# Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration
## 5 Conclusions  
The synthesis of antenna arrays employs a spectrum of techniques, ranging from intricate analytical methods to iterative numerical methods driven by optimization algorithms. However, a common limitation of these techniques is their predominant focus on  
Figure 14: SOM- input planes.  
Figure 13: SOM- neighbor weight distances and sample hits.  
the array factor, while often neglecting the intricate interactions among array elements and real-time operational constraints. This oversight can introduce inaccuracies in the resulting radiation pattern, prompting the need to incorporate the physical relationships between array feeding parameters and the resulting radiation patterns in order to enhance precision. Due to the inherently nonlinear behavior of antenna arrays, attempting to address these complexities using traditional methodologies poses significant challenges and is frequently disregarded. In contrast, leveraging neural network-based solutions offers a promising avenue by establishing connections between desired radiation patterns and key feeding parameters, such as voltage and spacing, within the real antenna array. This approach facilitates the transition from a conventional antenna array to a smart array, while bypassing the complexities associated with conventional methods. This paper provides an overview of various neural network applications in smart antenna array synthesis, underscoring their potential to address the limitations of traditional techniques and improve the accuracy and efficiency of antenna array synthesis processes.  
In conclusion, our research demonstrates the effectiveness of combining the Taguchi method with neural networks for accurate synthesis of antenna radiation patterns. Validation against real-world data confirms the reliability and practical applicability of our approach, opening avenues for further refinement and exploration.  
In our future work, in addition to exploring phase and amplitude synthesis using this Taguchi-neural network method, we plan to delve deeper into experimentation with various learning algorithms. We aim to compare the effectiveness of these algorithms against our current approach in order to identify the one that provides the best performance in terms of accuracy and generalization of antenna radiation pattern models. This approach will help us to gain a better understanding of the strengths and limitations of each algorithm and select the most suitable one for our specific application.  
Conceptualization, R.K. and R.G.; methodology, R.G., W.A. and R.K.; software, R.G., A.S., A.H. and R.K.; Data collection, A.S.; validation, R.G; investigation, A.H.; writing--original draft preparation, M.I.W.; writing--review and editing, J.F. and L.L.; supervision, L.L., A.S., R.G. and I.E.G.; project administration, L.L. All authors have read and agreed to the published version of the manuscript.  
The authors express their gratitude to HANALab ENSI and the University of Manouba for their support.  
The data presented in this study are available on request from the corresponding author.  
A heartfelt acknowledgment to all researchers who have contributed to this work.  
The authors declare no conflicts of interest  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SOM & Self-Organizing Maps (SOMs) \\ PSO & Particle Swarm Optimization \\ FEM & Finite Element Method \\ RF & Radio Frequency \\ BMU & Best-Matching Unit \\ IoT & Internet of Things \\ DACs & Digital-to-Analog Converters \\ ADCs & Analog-to-Digital Converters \\ SNR & Signal-to-Noise Ratio \\ MSE & Mean Squared Error \\ OA & Orthogonal Array \\ FDTD & Finite-Difference Time Domain \\ \end{tabular}