Joint Sleep Mode Activation and Load Balancing with Dynamic Cell Load: A Combinatorial Bandit Approach

2026-02-04

AI Summary

This work addresses the joint optimization of small base station (SBS) sleep scheduling and load balancing in 5G networks by proposing a combinatorial multi-armed bandit (CMAB)-based strategy that enhances system energy efficiency while meeting user 5G QoS Indicator (5QI) requirements. The approach innovatively incorporates dynamic cell load—integrating user location, relative distance, and traffic demand—into SBS sleep decisions and jointly optimizes them with Cell Range Expansion (CRE)-based load balancing. A tailored Combinatorial Upper Confidence Bound (CUCB) algorithm is designed for deployment within the O-RAN near-real-time RIC xApps architecture. Experimental results demonstrate that the proposed method significantly outperforms both the always-on baseline and other reinforcement learning approaches in terms of energy efficiency, while strictly adhering to 5QI service constraints.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Machine Learning: Online Learning & Bandits
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

We propose a combinatorial bandit formulation to opportunistically trigger sleep modes in gNode-B (gNB) small cells (SCs), followed by a cell range expansion (CRE)-based load balancing procedure. This is implemented by ensuring that the fifth generation (5G) quality of service identifier (5QI)-requirements of user equipments (UEs) are maintained. The key challenge is the fact that while deactivating a given SC gNB reduces its own consumption, it may increase the load on neighboring gNBs and the macro gNB (coverage cell), impacting the overall energy efficiency. This phenomenon is accurately characterized by modeling the dynamic cell load that jointly takes into account the location of the UEs, their relative locations to all the SCs, and their data demands. We experimentally show that the proposed combinatorial upper confidence bound (CUCB) followed by the load balancer outperforms not only the naive strategies like arbitrarily keeping all the SCs on, but also other state-of-the-art reinforcement learning solutions. The proposed algorithm can be implemented as open-radio access network (O-RAN) near-real-time (NRT) RAN intelligent controller (RIC) xApps.

Problem

• sleep mode activation
• load balancing
• dynamic cell load
• energy efficiency
• 5G networks

Innovation

• combinatorial bandit
• sleep mode activation
• load balancing
• dynamic cell load
• O-RAN RIC