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A Deep Q-Learning Bisection Approach for Power Allocation in Downlink NOMA Systems

Marie-Josepha Youssef 1, 2 Charbel Abdel Nour 1, 2 Xavier Lagrange 3, 4 Catherine Douillard 1, 2 
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
4 ADOPNET - Advanced technologies for operated networks
UR1 - Université de Rennes 1, IMT Atlantique - IMT Atlantique, IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES
Abstract : In this work, we study the weighted sum-rate maximization problem for a downlink non-orthogonal multiple access (NOMA) system. With power and data-rate constraints, this problem is generally non-convex. Therefore, a novel solution based on the deep reinforcement learning (DRL) framework is proposed for the power allocation problem. While previous work based on DRL restrict the solution to a limited set of possible power levels, the proposed DRL framework is specifically designed to find a solution with a much larger granularity, emulating a continuous power allocation. Simulation results show that the proposed power allocation method outperforms two baseline algorithms. Moreover, it achieves almost 85% of the weighted sum-rate obtained by a far more complex genetic algorithm that approaches exhaustive search in terms of performance.
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Contributor : Charbel Abdel Nour Connect in order to contact the contributor
Submitted on : Wednesday, February 9, 2022 - 5:02:00 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
Long-term archiving on: : Tuesday, May 10, 2022 - 7:10:50 PM


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Marie-Josepha Youssef, Charbel Abdel Nour, Xavier Lagrange, Catherine Douillard. A Deep Q-Learning Bisection Approach for Power Allocation in Downlink NOMA Systems. IEEE Communications Letters, Institute of Electrical and Electronics Engineers, 2021, 26 (2), pp.316-320. ⟨10.1109/LCOMM.2021.3130102⟩. ⟨hal-03448296⟩



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