Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study
Fouzi Boukhalfa, Reda Alami, Mastane Achab, Eric Moulines, Mehdi, Bennis

TL;DR
This study benchmarks deep reinforcement learning algorithms for V2X communication, demonstrating their effectiveness in improving redundancy, reducing costs, and enhancing reliability in vehicle communication systems.
Contribution
It introduces a DRL-based approach to optimize vertical handover decisions in V2X, addressing the complex coordination of multiple communication technologies.
Findings
DRL algorithms outperform existing methods in V2X handover tasks.
Significant reduction in communication costs achieved.
Enhanced reliability through better V2X technology selection.
Abstract
In today's era, autonomous vehicles demand a safety level on par with aircraft. Taking a cue from the aerospace industry, which relies on redundancy to achieve high reliability, the automotive sector can also leverage this concept by building redundancy in V2X (Vehicle-to-Everything) technologies. Given the current lack of reliable V2X technologies, this idea is particularly promising. By deploying multiple RATs (Radio Access Technologies) in parallel, the ongoing debate over the standard technology for future vehicles can be put to rest. However, coordinating multiple communication technologies is a complex task due to dynamic, time-varying channels and varying traffic conditions. This paper addresses the vertical handover problem in V2X using Deep Reinforcement Learning (DRL) algorithms. The goal is to assist vehicles in selecting the most appropriate V2X technology (DSRC/V-VLC) in a…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Software-Defined Networks and 5G · Age of Information Optimization
