Metaheuristic-Based Resource Management in URLLC-Enabled D2D Communications in Beyond 5G Networks

The increasing demand for ultra-fast connectivity and multitasking has prompted the need for an adaptive and intelligent resource management strategy in beyond fifth-generation (B5G) networks. Conventional methods often overlook mobility-induced user stratification and spatial interference in ultra-reliable low latency communication (URLLC)-enabled Device-to-Device (D2D) underlay communications. To address these challenges, we investigate a multi-objective optimization problem that minimizes latency while maximizing D2D throughput with low power consumption. In this paper, we propose a performance-aware heuristic adaptive power optimization algorithm (PHAPOA) that integrates a customized ant colony approach for adaptive power transmission with particle swarm of pheromone rates and objective weights to achieve efficient resource utilization. Simulation results show that the PHAPOA algorithm achieves superior system performance, with a 14.13% improvement in throughput and a 33.93% reduction in latency compared to the benchmark schemes. The proposed algorithm may be suitable for applications such as emergency services, automated vehicles, and industrial automation systems.