@inproceedings{99a966775b5a4fbcab0714ba4802fe59,
title = "Joint Power Allocation and LED Transmission Angle Tuning in NOMA-Enabled VLC Networks",
abstract = "Visible light communication (VLC) has been envisaged as a key enabler for high-data rate wireless services in future wireless communication networks. On the other hand, it was also demonstrated recently that non-orthogonal multiple access (NOMA) can further improve the spectral efficiency of multiuser VLC systems. In this context, we propose an efficient deep Q-learning based algorithm that maximizes the average sum rate of the downlink in the NOMA-VLC network. This is achieved by jointly optimizing the power allocation and the transmission angle of the LEDs. The obtained results demonstrate that our algorithm offers a noticeable performance enhancement in terms of average sum rate, while maintaining the minimum convergence time.",
keywords = "Deep reinforcement learning, Multiple access, Resource allocation, Sum-rate, Visible light communications",
author = "Hammadi, \{Ahmed Al\} and Lina Bariah and Sami Muhaidat and Mahmoud Al-Qutayri and Paschalis Sofotasios and Merouane Debbah",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 1st International Conference on 6G Networking, 6GNet 2022 ; Conference date: 06-07-2022 Through 08-07-2022",
year = "2022",
doi = "10.1109/6GNet54646.2022.9830384",
language = "British English",
series = "2022 1st International Conference on 6G Networking, 6GNet 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 1st International Conference on 6G Networking, 6GNet 2022",
address = "United States",
}