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| Auteurs principaux: | , , , |
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| Format: | Preprint |
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2025
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| Accès en ligne: | https://arxiv.org/abs/2506.09268 |
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| _version_ | 1866912424593981440 |
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| author | Alam, Henri de Domenico, Antonio Salem, Tareq Si Kaltenberger, Florian |
| author_facet | Alam, Henri de Domenico, Antonio Salem, Tareq Si Kaltenberger, Florian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09268 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Armed Bandit Framework for Online Optimisation in Green Integrated Terrestrial and Non-Terrestrial Networks Alam, Henri de Domenico, Antonio Salem, Tareq Si Kaltenberger, Florian Networking and Internet Architecture Artificial Intelligence 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. |
| title | A Multi-Armed Bandit Framework for Online Optimisation in Green Integrated Terrestrial and Non-Terrestrial Networks |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2506.09268 |