Opportunistic Resource Allocation for URLLC and eMBB in 5G Networks with Time Varying Channels: a Genetic Algorithm Approach

The fifth-generation (5G) New Radio (NR) introduces stringent delay and reliability requirements to support diverse services, including enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC). A major challenge is the efficient coexistence of these heterogeneous services, as eMBB demands high throughput while URLLC requires extreme reliability and minimal latency. This study investigates the coexistence of eMBB and URLLC under time-varying channel conditions and formulates a many-to-many URLLC resource allocation problem. To address this, we propose an opportunistic resource allocation scheme based on a genetic algorithm (GA) that dynamically optimizes resource block (RB) allocation for both services. The GA employs a heuristic fitness function designed to maximize eMBB fairness and throughput while ensuring URLLC reliability. Simulation results demonstrate that the proposed approach significantly improves overall system performance: eMBB fairness exceeds 95%, and the average eMBB data rate increases by approximately 500 Kbps compared to random allocation. Moreover, URLLC users maintain a consistent data rate of 600 Kbps, outperforming benchmark methods while satisfying the 99.999% reliability requirement. The results confirm that the proposed GA-based approach effectively balances throughput, fairness, and reliability, making it a promising solution for future 5G networks with mixed traffic demands.