Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN

TL;DR

This paper proposes a hierarchical reinforcement learning approach to optimize energy efficiency in RIS-assisted 5G and 6G networks by managing base station sleep modes and power levels, leading to significant energy savings.

Contribution

It introduces a novel HRL algorithm that combines RIS technology with sleep control for improved energy efficiency in heterogeneous networks.

Findings

RIS-assisted sleep control reduces energy consumption.

The approach doubles energy efficiency compared to no-RIS scenarios.

It achieves higher throughput with lower energy use.

Abstract

Reconfigurable intelligent surface (RIS) is emerging as a promising technology to boost the energy efficiency (EE) of 5G beyond and 6G networks. Inspired by this potential, in this paper, we investigate the RIS-assisted energy-efficient radio access networks (RAN). In particular, we combine RIS with sleep control techniques, and develop a hierarchical reinforcement learning (HRL) algorithm for network management. In HRL, the meta-controller decides the on/off status of the small base stations (SBSs) in heterogeneous networks, while the sub-controller can change the transmission power levels of SBSs to save energy. The simulations show that the RIS-assisted sleep control can achieve significantly lower energy consumption, higher throughput, and more than doubled energy efficiency than no-RIS conditions.