Deep Q-learning for reducing enhanced distributed channel access collision in IEEE 802.11p of Vehicular Ad Hoc Network

Yongjun Liang, Ismail Fauzi Bin Isnin, Nur Haliza Abdul Wahab, Jiafu Su

Article ID: 9494
Vol 8, Issue 15, 2024

VIEWS - 548 (Abstract)

Abstract


The purpose of Vehicular Ad Hoc Network (VANET) is to provide users with better information services through effective communication. For this purpose, IEEE 802.11p proposes a protocol standard based on enhanced distributed channel access (EDCA) contention. In this standard, the backoff algorithm randomly adopts a lower bound of the contention window (CW) that is always fixed at zero. The problem that arises is that in severe network congestion, the backoff process will choose a smaller value to start backoff, thereby increasing conflicts and congestion. The objective of this paper is to solve this unbalanced backoff interval problem in saturation vehicles and this paper proposes a method that is a deep neural network Q-learning-based channel access algorithm (DQL-CSCA), which adjusts backoff with a deep neural network Q-learning algorithm according to vehicle density. Network simulation is conducted using NS3, the proposed algorithm is compared with the CSCA algorithm. The find is that DQL-CSCA can better reduce EDCA collisions.


Keywords


Vehicular Ad Hoc Network; enhanced distributed channel access; packet collision; deep neural network Q-learning

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References


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DOI: https://doi.org/10.24294/jipd9494

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