Full Text:
<11>
CLC number:
On-line Access: 2025-10-13
Received: 2025-03-18
Revision Accepted: 2025-07-22
Crosschecked: 0000-00-00
Cited: 0
Clicked: 25
Uplink puncturing for mixed URLLC and eMBB services in 5G-based IWNs: a model-aided DRL method
| |||||||||||||
Jingfang DING1, Meng ZHENG1, Haibin YU2,3, Yitian WANG2,3,4, Chi XU2,3. Uplink puncturing for mixed URLLC and eMBB services in 5G-based IWNs: a model-aided DRL method[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@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 YU2,3, Yitian WANG2,3,4, Chi XU2,3",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2500173"
}
%0 Journal Article
%T Uplink puncturing for mixed URLLC and eMBB services in
5G-based IWNs: a model-aided DRL method
%A Jingfang DING1
%A Meng ZHENG1
%A Haibin YU2
%A 3
%A Yitian WANG2
%A 3
%A 4
%A Chi XU2
%A 3
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2500173
TY - JOUR
T1 - Uplink puncturing for mixed URLLC and eMBB services in
5G-based IWNs: a model-aided DRL method
A1 - Jingfang DING1
A1 - Meng ZHENG1
A1 - Haibin YU2
A1 - 3
A1 - Yitian WANG2
A1 - 3
A1 - 4
A1 - Chi XU2
A1 - 3
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2500173
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.
Open peer comments: Debate/Discuss/Question/Opinion
<1>