IET Communications, Volume (19), No (1), Year (2025-2)

Title : ( Deep multi‐agent RL for anti‐jamming and inter‐cell interference mitigation in NOMA networks )

Authors: Sina Yousefzadeh Marandi , Mohammad Ali Amirabadi , Mohammad Hossein Kahaei , S. Mohammad Razavizadeh ,

Citation: BibTeX | EndNote

Abstract

Inter-cell interference and smart jammer attacks significantly impair the performance of non-orthogonal multiple access (NOMA) networks. This issue is particularly critical when considering strategic interactions with malicious actors. To address this challenge, the power allocation problem is framed in a two-cell NOMA network as a sequential game. In this game, each base station acts as a leader, choosing a power allocation strategy, while the smart jammer acts as a follower, reacting optimally to the base stations’ choices. To address this multi-agent scenario, four multi-agent reinforcement learning algorithms are proposed: Q-learning based unselfish (QLU), deep QLU, hot booting deep QLU, and decreased state deep QLU. A game-theoretic analysis that demonstrates the algorithms’ convergence to the optimal network-wide strategy with high probability is provided. Simulation results further confirm the superiority of our proposed algorithms compared to the Q-learning-based selfish NOMA power allocation method.

Keywords

NOMA
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@article{paperid:1102109,
author = {سینا یوسف زاده مرندی and Amirabadi, Mohammad Ali and محمد حسین کاهائی and سید محمد رضوی زاده},
title = {Deep multi‐agent RL for anti‐jamming and inter‐cell interference mitigation in NOMA networks},
journal = {IET Communications},
year = {2025},
volume = {19},
number = {1},
month = {February},
issn = {1751-8628},
keywords = {NOMA},
}

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%0 Journal Article
%T Deep multi‐agent RL for anti‐jamming and inter‐cell interference mitigation in NOMA networks
%A سینا یوسف زاده مرندی
%A Amirabadi, Mohammad Ali
%A محمد حسین کاهائی
%A سید محمد رضوی زاده
%J IET Communications
%@ 1751-8628
%D 2025

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