Communication Scheduling by Deep Reinforcement Learning for Remote Traffic State Estimation With Bayesian Inference-Reference-Cited by-同舟云学术

Communication Scheduling by Deep Reinforcement Learning for Remote Traffic State Estimation With Bayesian Inference

Published:2022-04 Issue:4 Volume:71 Page:4287-4300
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Peng Bile¹^ORCID,Xie Yuhang¹,Seco-Granados Gonzalo²^ORCID,Wymeersch Henk³^ORCID,Jorswieck Eduard A.¹^ORCID

Affiliation:

1. Institute for Communications Technology, TU Braunschweig, Braunschweig, Lower Saxony, Germany

2. Department of Telecommunications and Systems Engineering, School of Engineering, Universitat Autònoma de Barcelona (UAB), Barcelona, Bellaterra, Spain

3. Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Bohuslän, Sweden

Funder

Bundesministerium für Bildung und Forschung

6G Research and Innovation Cluster 6G-RIC

Ministerio de Ciencia e Innovación

Catalan ICREA Academia Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/9765825/09693175.pdf?arnumber=9693175

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1. Proximal policy optimization algorithms;schulman,2017

2. The Semantic Communication Game

3. Semantics-Empowered Communication for Networked Intelligent Systems

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

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