# A Multi-Armed Bandit Framework for Online Optimisation in Green Integrated Terrestrial and Non-Terrestrial Networks

arXiv: https://arxiv.org/abs/2506.09268 · HTML: https://mlsanity.com/paper/2506.09268/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning](https://mlsanity.com/paper/2101.11246/) (2021)
- [Machine Learning and Analytical Power Consumption Models for 5G Base Stations](https://mlsanity.com/paper/2209.11600/) (2022)
- [Distributed Pricing-Based User Association for Downlink Heterogeneous Cellular Networks](https://mlsanity.com/paper/1407.4694/) (2014)
- [Throughput and Coverage Trade-Off in Integrated Terrestrial and Non-Terrestrial Networks: an Optimization Framework](https://mlsanity.com/paper/2310.02002/) (2023)
- [UAV Communications in Integrated Terrestrial and Non-terrestrial Networks](https://mlsanity.com/paper/2208.02683/) (2022)

## Abstract (arXiv)

Integrated terrestrial and non-terrestrial network (TN-NTN) architectures offer a promising solution for expanding coverage and improving capacity for the network. While non-terrestrial networks (NTNs) are primarily exploited for these specific reasons, their role in alleviating terrestrial network (TN) load and enabling energy-efficient operation has received comparatively less attention. In light of growing concerns associated with the densification of terrestrial deployments, this work aims to explore the potential of NTNs in supporting a more sustainable network. In this paper, we propose a novel online optimisation framework for integrated TN-NTN architectures, built on a multi-armed bandit (MAB) formulation and leveraging the Bandit-feedback Constrained Online Mirror Descent (BCOMD) algorithm. Our approach adaptively optimises key system parameters--including bandwidth allocation, user equipment (UE) association, and macro base station (MBS) shutdown--to balance network capacity and energy efficiency in real time. Extensive system-level simulations over a 24-hour period show that our framework significantly reduces the proportion of unsatisfied UEs during peak hours and achieves up to 19% throughput gains and 5% energy savings in low-traffic periods, outperforming standard network settings following 3GPP recommendations.