# Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration
## 2 Phased Antenna Arrays  
Phased antenna arrays are intricate systems comprising multiple antenna elements, each capable of independent phase and amplitude adjustments in the signals they transmit or receive [28; 29; 30]. These adjustments enable the array to sculpt the radiation pattern of emitted waves or enhance reception sensitivity. By precisely coordinating these adjustments, the array achieves beamforming, directing a focused beam in specific directions without the need for physical repositioning. This capability proves invaluable in radar systems for swift target tracking and wide-area surveillance [31; 32]. In communication networks, phased arrays facilitate adaptive beam steering, optimizing signal reception in dynamic and challenging environments. Despite the complexity inherent in managing numerous elements and ensuring their coordinated actions, ongoing advancements in technology continue to refine these arrays, making them more efficient, adaptable, and pivotal across diverse domains, including telecommunications, space exploration, and defense systems [33; 34]. In the world of digital systems, the transition from conventional analog beamforming to digital processes involves intricate signal manipulation. This manipulation occurs at the baseband level, where precise adjustments to signal amplitude and phase are made digitally to align with the desired excitation pattern [35; 36]. This departure from traditional analog beamforming involves specialized signal processing components. Digital systems have the potential to generate a multitude of beams, designated as \(N\), by merging signals from multiple sources [37]. The actual number of formed beams relies on the RF characteristics of \(M\) transmission channels and the computational capabilities of the signal processing modules [38]. Critical components such as digital-to-analog converters (DACs), analog-to-digital converters (ADCs), and signal processing modules play pivotal roles in this process. These components require exceptional speed, surpassing that of their analog counterparts, in order to efficiently process digital data and precisely shape analog signals [39]. This rapid processing capability serves as the bedrock of digital beamforming systems, enabling efficient signal manipulation within the digital realm, forming an essential aspect of their operational efficiency [40]. In fully digital beamforming systems, there is a direct relationship between the number of amplifiers, such as Power Amplifiers (PAs) and Low-Noise Amplifiers (LNAs), and the transmission channels. This connection categorizes these systems as active antenna array architectures [41]. Notably, these arrays possess reconfigurable properties, allowing for dynamic modifications during beam formation. This adaptability in altering beams designates these active antenna arrays as reconfigurable antenna arrays, providing flexibility and adaptiveness across diverse communication scenarios [42; 43]. In this study, the optimization methodology we introduce was implemented using Matlab scripts, providing a flexible and powerful platform for algorithm development and execution. Moreover, to enhance the user experience and facilitate a deeper understanding \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Efficiency** & **Complexity** & **Precision** \\ \hline Taylor & Fast & Moderate & High \\ PSO & Good & High & Variable \\ Analytical & Fast & Low & High \\ Genetic Algorithm & Moderate & Moderate & High \\ FEM & Fast & Moderate & High \\ FDTD & Fast & Moderate & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of antenna array radiation pattern synthesis methods [24; 25; 26; 27]. of the optimization process, we leveraged Matlab's Integrated Development Environment (IDE) to provide real-time visual feedback on the convergence of the optimization [44, 45].