Islam, Amirul and Thomos, Nikolaos and Musavian, Leila (2023) Multi-Agent Deep Reinforcement Learning for Spectral Efficiency Optimization in Vehicular Optical Camera Communications. IEEE Transactions on Mobile Computing, 23 (5). pp. 3666-3679. DOI https://doi.org/10.1109/tmc.2023.3278277
Islam, Amirul and Thomos, Nikolaos and Musavian, Leila (2023) Multi-Agent Deep Reinforcement Learning for Spectral Efficiency Optimization in Vehicular Optical Camera Communications. IEEE Transactions on Mobile Computing, 23 (5). pp. 3666-3679. DOI https://doi.org/10.1109/tmc.2023.3278277
Islam, Amirul and Thomos, Nikolaos and Musavian, Leila (2023) Multi-Agent Deep Reinforcement Learning for Spectral Efficiency Optimization in Vehicular Optical Camera Communications. IEEE Transactions on Mobile Computing, 23 (5). pp. 3666-3679. DOI https://doi.org/10.1109/tmc.2023.3278277
Abstract
In this paper, we propose a vehicular optical camera communication system that can meet low bit error rate (BER) and ultra-low latency constraints. First, we formulate a sum spectral efficiency optimization problem that aims at finding the speed of vehicles and the modulation order that maximizes the sum spectral efficiency subject to reliability and latency constraints. This problem is mixed-integer programming with nonlinear constraints, and even for a small set of modulation orders, is NP-hard. To overcome the entailed high computational and time complexity which prevents its solution with traditional methods, we first model the optimization problem as a partially observable Markov decision process. We then solve it using an independent Q-learning framework, where each vehicle acts as an independent agent. Since the state-action space is large we then adopt deep reinforcement learning (DRL) to solve it efficiently. As the problem is constrained, we employ the Lagrange relaxation approach prior to solving it using the DRL framework. Simulation results demonstrate that the proposed DRL-based optimization scheme can effectively learn how to maximize the sum spectral efficiency while satisfying the BER and ultra-low latency constraints. The evaluation further shows that our scheme can achieve superior performance compared to radio frequency-based vehicular communication systems and other vehicular OCC variants of our scheme.
Item Type: | Article |
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Uncontrolled Keywords: | deep reinforcement learning; Lagrangian relaxation; low latency; optical camera communication; spectral efficiency maximization; Vehicular communication |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 25 May 2023 11:48 |
Last Modified: | 30 Oct 2024 20:50 |
URI: | http://repository.essex.ac.uk/id/eprint/35674 |
Available files
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