An Intelligent Coexistence Strategy for eMBB/URLLC Traffic in Multi-UAV Relay Networks via Deep Reinforcement Learning

• Published in: IEEE transactions on wireless communications Vol. 23; no. 10; pp. 13424 - 13439
• Main Authors: Tian, Mengqiu, Li, Changle, Hui, Yilong, Chen, Binbin, Yue, Wenwei, Fu, Yuchuan, Han, Zhu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous aerial vehicles; Consumption; Customization; Decomposition; Deep learning; deep reinforcement learning; Delays; eMBB/URLLC multiplexing; Fluctuations; Load distribution (forces); Machine learning; Multiplexing; Network latency; Optimization; personalized fluctuations; Preempting; Relay networks; Relaying; Resource management; UAV-relay networks; Ultra reliable low latency communication; Unmanned aerial vehicles
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2024.3401163

Preemptive scheduling efficiently addresses the coexistence of enhanced Mobile Broad Band (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC). While URLLC puncturing influences eMBB performance, further investigation is necessary to study the trade-offs between stability, delay, and efficiency. However, existing studies overlook the imbalance in eMBB/URLLC load distribution and personalized fluctuations in eMBB performance, leading to sub-optimal results. To tackle this, we propose an unmanned aerial vehicle (UAV) relay-assisted eMBB/URLLC multiplexing framework. Specifically, considering the utilization of UAVs for connecting separated next-generation Node Bs (gNBs) and the individual subject experience of services, we first formulate the multiplexing problem as an optimization problem. The objective is to maximize eMBB throughput and minimize personalized fluctuations in eMBB performance and UAV consumption, subject to URLLC constraints. Then, the challenging problem is decomposed into the eMBB problem and the URLLC problem. For the former, we further decompose it into three sub-problems and solve them using optimization methods. For the latter, we propose a deep reinforcement learning-based algorithm to obtain an optimal strategy for relaying and puncturing URLLC into eMBB intelligently. Simulation results demonstrate that our proposals outperform benchmark schemes regarding eMBB throughput, UAV consumption, eMBB performance fluctuation, URLLC satisfaction, and learning efficiency.