Design and Machine Learning based Optimization of 5G Sub-Band Antenna
Keywords:
Decision Tree, High Frequency Structural Simulator, Machine Learning Algorithm, Microstrip Patch Antenna, Root Mean Square Error.Abstract
Use of machine learning techniques in today's science and engineering domains, including electromagnetic applications is inevitable. This article communicates the introduction of supervised Decision Tree (DT) Machine Learning (ML) algorithm for the optimization of a microstrip patch antenna at 5G-n78 sub-band frequency 3.3GHz. The proposed antenna is a rectangular patch with dimensions of 0.304λ0 x 0.238 λ0 designed using the line feeding technique over FR-4 substrate (ɛr = 4.4) with thickness of 1.6mm and 0.002 loss tangent. The antenna has a return loss of -37.4dB, 1.03 VSWR, and gain of 4.06 dBi at 3.3GHz operating frequency. Application of supervised decision tree technique on the proposed antenna is discussed and dimensions from DT ML techniques are used for fabrication purpose. High Frequency Structural Simulator (HFSS) is used for proposed antenna design and data collection, further ML model is designed in python language by choosing return loss of the proposed antenna as target variable. The Root Mean Square error is calculated for train and test data which is 4.42 & 4.44 respectively. Simulated and measured return loss values from ML model and fabrication are in good agreement. The proposed ML model is capable to optimize the proposed 5G –n78 sub band antenna dimensions which are suitable for wireless WLAN & WiMAX applications.
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