Constrained Risk-Sensitive Deep Reinforcement Learning for eMBB-URLLC Joint Scheduling-Reference-Cited by-同舟云学术

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Constrained Risk-Sensitive Deep Reinforcement Learning for eMBB-URLLC Joint Scheduling

Published:2024-09 Issue:9 Volume:23 Page:10608-10624
ISSN:1536-1276
Container-title:IEEE Transactions on Wireless Communications
language:
Short-container-title:IEEE Trans. Wireless Commun.

Author:

Zhang Wenheng¹^ORCID,Derakhshani Mahsa¹^ORCID,Zheng Gan²^ORCID,Lambotharan Sangarapillai³^ORCID

Affiliation:

1. Signal Processing and Networks Research Group, Wolfson School, Loughborough University, Loughborough, U.K.

2. School of Engineering, University of Warwick, Coventry, U.K.

3. Institute for Digital Technologies, Loughborough University, London, U.K.

Funder

U.K. Engineering and Physical Sciences Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7693/10678775/10471327.pdf?arnumber=10471327

Reference36 articles.

1. Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC)

2. An Industrial Perspective on Wireless Sensor Networks — A Survey of Requirements, Protocols, and Challenges

3. Ultra-Reliable and Low-Latency Communications in 5G Downlink: Physical Layer Aspects

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