DDPG-based energy efficiency optimization for ABS-assisted beyond-5G cellular networks with sleep mode management

Introduction Energy efficiency is a critical challenge in Beyond-5G (B5G) cellular networks, where ground base stations (GBSs) are responsible for a substantial portion of network energy consumption. Reducing this consumption while maintaining minimum user data rate requirements remains a key research problem. Methods This paper proposes an Aerial Base Station (ABS)-assisted energy optimization framework that integrates ABS deployment with low-power sleep states of GBSs. Traffic is selectively offloaded from lightly loaded GBSs to ABSs, enabling energy savings without violating user quality-of-service constraints. A Deep Deterministic Policy Gradient (DDPG) algorithm is employed to jointly optimize ABS positioning, GBS sleep mode scheduling, and resource allocation under dynamic traffic conditions. Results Simulation results demonstrate that the proposed DDPG-based framework significantly reduces network energy consumption while improving achievable user data rates compared to baseline schemes without ABS assistance or learning-based optimization. Discussion The results highlight the effectiveness of integrating ABSs with GBS low-power sleep states using reinforcement learning. By enforcing minimum data rate constraints and dynamically adapting to traffic variations, the proposed approach provides a scalable and energy-efficient solution for sustainable operation.