TY - GEN
T1 - Joint Computation Offloading and Multi-User Scheduling for Max-Latency Minimization in MEC-Aided Cell-Free Networks
AU - Sun, Yanzan
AU - Jiang, Xueyang
AU - Pan, Guangjin
AU - Zhang, Shunqing
AU - Xu, Shugong
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Combining cell-free networks and mobile edge computing (MEC) is expected to meet the low latency and multi-user access requirements of the Industrial Internet of Things (IIoT). Firstly, this paper considers MEC-aided cell-free networks in multi-user access scenarios, our goal is minimizing the maximum user latency. Secondly, in order to mitigate the impact of both multi-user interference and pilot contamination on system performance, we divide the frequency band into multiple sub-channels and dynamically schedule users on sub-channels in the uplink. Subsequently, we jointly optimize computation offloading, multi-scheduling and computational resources allocation, resulting in a complex non-convex optimization problem. Finally, To address this problem, we introduce a Bisection Search Feasibility (BSF)-Proximal Policy Optimization (PPO) algorithm, PPO algorithm is utilized to determine user scheduling strategy, and then the remaining non-convex problem is converted into a convex opti-mization problem using BSF algorithm. The simulation results demonstrate that the suggested algorithm achieves rapid convergence and surpasses the performance of baseline approaches.
AB - Combining cell-free networks and mobile edge computing (MEC) is expected to meet the low latency and multi-user access requirements of the Industrial Internet of Things (IIoT). Firstly, this paper considers MEC-aided cell-free networks in multi-user access scenarios, our goal is minimizing the maximum user latency. Secondly, in order to mitigate the impact of both multi-user interference and pilot contamination on system performance, we divide the frequency band into multiple sub-channels and dynamically schedule users on sub-channels in the uplink. Subsequently, we jointly optimize computation offloading, multi-scheduling and computational resources allocation, resulting in a complex non-convex optimization problem. Finally, To address this problem, we introduce a Bisection Search Feasibility (BSF)-Proximal Policy Optimization (PPO) algorithm, PPO algorithm is utilized to determine user scheduling strategy, and then the remaining non-convex problem is converted into a convex opti-mization problem using BSF algorithm. The simulation results demonstrate that the suggested algorithm achieves rapid convergence and surpasses the performance of baseline approaches.
KW - cell-free networks
KW - min-max latency
KW - Mobile edge computing
KW - multi-user scheduling
KW - pilot contamination
UR - http://www.scopus.com/inward/record.url?scp=85217542329&partnerID=8YFLogxK
U2 - 10.1109/WCSP62071.2024.10827524
DO - 10.1109/WCSP62071.2024.10827524
M3 - Conference Proceeding
AN - SCOPUS:85217542329
T3 - 16th International Conference on Wireless Communications and Signal Processing, WCSP 2024
SP - 703
EP - 708
BT - 16th International Conference on Wireless Communications and Signal Processing, WCSP 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Conference on Wireless Communications and Signal Processing, WCSP 2024
Y2 - 24 October 2024 through 26 October 2024
ER -