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.
@article{arxiv.2301.02771,
title = {Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN},
author = {Hao Zhou and Long Kong and Medhat Elsayed and Majid Bavand and Raimundas Gaigalas and Steve Furr and Melike Erol-Kantarci},
journal= {arXiv preprint arXiv:2301.02771},
year = {2023}
}
Comments
This paper has been accepted by 2022 IEEE Globecom