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

arXiv: https://arxiv.org/abs/2402.13096 · HTML: https://mlsanity.com/paper/2402.13096/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [Cell Switching in HAPS-Aided Networking: How the Obscurity of Traffic Loads Affects the Decision](https://mlsanity.com/paper/2405.00387/) (2024)

## Abstract (arXiv)

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.