Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks

Yan Liu, Zhiyuan Jiang, Shunqing Zhang, Shugong Xu

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

23 Citations (Scopus)

Abstract

Ultra-Reliable and Low-Latency Communications (URLLC) services in vehicular networks on millimeter-wave bands present a significant challenge, considering the necessity of constantly adjusting the beam directions. Conventional methods are mostly based on classical control theory, e.g., Kalman filter and its variations, which mainly deal with stationary scenarios. Therefore, severe application limitations exist, especially with complicated, dynamic Vehicle-to-Everything (V2X) channels. This paper gives a thorough study of this subject, by first modifying the classical approaches, e.g., Extended Kalman Filter (EKF) and Particle Filter (PF), for non-stationary scenarios, and then proposing a Reinforcement Learning (RL)-based approach that can achieve the URLLC requirements in a typical intersection scenario. Simulation results based on a commercial ray-tracing simulator show that enhanced EKF and PF methods achieve packet delay more than 10 ms, whereas the proposed deep RL-based method can reduce the latency to about 6 ms, by extracting context information from the training data.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728150895
DOIs
Publication statusPublished - Jun 2020
Externally publishedYes
Event2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland
Duration: 7 Jun 202011 Jun 2020

Publication series

NameIEEE International Conference on Communications
Volume2020-June
ISSN (Print)1550-3607

Conference

Conference2020 IEEE International Conference on Communications, ICC 2020
Country/TerritoryIreland
CityDublin
Period7/06/2011/06/20

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