Forest Resonance Model: A Novel Forest-Inspired Approach to Microwave Antenna Signal Strength Estimation

Microwave antenna design optimization has always been a challenge due to the intricate relationships between design parameters and signal strength. Traditional models often fail to capture the nuanced interactions between these parameters. Drawing inspiration from the dynamics of a forest ecosystem, we introduce the Forest Resonance Model (FRM) to estimate signal strength based on antenna design parameters. The primary goal of this research is to develop and validate a novel forest-inspired model that can accurately predict the signal strength of microwave antennas based on design parameters. The FRM offers a fresh perspective on microwave antenna design optimization by bridging the gap between nature-inspired concepts and machine learning and highlights the potential of drawing inspiration from natural systems for complex engineering challenges. The FRM conceptualizes each antenna design as a tree, with its attributes such as TestFreq, PatchLength, PatchWidth, SlotLength, and SlotWidth determining its height, base width, and health. The interactions between these 'trees' (antenna designs) mimic the feature space interactions in machine learning. We implemented this concept using a modified Random Forest algorithm, incorporating feature engineering techniques to capture combined effects of design parameters. The dataset, comprising different antenna designs and their corresponding signal strengths, was used to train and validate the model. Preliminary results indicate that the our enhanced FRM model provides superior prediction accuracy compared to traditional models. The feature importance scores derived from the model shed light on the most influential design parameters, offering insights into optimal antenna design. The manufactured optimized antenna design was confirmed through a series of signal strength measurements in lab environment.