Constrained risk-sensitive deep reinforcement learning for eMBB-URLLC joint scheduling

Constrained risk-sensitive deep reinforcement learning for eMBB-URLLC joint scheduling

In this work, we employ a constrained risk-sensitive deep reinforcement learning (CRS-DRL) approach for joint scheduling in a dynamic multiplexing scenario involving enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC). Our scheduling policy minimizes the adverse im...

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Bibliographic Details
Main Authors: Wenheng Zhang, Mahsa Derakhshani, Gan Zheng, Sangarapillai Lambotharan
Format: Default Article
Published: 2024
Subjects:
Engineering
Communications engineering
Electrical engineering
Distributed computing and systems software
eMBB
deep reinforcement learning
punctured scheduling
resource allocation
risk-sensitive
URLLC
Online Access:https://hdl.handle.net/2134/25368532.v1
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https://hdl.handle.net/2134/25368532.v1

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