Joint User Association and Bandwidth Assignment for Digital Twin-Assisted Multi-RAT Networks

In this paper, we investigate user equipment (UE)-radio access technology (RAT) association and bandwidth assignment to maximize sum-rates in a multi-RAT network. To this end, we formulate an optimization problem that jointly addresses UE association and bandwidth allocation, adhering to practical constraints. Because of the NP-hard nature of this problem, finding a globally optimal solution is computationally infeasible. To address this challenge, we propose a centralized and computationally efficient heuristic algorithm that aims to maximize sumrates while enhancing quality of service (QoS). Yet, the proposed approach requires global channel state information (CSI) for near-optimal performance, which incurs substantial overhead and data collection costs in large-scale multi-RAT networks. To alleviate this burden, we use a digital twin (DT) of the multi-RAT network, leveraging its context-awareness to acquire global CSI with reduced overhead. Our numerical results reveal that our approach improves sum-rates by up to 43 % over baseline method, with less than a 5 % deviation from the theoretical optimal solution, while achieving up to a 43 % improvement in QoS. Further analysis reveals that our method not only surpasses the optimal solution in terms of QoS enhancement, but also ensures significant computational efficiency.

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Publication Details

Published
2025-06-08
DOI
https://doi.org/10.1109/icc52391.2025.11161528
Primary Topic
Software-Defined Networks and 5G
Type
article
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article

Joint User Association and Bandwidth Assignment for Digital Twin-Assisted Multi-RAT Networks

Manobendu Sarker, Georges Kaddoum, Zoheb Hassan
Software-Defined Networks and 5G
article

Joint User Association and Bandwidth Assignment for Digital Twin-Assisted Multi-RAT Networks

Manobendu Sarker, Georges Kaddoum, Zoheb Hassan
article en

Abstract

In this paper, we investigate user equipment (UE)-radio access technology (RAT) association and bandwidth assignment to maximize sum-rates in a multi-RAT network. To this end, we formulate an optimization problem that jointly addresses UE association and bandwidth allocation, adhering to practical constraints. Because of the NP-hard nature of this problem, finding a globally optimal solution is computationally infeasible. To address this challenge, we propose a centralized and computationally efficient heuristic algorithm that aims to maximize sumrates while enhancing quality of service (QoS). Yet, the proposed approach requires global channel state information (CSI) for near-optimal performance, which incurs substantial overhead and data collection costs in large-scale multi-RAT networks. To alleviate this burden, we use a digital twin (DT) of the multi-RAT network, leveraging its context-awareness to acquire global CSI with reduced overhead. Our numerical results reveal that our approach improves sum-rates by up to 43 % over baseline method, with less than a 5 % deviation from the theoretical optimal solution, while achieving up to a 43 % improvement in QoS. Further analysis reveals that our method not only surpasses the optimal solution in terms of QoS enhancement, but also ensures significant computational efficiency.

Xuzhou Construction Machinery Group (China) (CN), Université Laval (CA)
Openalex Percentile: Top 69%
Software-Defined Networks and 5G
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