In this paper a novel design of microstrip fed L-shaped arm slot and notch loaded RMPA (Rectangular Microstrip Patch Antenna) with mended ground plane for wide bandwidth is presented. The proposed prototype antenna is fabricated on an FR-4 (Fire retardant) substrate with dimension 30 × 30.8 mm 2 and 1.6 mm thickness. The proposed design is analyzed and simulated using high frequency structure simulator (HFSS) tool version 15. The analysed results are validated through fabrication and measurement results. The analyzed result shows 96.1% maximum radiation efficiency at 2.9 GHz whereas overall efficiency is more than 85% over the entire frequency range, and experiment achieves gain 8.4 dB at 7 GHz. The designed antenna achieves 119.39% impedance bandwidth with more than 5 dB gain over the operating frequency range of 2.41 GHz to 9.55 GHz. For better performance and analysis of proposed antenna, a parametric study has been carried out to analyze the effects of variations in the following-slot and notch dimensions loaded on the patch as well as variations in ground length. The designed antenna can be utilized for various applications incorporating Bluetooth, WLAN, Wi-Max, and UWB operation.
In this paper, a novel Evolutionary Computing named Adaptive Genetic Algorithm (AGA) based ANN model is developed for rectangular MPA (Microstrip patch antenna). Considering at-hand and Nextgeneration Ultra wideband application demands, the emphasis has been made on retaining optimal lowcost design with desired cut-off frequency. The proposed method employs multiple sets of theoreticallydriven training instances or patch antenna design parameters which have been processed for normalization and sub-sampling to achieve a justifiable and reliable sample size for further design parameter prediction. Procedurally, the input design parameters were processed for normalization followed by sub-sampling to give rise to a sufficient set of inputs to perform knowledge-driven (designparameter) prediction. Considering limitations of the major at-hand machine learning methods which often undergo local minima and convergence while training, we designed a state-of-art new Adaptive Genetic Algorithm based neuro-computing model (AGA-ANN), which helped to predict the set of optimal design parameters for rectangular microstrip patch antenna. The predicted patch antenna length and width values were later used for verification which achieved the expected frequency. The depth analysis revealed that a rectangular patch antenna with width 14.78 mm, length 11.08mm, feed-line 50 Ω can achieve the cut-off frequency of 8.273 GHz, which can be of great significance for numerous UWB applications.
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