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

TL;DR

This paper introduces a combinatorial bandit approach for activating sleep modes in 5G small cells, optimizing energy efficiency while maintaining quality of service through dynamic load balancing.

Contribution

It develops a novel CUCB-based algorithm for joint sleep mode activation and load balancing, outperforming existing strategies in energy efficiency and adaptability.

Findings

CUCB algorithm outperforms naive and state-of-the-art RL methods

Effective load balancing maintains QoS while reducing energy consumption

Algorithm suitable for implementation in O-RAN near-real-time RIC xApps

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.