Islam, Amirul and Thomos, Nikolaos and Musavian, Leila (2024) Deep Reinforcement Learning Based Ultra Reliable and Low Latency Vehicular OCC. IEEE Transactions on Communications. p. 1. DOI https://doi.org/10.1109/TCOMM.2024.3478108
Islam, Amirul and Thomos, Nikolaos and Musavian, Leila (2024) Deep Reinforcement Learning Based Ultra Reliable and Low Latency Vehicular OCC. IEEE Transactions on Communications. p. 1. DOI https://doi.org/10.1109/TCOMM.2024.3478108
Islam, Amirul and Thomos, Nikolaos and Musavian, Leila (2024) Deep Reinforcement Learning Based Ultra Reliable and Low Latency Vehicular OCC. IEEE Transactions on Communications. p. 1. DOI https://doi.org/10.1109/TCOMM.2024.3478108
Abstract
In this paper, we present a deep reinforcement learning (DRL) framework for vehicular optical camera communication (OCC) systems that ensures ultra-reliable and low-latency communication (uRLLC). We first formulate a throughput maximization problem that aims at optimizing speed of vehicles, channel code rate, and modulation order while respecting the uRLLC requirements. We model reliability by satisfying a target bit error rate and latency as transmission latency. To improve the transmission rate and provide high reliability and low latency, our scheme uses low-density parity-check codes and adaptive modulation. We then solve the optimization problem using the actor-critic-based DRL scheme with Wolpertinger framework. We employ a deep deterministic policy gradient algorithm to operate over continuous action spaces. The evaluation confirms that our proposed DRL-based optimization scheme achieves superior performance compared to radio frequency-based communication systems as well as variants of the proposed scheme. Finally, we verify through simulations that our proposed solution can maximize the communication rate while meeting the uRLLC constraints.
Item Type: | Article |
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Uncontrolled Keywords: | DRL; vehicular OCC; uRLLC; LDPC codes; actor-critic; DDPG |
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: | 16 Oct 2024 15:03 |
Last Modified: | 16 Oct 2024 15:03 |
URI: | http://repository.essex.ac.uk/id/eprint/39420 |
Available files
Filename: Final Author version.pdf
Licence: Creative Commons: Attribution 4.0