DDPG-based energy efficiency optimization for ABS-assisted beyond-5G cellular networks with sleep mode management - Saleh, Vala; Eslami, Mohsen; Kazemi, Kamran (Frontiers in Communications and Networks, 2026)
IntroductionEnergy 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.MethodsThis 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.ResultsSimulation 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.DiscussionThe 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.