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Deep Q-Learning-based Resource Allocation in NOMA Visible Light Communications
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  • Ahmed Al Hammadi ,
  • Lina Bariah ,
  • Sami Muhaidat ,
  • Mahmoud Al-Qutayri ,
  • Paschalis C. Sofotasios ,
  • Merouane Debbah
Ahmed Al Hammadi
Khalifa University

Corresponding Author:[email protected]

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Lina Bariah
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Sami Muhaidat
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Mahmoud Al-Qutayri
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Paschalis C. Sofotasios
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Merouane Debbah
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Abstract

Visible light communication (VLC) has been introduced as a key enabler for high-data rate wireless services in future wireless communication networks. In addition to this, it was also demonstrated recently that non-orthogonal multiple access (NOMA) can further improve the spectral efficiency of multi-user VLC systems. In this context and owing to the significantly promising potential of artificial intelligence in wireless communications, the present contribution proposes a deep Q-learning (DQL) framework that aims to optimize the performance of an indoor NOMA-VLC downlink network. In particular, we formulate a joint power allocation and LED transmission angle tuning optimization problem, in order to maximize the average sum rate and the average energy efficiency. The obtained results demonstrate that our algorithm offers a noticeable performance enhancement into the NOMA-VLC systems in terms of average sum rate and average energy efficiency, while maintaining the minimum convergence time, particularly for higher number of users. Furthermore, considering a realistic downlink VLC network setup, the simulation results have shown that our algorithm outperforms the genetic algorithm (GA) and the differential evolution (DE) algorithm in terms of average sum rate, and offers considerably less run-time complexity.
2022Published in IEEE Open Journal of the Communications Society volume 3 on pages 2284-2297. 10.1109/OJCOMS.2022.3219014