Latency Fairness for MEC-Enabled Cell-Free Massive MIMO: ICA- and AI-Based Approaches
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| Názov: | Latency Fairness for MEC-Enabled Cell-Free Massive MIMO: ICA- and AI-Based Approaches |
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| Autori: | Hieu V. Nguyen, Van-Phuc Bui, Mai T. P. Le, Vien Nguyen-Duy-Nhat, Hung Nguyen-Le, Nghi H. Tran |
| Zdroj: | Nguyen, H V, Bui, V-P, Le, M T P, Nguyen-Duy-Nhat, V, Nguyen-Le, H & Tran, N H 2025, 'Latency Fairness for MEC-Enabled Cell-free massive MIMO : ICA-and AI-based Approaches', IEEE Communications Letters, vol. 29, no. 8, pp. 1963-1967. https://doi.org/10.1109/LCOMM.2025.3581730 |
| Informácie o vydavateľovi: | Institute of Electrical and Electronics Engineers (IEEE), 2025. |
| Rok vydania: | 2025 |
| Predmety: | Optimization, Artificial intelligence, Resource management, Cell-free, Scalability, resource allocation, Fading channels, Servers, Vectors, Convolutional neural networks, obile edge computing, ICA, mobile edge computing, Massive MIMO, CNN, Data communication, latency |
| Popis: | This letter investigates the latency minimization at the network edge in mobile edge computing (MEC)-enabled Cell-Free massive MIMO systems. We introduce a new edge computing model that integrates both task offloading and local execution. To minimize overall system latency while considering power allocation constraints, we formulate an optimization problem aimed at reducing maximum computing time. This mixed-integer non-convex problem is then reformulated into a more tractable form, which is solved using an iterative convex approximation method to achieve locally-optimal solutions. Additionally, we propose a convolutional neural network-based algorithm as an alternative solution to further improve system efficiency. Numerical results are provided to validate the theoretical framework and demonstrate the effectiveness of the proposed approaches in accelerating the data processing in MEC-enabled cell-free networks. |
| Druh dokumentu: | Article |
| Popis súboru: | application/pdf |
| ISSN: | 2373-7891 1089-7798 |
| DOI: | 10.1109/lcomm.2025.3581730 |
| Prístupová URL adresa: | https://vbn.aau.dk/ws/files/785419171/Accepted_Author_Manuscript.pdf https://doi.org/10.1109/LCOMM.2025.3581730 http://www.scopus.com/inward/record.url?scp=105009015316&partnerID=8YFLogxK https://vbn.aau.dk/da/publications/9695873e-34dd-4a89-bc91-26e107f9926e |
| Rights: | IEEE Copyright |
| Prístupové číslo: | edsair.doi.dedup.....46961ebd1cbee3e1ede75b9291a9c62a |
| Databáza: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | This letter investigates the latency minimization at the network edge in mobile edge computing (MEC)-enabled Cell-Free massive MIMO systems. We introduce a new edge computing model that integrates both task offloading and local execution. To minimize overall system latency while considering power allocation constraints, we formulate an optimization problem aimed at reducing maximum computing time. This mixed-integer non-convex problem is then reformulated into a more tractable form, which is solved using an iterative convex approximation method to achieve locally-optimal solutions. Additionally, we propose a convolutional neural network-based algorithm as an alternative solution to further improve system efficiency. Numerical results are provided to validate the theoretical framework and demonstrate the effectiveness of the proposed approaches in accelerating the data processing in MEC-enabled cell-free networks. |
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| ISSN: | 23737891 10897798 |
| DOI: | 10.1109/lcomm.2025.3581730 |