Article: Subcarrier power control for URLLC communication system via multi-agent deep reinforcement learning in IoT network Journal: International Journal of Communication Networks and Distributed Systems (IJCNDS) 2024 Vol.30 No.3 pp.374 - 392 Abstract: Designing an intelligent resource allocation scheme to achieve the performance requirements of internet of things (IoT) devices for the future ultra-reliable low-latency communication (URLLC) network is a challenging task. In this paper, we formulate a joint blocklength allocation and power control optimisation problem to maximise the sum-rate performance with the short data packet in an uplink URLLC communication system. To alleviate this non-convex optimisation problem under the subcarrier power, blocklength and rate constraints, we firstly transfer it into a multi-agent reinforcement learning (RL) problem, in which each subcarrier works as the agent to decide its own power intelligently. Then a distributed blocklength allocation and power control scheme is proposed based on deep Q-network (DQN). To improve the rate performance in the dynamic communication environment, we design the segmented reward function depending on the communication rate and blocklength under different conditions, and adopt the experience replay strategy to avoid the dependency of training data. Finally, the simulation results show that the proposed scheme achieve the effectiveness and convergence under different settings compared to benchmark schemes. Inderscience Publishers - linking academia, business and industry through research

Title: Subcarrier power control for URLLC communication system via multi-agent deep reinforcement learning in IoT network

Authors: Haiyan Wang; Xinmin Li; Feiying Luo; Jiahui Li; Xiaoqiang Zhang

Addresses: School of Internet of Things and Intelligent Engineering, Jiangsu Vocational Institute of Commerce, Nanjing, China ' Key Laboratory of Medicinal and Edible Plant Resources Development of Sichuan Education Department, Chengdu University, Chengdu, China; Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong, Shenzhen, China ' CEC Jinjiang Information Industry Co., Ltd., Chengdu, China; School of Information Engineering, Southwest University of Science and Technology, Mianyang, China ' School of Information Engineering, Southwest University of Science and Technology, Mianyang, China ' School of Information Engineering, Southwest University of Science and Technology, Mianyang, China

Abstract: Designing an intelligent resource allocation scheme to achieve the performance requirements of internet of things (IoT) devices for the future ultra-reliable low-latency communication (URLLC) network is a challenging task. In this paper, we formulate a joint blocklength allocation and power control optimisation problem to maximise the sum-rate performance with the short data packet in an uplink URLLC communication system. To alleviate this non-convex optimisation problem under the subcarrier power, blocklength and rate constraints, we firstly transfer it into a multi-agent reinforcement learning (RL) problem, in which each subcarrier works as the agent to decide its own power intelligently. Then a distributed blocklength allocation and power control scheme is proposed based on deep Q-network (DQN). To improve the rate performance in the dynamic communication environment, we design the segmented reward function depending on the communication rate and blocklength under different conditions, and adopt the experience replay strategy to avoid the dependency of training data. Finally, the simulation results show that the proposed scheme achieve the effectiveness and convergence under different settings compared to benchmark schemes.

Keywords: ultra-reliable low-latency communication; URLLC; blocklength allocation; power control; deep reinforcement learning.

DOI: 10.1504/IJCNDS.2024.138252

International Journal of Communication Networks and Distributed Systems, 2024 Vol.30 No.3, pp.374 - 392

Received: 04 Sep 2023
Accepted: 17 Nov 2023
Published online: 30 Apr 2024 *

Title: Subcarrier power control for URLLC communication system via multi-agent deep reinforcement learning in IoT network

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