A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching in 6G HAPS Networks

TL;DR

This paper proposes a lightweight Q-learning based cell-switching method for HAPS networks in 6G, optimizing energy efficiency while respecting user delay requirements.

Contribution

It introduces a novel, simple Q-learning algorithm tailored for delay-aware cell-switching in HAPS-based 6G networks, enhancing energy efficiency and QoS.

Findings

The proposed algorithm reduces energy consumption under various interference scenarios.

It effectively balances delay requirements with energy savings.

Simulation results confirm the algorithm's efficacy in different network conditions.

Abstract

This study investigates the integration of a high altitude platform station (HAPS), a non-terrestrial network (NTN) node, into the cell-switching paradigm for energy saving. By doing so, the sustainability and ubiquitous connectivity targets can be achieved. Besides, a delay-aware approach is also adopted, where the delay profiles of users are respected in such a way that we attempt to meet the latency requirements of users with a best-effort strategy. To this end, a novel, simple, and lightweight Q-learning algorithm is designed to address the cell-switching optimization problem. During the simulation campaigns, different interference scenarios and delay situations between base stations are examined in terms of energy consumption and quality-of-service (QoS), and the results confirm the efficacy of the proposed Q-learning algorithm.