Design and implementation of a fractal microstrip patch array for vehicle-to-vehicle communication using meta-heuristic techniques

• Published in: Journal of computational electronics Vol. 24; no. 4; p. 110
• Main Authors: Karanam, Raghavendra, Kakkar, Deepti
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2025; Springer Nature B.V
• Subjects: Algorithms; Ant colony optimization; Antenna arrays; Antennas; Bandwidths; Communications systems; Computer centers; Data transmission; Electrical Engineering; Engineering; Evolutionary algorithms; Fractal geometry; Fractals; Genetic algorithms; Geometry; Heuristic methods; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mechanical Engineering; Optical and Electronic Materials; Optimization techniques; Patch antennas; Roads & highways; Theoretical; Vehicles; Virtual private networks; Wireless communication systems; mm wave; Vehicle-to-vehicle application; Fractal microstrip antenna; Evolutionary algorithm; Swarm intelligence approach
• ISSN: 1569-8025; 1572-8137
• DOI: 10.1007/s10825-025-02355-w

A new novel designed 1 × 2 patch array antenna, which is fractal geometry based, is optimized using the genetic ant colony optimization (GACO) hybrid evolutionary algorithm for better V2V communication. Portions of a genetic algorithm (GA), ant colony optimization (ACO), and GACO optimization focus on a fitness evaluation process, where GACO determines the most optimal antenna dimensions. First, one patch antenna is designed with an estimated gain of 13 dB and a bandwidth of 4500 MHz. Then, an array factor is estimated with the wave number as 2π/λ and φ = 0, followed by the development of a uniformly spaced 1 × 2 patch array. The array configuration brings about a higher gain of 14 dB and a greater bandwidth of 5200 MHz, thus providing high signal reliability for V2V communications. The results claim supporting evidence for the effectiveness of GACO in antenna parameter optimization and suggest using GACO in high-performance antenna for next-generation wireless communication systems. After simulating and testing, it was noticed that the GACO method provided better dimension optimizations, thereby enhancing the antenna's characteristics in terms of S11 and gain with a faster convergence rate compared with GA and ACO.