JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

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Uplink puncturing for mixed URLLC and eMBB services in 5G-based IWNs: a model-aided DRL method

Author(s): Jingfang DING¹, Meng ZHENG¹, Haibin YU^2, ³, Yitian WANG^2, ^3, ⁴, Chi XU^2, ³
Affiliation(s): ¹School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China ²State Key Laboratory of Robotics, Chinese Academy of Sciences, Shenyang 110016, China ³Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China ⁴University of Chinese Academy of Sciences, Beijing 100049, China
Corresponding email(s): dingjingfang@cse.neu.edu.cn, zhengmeng@cse.neu.edu.cn, yhb@sia.cn
Key Words: Uplink 5G networks; eMBB; URLLC; Resource slicing; Puncturing; Deep reinforcement learning

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Jingfang DING¹, Meng ZHENG¹, Haibin YU^2,³, Yitian WANG^2,^3,⁴, Chi XU^2,³. Uplink puncturing for mixed URLLC and eMBB services in 5G-based IWNs: a model-aided DRL method[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2500173

@article{title="Uplink puncturing for mixed URLLC and eMBB services in 5G-based IWNs: a model-aided DRL method",
author="Jingfang DING¹, Meng ZHENG¹, Haibin YU^2,³, Yitian WANG^2,^3,⁴, Chi XU^2,³",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2500173"
}

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%T Uplink puncturing for mixed URLLC and eMBB services in 5G-based IWNs: a model-aided DRL method
%A Jingfang DING¹
%A Meng ZHENG¹
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doi="https://doi.org/10.1631/FITEE.2500173"

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A1 - Yitian WANG^{2
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doi="https://doi.org/10.1631/FITEE.2500173"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: The coexistence of Ultra-Reliable Low-Latency Communication (URLLC) and Enhanced Mobile Broad band (eMBB) services in 5th Generation (5G)-based Industrial Wireless Networks (IWNs) poses significant resource slicing challenges due to their inherent performance requirement conflicts. To address this challenge, this paper proposes a puncturing method that utilizes a model-aided Deep Reinforcement Learning (DRL) algorithm for URLLC over eMBB services in uplink 5G networks. First, a puncturing-based optimization problem is formulated to maximize the accumulated eMBB rate under strict URLLC latency and reliability constraints. Next, we design a Random Repetition Coding-based Contention (RRCC) scheme for sporadic URLLC traffic and derive its analytical reliability model. To jointly optimize the scheduling parameters of URLLC and eMBB, a DRL solution based on the reliability model is developed, which is capable of dynamically adapting to changing environments. The accelerated convergence of the model-aided DRL algorithm is demonstrated using simulations, and the superiority in resource effciency of the proposed method over existing approaches is validated.

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