Joint beamforming for internet of vehicles via IBKA-driven intelligent reflecting surface optimization

The deployment of terahertz communications in high-dynamic 6G vehicular networks faces two major challenges: severe link blockage in non-line-of-sight scenarios and the high computational load of beamforming. To address these issues, this paper introduces an intelligent reflecting surface (IRS) into the system to create reliable secondary links through dynamic phase reconfiguration. We propose a joint beamforming scheme in which the base station (BS) beamforming is computed using the zero-forcing algorithm, while the IRS phase shifts are optimized using the Improved Black-winged Kite Algorithm (IBKA). The IBKA is specifically designed to overcome the original algorithm’s limitations, including low population diversity and local optima convergence. Key improvements include the use of Circle chaotic mapping for population initialization, the incorporation of an adaptive T-distribution perturbation spiral strategy during the attack phase, and the application of a Golden Sine Strategy (GSS) during the migration phase. Simulation results validate the efficacy of our framework. Compared to the benchmark Genetic Algorithm (GA), the proposed IBKA framework significantly reduces the computational burden by approximately 45% (in terms of Number of Function Evaluations), while incurring only a marginal 1.55% sacrifice in the system sum-rate, effectively enabling a real-time optimal performance-complexity trade-off for 6G vehicular networks.