Vehicular Ad-hoc Network (VANET) is a standard protocol for wireless vehicular communication that enables Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications. VANET safety applications aim to prevent traffic accidents and require a high Packet Delivery Ratio (PDR) and low latency of safety packet broadcast. When a large number of vehicles simultaneously access a limited channel resource for the safety broadcast, the safety requirements impose more challenges; the communication performance will significantly degrade due to network congestion. Especially, infrastructureless VANETs, which only allow V2V communication, vehicles are supposed to overcome the congestion problem using a self-adaptation scheme without the aid of infrastructures. In this paper, we propose a self-adaptive MAC layer algorithm employing Deep Q Network (DQN) with a novel contention information-based state representation to improve the performance of the V2V safety packet broadcast. The proposed algorithm operates a fully distributed manner, and it is evaluated by simulations considering various levels of traffic congestion.
Multiple Channel Access using Deep Reinforcement Learning for Congested Vehicular Networks
01.05.2020
199389 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Multiple Access in Cellular V2X: Performance Analysis in Highly Congested Vehicular Networks
ArXiv | 2018
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