Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement Learning

• Published in: International journal of computational intelligence systems Vol. 17; no. 1; pp. 1 - 13
• Main Authors: Li, Xinmin, Zhang, Xuhao, Li, Jiahui, Luo, Feiying, Huang, Yi, Zhang, Xiaoqiang
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 03.06.2024; Springer
• Subjects: Artificial Intelligence; Blocklength allocation; Computational Intelligence; Control; Deep reinforcement learning; Engineering; Mathematical Logic and Foundations; Mechatronics; Power control; Research Article; Robotics; UAV; URLLC; Deep reinforcement learning; URLLC; Blocklength allocation; UAV; Power control
• ISSN: 1875-6883; 1875-6883
• DOI: 10.1007/s44196-024-00530-8

Integration of unmanned aerial vehicles (UAVs) with ultra-reliable and low-latency communication (URLLC) systems can improve the real-time communication performance for various industrial internet of things (IIoT) applications. Designing an intelligent resource allocation system is one of the challenges posed by an energy-constrained UAV communication system. Therefore, we formulate a sum rate maximization problem, subject to the UAVs’ energy by optimizing the blocklength allocation and the power control jointly in the uplink UAV-assisted URLLC systems, in which the probabilistic channel model between UAV and users is adopted. The problem is difficult to solve due to the non-convex objective function and the energy constraints, and also challenging to make fast decision in the complex communication environment. Thus, we propose a deep reinforcement learning (DRL)-based scheme to optimize the blocklength allocation and power control jointly. First, transform the original problem into the multi-agent reinforcement learning process, where each subcarrier is regarded as the agent that optimizes its individual blocklength allocation and power control. Then, each agent makes the intelligent decision to obtain the maximum reward value depending on the weighted segmented reward function, which is related to the UAV energy consumption and user rates to improve the rate performance. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes and has the stable convergence in different settings, such as the learning rate, the error probability, the subcarrier spacing, and the number of users.