NOMA-CSK Integrated VLC System with Reinforcement Learning-Based Multi-Objective Power Allocation
Serkan Vela, Gokce Hacioglu

TL;DR
This paper proposes a novel NOMA-CSK VLC system integrated with a reinforcement learning-based power allocation strategy, significantly improving spectral efficiency, fairness, and throughput in indoor visible light communication environments.
Contribution
It introduces the first NOMA-CSK VLC system design combined with an RL-based power allocation method that adapts dynamically to channel conditions and outperforms traditional approaches.
Findings
RL-based power allocation outperforms traditional methods
Achieves higher throughput and fairness
Maintains performance in unseen environments
Abstract
This paper introduces a novel framework that synergistically combines Non-Orthogonal Multiple Access (NOMA) with Color Shift Keying (CSK) modulation to substantially boost spectral efficiency in Visible Light Communication (VLC) systems. A key challenge in the proposed NOMA-CSK architecture is managing the complex power allocation process, especially under cross-color interference caused by spectral overlap among LEDs and the limitations of optical filters. To overcome this, we develop an intelligent power allocation strategy powered by a Soft Actor-Critic (SAC) reinforcement learning agent. Trained in a simulated indoor environment, the SAC agent dynamically distributes power among users with diverse channel conditions while balancing multiple performance objectives. Simulation results show that our SAC-based method significantly outperforms traditional approaches such as Gain Ratio…
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Taxonomy
TopicsOptical Wireless Communication Technologies · Advanced Wireless Communication Technologies · Retinal and Optic Conditions
