Learning to Navigate Connected Autonomous Cars for Long-Term Communication Coverage

Zheyu Chen; Chi Harold Liu; Runan Wang

doi:10.1109/MITP.2018.2876923

Learning to Navigate Connected Autonomous Cars for Long-Term Communication Coverage

Zheyu Chen, Chi Harold Liu, Runan Wang

School of Computer Science and Technology

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

Autonomous cars employed as mobile base stations could provide communication networks in network-congested areas. In this paper, the authors leverage emerging deep reinforcement learning (DRL) techniques for enabling autonomous cars control and present a novel and highly effective DRL-based control framework called DRL-C3.

Original language	English
Article number	8617769
Pages (from-to)	46-53
Number of pages	8
Journal	IT Professional
Volume	20
Issue number	6
DOIs	https://doi.org/10.1109/MITP.2018.2876923
Publication status	Published - 1 Nov 2018

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10.1109/MITP.2018.2876923

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title = "Learning to Navigate Connected Autonomous Cars for Long-Term Communication Coverage",

abstract = "Autonomous cars employed as mobile base stations could provide communication networks in network-congested areas. In this paper, the authors leverage emerging deep reinforcement learning (DRL) techniques for enabling autonomous cars control and present a novel and highly effective DRL-based control framework called DRL-C3.",

author = "Zheyu Chen and Liu, \{Chi Harold\} and Runan Wang",

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N2 - Autonomous cars employed as mobile base stations could provide communication networks in network-congested areas. In this paper, the authors leverage emerging deep reinforcement learning (DRL) techniques for enabling autonomous cars control and present a novel and highly effective DRL-based control framework called DRL-C3.

AB - Autonomous cars employed as mobile base stations could provide communication networks in network-congested areas. In this paper, the authors leverage emerging deep reinforcement learning (DRL) techniques for enabling autonomous cars control and present a novel and highly effective DRL-based control framework called DRL-C3.

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Learning to Navigate Connected Autonomous Cars for Long-Term Communication Coverage

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