Puncturing-Based Resource Allocation for URLLC and eMBB Services via Matching Theory and Unsupervised Deep Learning

• Published in: IEEE transactions on vehicular technology 2024-09, Vol.73 (9), p.13396-13411
• Main Authors: Shi, Bing, She, Changyang, Zheng, Fu-Chun, Gao, Lin, Li, Ge
• Format: Article
• Language: English
• Subjects: Algorithms; Bandwidth; Broadband; Deep learning; eMBB; Heuristic algorithms; Machine learning; matching theory; Mobile computing; Network latency; Network reliability; Packets (communication); Phase matching; puncturing; Quality of service; Reliability; Resource allocation; Resource management; Supervised learning; Throughput; Ultra reliable low latency communication; Unsupervised learning; URLLC; Wireless networks
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2024.3394716

The coexistence of Ultra-Reliable and Low-Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB) brings significant challenges for service pairing and resource allocation in beyond fifth-generation (B5G) wireless networks. To meet the reliability requirement of URLLC services and improve the fairness of eMBB services, we first develop a supervised learning-based resource allocation policy for eMBB services. Then, a two-phase resource allocation framework is proposed for URLLC services: 1) eMBB/URLLC service pairing and 2) URLLC resource allocation . In the first phase, matching theory pairs eMBB and URLLC services for better fairness. In the second phase, URLLC resource allocation policy is optimized by a constrained unsupervised learning algorithm. Simulation results show that our proposed framework can achieve better trade-offs among fairness, throughput, and reliability compared with two existing baselines. For example, a dynamic proportional fairness algorithm can meet the reliability requirement of URLLC when its average packet arrival rate is below 0.7 packets/mini-slot. The proposed algorithm can support URLLC services with an average packet arrival rate of 1.6 packets/mini-slot.