Enhanced 5G Radio Resource Allocation Mechanism Using Game Theory and Firefly Algorithm

The 5G-PPP defined new requirements for different services, which include Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and Massive Machine Type Communication. Network slicing technology addresses these diverse requirements by creating multiple logical networks on a generic physical infrastructure, ensuring efficient and cost-effective deployment. This paper focuses on 5G radio resource allocation in the Radio Access Network to satisfy eMBB and URLLC slice requirements. We propose an allocation scheme that adapts to the mobility of cellular vehicles (URLLC), ensuring low latency requirements while maximizing eMBB’s high-speed data capacity. We employ the Deferred Acceptance Method from game theory for high-quality frequency resource allocation and a modified Firefly Algorithm for optimizing URLLC communication tasks. Simulation results demonstrate that our approach improved URLLC packet transmission target achievement, average time of URLLC transmission tasks, number of remaining high-quality frequency resources, and eMBB packets utilization.