Antenna Array Pattern with Sidelobe Level Control using Deep Learning

Motivated by the demonstrated success of artificial intelligence (AI) in wireless communications systems, this paper proposes a deep learning-based approach for generating a desired radiation pattern with sidelobe level (SLL) control in active electronically scanned array (AESA) antennas. Recent works in this direction are mostly limited to generating radiation patterns with only beam scanning capability, inhibiting their wide-scale applicability. In this work, we propose a unified deep neural network (DNN) model that enable simultaneous control over both beam scanning angles and SLLs across a range of operating scenarios. To accomplish this task, the DNN model efficiently predicts the phase and amplitude of each array element. To learn the DNN model’s parameters, we construct a training dataset comprising amplitude values and phases as labeled outputs and corresponding 181-point radiation patterns as input features. The training and validation process of the proposed DNN model reveals high accuracy in terms of R2 score and mean square error (MSE). For prediction, the desired radiation pattern consisting of 181 points is fed to the trained DNN model to yield optimized weights of antenna elements. The numerical results on a 1×8 linear phase antenna array, using an assortment of beam scanning angles and SLLs, demonstrate the effectiveness of the proposed model. The numerical results presented in MATLAB and CST simulators are validated by measurements on a 1×8 microstrip prototype array.