# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
###### Abstract
Overall, the integration of the [PERSON] method with neural networks empowers antenna arrays with unparalleled levels of performance, adaptability, and versatility, driving transformative advancements in wireless communication, radar systems, and beyond. The applications of integrating the Taylor method with neural networks in antenna design are extensive and span various domains. Wireless communication and mobile networks: In cellular networks and wireless communication systems, optimizing antenna radiation patterns is crucial to maximizing coverage, capacity, and signal quality. Integrating the [PERSON] method with neural networks enables the dynamic adaptation of radiation patterns to varying channel conditions, ensuring optimal network performance [8; 9]. Radars: In radar systems, the precision and reliability of radar data largely depend on the antenna's ability to form directional beams and suppress unwanted echoes [10; 11; 12]. The joint use of the Taylor method and neural networks optimizes radiation patterns for precise target detection and localization, even in noisy environments. Remote sensing and surveillance: In applications such as environmental monitoring, natural resource management, and traffic surveillance, antennas must detect and track objects accurately over large areas [13; 14]. Integrating the Taylor method with neural networks enables the design of adaptive antennas capable of meeting the specific needs of each surveillance application. Smart antenna systems: Smart antenna systems, which use adaptive algorithms to optimize antenna performance based on channel conditions, greatly benefit from integrating the Taylor method with neural networks. These systems can dynamically adapt to changes in the RF environment, ensuring reliable connectivity and high-quality service delivery in various scenarios [15; 16; 17]. The integration of the Taylor method with neural networks opens up new perspectives in antenna design, offering optimized performance and increased adaptability across a wide range of applications, from wireless communication networks to radar systems to environmental surveillance. Table 1 compares different beamforming methods for antenna arrays, each with specific advantages and disadvantages. The Taylor method adjusts antenna phases using Taylor co  
efficients, offering easy implementation but requiring prior knowledge of the spatial signal distribution [18]. The Dolph-Chebyshev method [19] minimizes amplitude error through Chebyshev polynomials, achieving high sidelobe attenuation, though its complexity increases in certain antenna configurations. Particle swarm optimization (PSO) [20] adjusts parameters by simulating the collective behavior of animals, ensuring quick convergence, but its effectiveness depends on the initial setup. Genetic algorithms (GAs) [21] optimize parameters by simulating natural selection mechanisms, making them well suited to complex designs, although they require significant computation times. Therefore, the choice of method depends on performance goals and implementation constraints. This hybrid integration strategy provides a structured design pipeline that not only accelerates convergence but also enhances the physical interpretability of the learned model, distinguishing it from purely data-driven or purely analytical approaches.