Federated reinforcement learning for scheduling-offloading policies in multi-cluster NOMA systems
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| Publicado en: | EURASIP Journal on Advances in Signal Processing vol. 2025, no. 1 (Dec 2025), p. 37 |
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| Otros Autores: | , |
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Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1186/s13634-025-01242-7 |2 doi | |
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| 045 | 2 | |b d20251201 |b d20251231 | |
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| 100 | 1 | |a Djemai, Ibrahim |u Télécom SudParis, Institut Polytechnique de Paris, SAMOVAR, Palaiseau, France (GRID:grid.508893.f) | |
| 245 | 1 | |a Federated reinforcement learning for scheduling-offloading policies in multi-cluster NOMA systems | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Intelligent scheduling and resource allocation of user equipments (UEs) in wireless networks has been an ongoing topic of research. The innovation in this field focuses mostly on generalizing the system to include more components, as well as deriving new ways to solve the problem. We address in this paper an unexplored case of the scheduling-offloading problem for a wireless network with mobile edge computing (MEC). In this network, the UEs have mobility models and are transmitting using non-orthogonal multiple access (NOMA). They are also equipped with data buffers and batteries with energy harvesting (EH) capabilities. We propose a novel UEs clustering approach to account for the growing NOMA inter-user interference, which can lead to performance issues especially in the downlink decoding phase. In addition, clustering can help reduce the problem complexity by distributing it among the clusters that operate independently. We investigate deep reinforcement learning (DRL) to devise efficient policies that minimize the packet loss due to delay infringements. Moreover, we use federated learning (FL) to learn a unified policy accounting for the dynamic nature of clusters. Our simulation results based on DRL method, namely the proximal policy optimization (PPO), and standard methods, show the effectiveness of using learning-based algorithms in terms of minimizing the packet loss and the energy consumption. | |
| 653 | |a Energy harvesting | ||
| 653 | |a Wireless networks | ||
| 653 | |a Deep learning | ||
| 653 | |a Decoding | ||
| 653 | |a Bandwidths | ||
| 653 | |a Optimization | ||
| 653 | |a Edge computing | ||
| 653 | |a Resource allocation | ||
| 653 | |a Mobile computing | ||
| 653 | |a Energy | ||
| 653 | |a Spectrum allocation | ||
| 653 | |a Machine learning | ||
| 653 | |a Internet of Things | ||
| 653 | |a Policies | ||
| 653 | |a Scheduling | ||
| 653 | |a Digital twins | ||
| 653 | |a Clustering | ||
| 653 | |a Decision making | ||
| 653 | |a Computation offloading | ||
| 653 | |a Medical equipment | ||
| 653 | |a Algorithms | ||
| 653 | |a Federated learning | ||
| 653 | |a Energy consumption | ||
| 653 | |a Markov analysis | ||
| 653 | |a Nonorthogonal multiple access | ||
| 700 | 1 | |a Sarkiss, Mireille |u Télécom SudParis, Institut Polytechnique de Paris, SAMOVAR, Palaiseau, France (GRID:grid.508893.f) | |
| 700 | 1 | |a Ciblat, Philippe |u Télécom Paris, Institut Polytechnique de Paris, LTCI, Palaiseau, France (GRID:grid.508893.f) | |
| 773 | 0 | |t EURASIP Journal on Advances in Signal Processing |g vol. 2025, no. 1 (Dec 2025), p. 37 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244968086/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3244968086/fulltext/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244968086/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |