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Machine Learning to Improve Multi-hop Searching and Extended Wireless Reachability in V2X

Morocho-Cayamcela, Manuel Eugenio, Lee, Haeyoung and Lim, Wansu (2020) Machine Learning to Improve Multi-hop Searching and Extended Wireless Reachability in V2X IEEE Communications Letters.

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Abstract

Multi-hop relay selection is a critical issue in vehicle to everything networks. In previous works, the optimal hopping strategy is assumed to be based on the shortest distance. This study proposes a hopping strategy based on the lowest propagation loss, considering the effect of the environment. We use a twostep machine learning routine: improved deep encoder-decoder architecture to generate environmental maps and Q-learning to search for the multi-hopping path with the lowest propagation loss. Simulation results show that our proposed method can improve environmental recognition and extend the reachability of multi-hop communications by up to 66.7%, compared with a shortest-distance selection.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Morocho-Cayamcela, Manuel Eugenio
Lee, Haeyounghaeyoung.lee@surrey.ac.uk
Lim, Wansu
Date : 20 March 2020
Copyright Disclaimer : © 2019 IEEE.
Uncontrolled Keywords : Machine learning, multi-hop wireless communication, Q-learning, vehicle-to-everything.
Depositing User : James Marshall
Date Deposited : 20 Mar 2020 11:48
Last Modified : 20 Mar 2020 11:48
URI: http://epubs.surrey.ac.uk/id/eprint/854056

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