Transformation (TGA) and Particle Swarm Optimization (PSO) under high and low traffic demands. The experimental results reveal the efficiency of RL compared to TGA and PSO techniques in terms of energy efficiency and system capacity.