Deep Learning-Based Traffic-Aware Base Station Sleep Mode and Cell Zooming Strategy in RIS-Aided Multi-Cell Networks

TL;DR

This paper introduces a novel RIS-assisted multi-cell network strategy that combines sleep modes, adaptive cell zooming, and intelligent surface reconfiguration to significantly reduce base station energy consumption while managing delay.

Contribution

It proposes a new joint optimization framework using MDP and deep reinforcement learning to minimize energy use in RIS-aided multi-cell networks, balancing delay and efficiency.

Findings

Energy consumption reduced by 49.61% compared to benchmarks.

PPO algorithm effectively balances energy savings and delay.

RIS reflection optimization enhances overall energy efficiency.

Abstract

Advances in wireless technology have significantly increased the number of wireless connections, leading to higher energy consumption in networks. Among these, base stations (BSs) in radio access networks (RANs) account for over half of the total energy usage. To address this, we propose a multi-cell sleep strategy combined with adaptive cell zooming, user association, and reconfigurable intelligent surface (RIS) to minimize BS energy consumption. This approach allows BSs to enter sleep during low traffic, while adaptive cell zooming and user association dynamically adjust coverage to balance traffic load and enhance data rates through RIS, minimizing the number of active BSs. However, it is important to note that the proposed method may achieve energy-savings at the cost of increased delay, requiring a trade-off between these two factors. Moreover, minimizing BS energy consumption under the delay constraint is a complicated non-convex problem. To address this issue, we model the RIS-aided multi-cell network as a Markov decision process (MDP) and use the proximal policy optimization (PPO) algorithm to optimize sleep mode (SM), cell zooming, and user association. Besides, we utilize a double cascade correlation network (DCCN) algorithm to optimize the RIS reflection coefficients. Simulation results demonstrate that PPO balances energy-savings and delay, while DCCN-optimized RIS enhances BS energy-savings. Compared to systems optimised by the benchmark DQN algorithm, energy consumption is reduced by 49.61%