Maximizing energy efficiency and system throughput using a self-adaptive penalty function with a whale optimization algorithm in social-aware device-to-device communications

Abstract

Device-to-device (D2D) communication is an emerging approach for 5 G networks. It allows for high capacity, low latency, and increased bandwidth. These features have prompted several research studies to focus on meeting customers’ expectations. This research concentrates on obtaining optimal resource and power allocation, better quality of service, and reliable connectivity between D2D clients through a whale optimization algorithm (WOA). The WOA possesses greater stability, faster integration speeds, a powerful global search ability, and greater accuracy than traditional methods. The WOA utilizes an unconstrained objective function obtained through a self-adaptive penalty function. This unconstrained objective function yields optimal solutions for the resource as well as power allocation, higher throughputs, and energy and spectral efficiencies in D2D communications. For D2D communications, the energy efficiency of the WOA is 47.12 % higher than a genetic algorithm (GA) and 70.33 % higher than particle swarm optimization (PSO), and it yields high spectral efficiency and system throughput. Obviously, the WOA in this proposal is more efficient than a GA or PSO and shows the advantages of combining physical and social parameters for improved wireless network performance.

Keywords

• D2D communication
• Energy efficiency
• GA
• PSO
• SAPF
• Social-aware