# Taguchi Method-Based Synthesis of a Circular Antenna Array for Enhanced IoT Applications
## 3 Conclusions and Future Work  
In this paper, we present the synthesis of circular planar antenna arrays using the Taguchi method, a robust optimization approach for complex systems. The method focuses on aligning the actual radiation pattern of the antenna array with a user-specified desired pattern, improving the overall performance of the antenna system. The Taguchi method was applied to synthesize both circular arrays and concentric ring arrays consisting of three and four rings, where each ring has a varying number of elements, and the radius of the rings is adjusted based on the inter-element distance and the total number of sources. This flexibility enables the design of antennas tailored to specific operational requirements. One of the method's key advantages is its computational efficiency, with calculations typically taking only a few seconds, enabling real-time adjustments and making it ideal for iterative design processes. Using this technique, we optimized radiation patterns by calculating the weights for different antenna configurations. The Taguchi method was also used to synthesize directive lobes, focusing on key objectives such as reducing side lobes, improving the main lobe gain, and precisely directing the lobes by controlling radiation pattern levels in each direction with appropriate weighting coefficients for amplitude and/or phase. The technique was rigorously tested across various antenna array types, exploring multiple scenarios to synthesize directive lobes that cover the angular range from \(-90^{\circ}\) to \(90^{\circ}\), essential for applications requiring precise directionality and signal integrity. Simulations using CST Microwave Studio were conducted to validate the effectiveness of  
Figure 21: Synthesis of 3D radiation patterns for a 36-element array (\(N_{1}=6\), \(N_{2}=8\), \(N_{3}=10\), and \(N_{4}=12\)) at 2.45 GHz. (**a**) Structure of the concentric ring array (\(N_{1}=6\), \(N_{2}=8\), \(N_{3}=10\), and \(N_{4}=12\)). (**b**) Uniform excitations. (**c**) Excitations with Taguchi optimization.  
the Taguchi method, and the results confirmed that the synthesized designs met or exceeded the expected operational standards, demonstrating the method's reliability and practical effectiveness. Future work will focus on incorporating neural network models and artificial intelligence (AI) techniques to enhance the synthesis process, automate optimization, and refine designs in real-time applications. By combining the Taguchi method with AI, we aim to develop adaptive antenna configurations that can dynamically adjust to environmental conditions and operational demands, improving the performance and versatility of antenna systems for next-generation communication networks.  
Conceptualization, W.A. and R.G.; methodology, W.A.; software, R.G., A.S. and R.K.; validation, I.E.G. and J.F.; formal analysis, A.S.; investigation, R.G.; resources, I.E.G.; data curation, W.A.; writing--original draft preparation, R.G.; writing--review and editing, R.G.; visualization, A.S.; supervision, R.G.; project administration, R.G. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research work was funded by Umm Al Qura University, Saudi Arabia under grant number: 25 UQU4361156 GSSR01.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors extend their appreciation to Umm Al-Qura University, Saudi Arabia for funding this research work through Grant number:25 UQU4361156 GSSR01.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.