Intelligent optimization of handover control parameters for beyond 5G networks using fuzzy logic and differential evolution algorithm

Abstract Beyond 5G (B5G), wireless networks incorporate technologies like millimeter-wave (mmWave) and ultra-dense networks (UDNs). These technologies are intended to meet the growing demand for higher data rates, reduced latency, and greater connectivity. Nevertheless, the deployment of these technologies has raised many challenges. Ensuring an effective handover is one of the most significant challenges, as providing a smooth connection and uninterrupted service is necessary. Conventional handover processes do not work well under dynamic network environments. This leads to frequent handovers and handover failures (HOFs). To overcome this, this paper proposes a new optimization method of Fuzzy Logic-Differential Evolution Algorithm (FL-DEA). This strategy will maximize two important Handover Control Parameters (HCPs): Handover Margin (HOM) and Time to Trigger (TTT). FL-DEA combines the uncertainty-processing capability of fuzzy logic and the global optimization capability of the Differential Evolution (DE) Algorithm. The fuzzy logic controller estimates HOF and Handover Ping-Pong (HOPP) probabilities using user speed, HOM, and TTT. These preliminary estimates are then optimized by the DE algorithm, which minimizes a cost function that aims to minimize both HOF and HOPP probabilities. The given method is compared with the current HCP optimization methods, such as Particle Swarm Optimization (PSO) and Fuzzy Logic Controller. According to simulation results, FL-DEA performs better with the least value of handover failures, minimal the ping-pong effect, and improved Quality of Service (QoS) in dynamic B5G wireless networks.