Angular Sector-Based Sparse Array Design for Adaptive Beamforming Using Deep Learning

Abstract

Efficient sparse array reconfigurability is essential for cognitive sensing in dynamic radio frequency environments, where rapid interference variations require both adaptability and stability. This work presents a framework for designing sparse arrays optimized over broad angular sectors, enabling near-optimal beamforming that maximizes the signal-to-interference-plus-noise ratio (SINR) across a range of interferer angles. Full data correlation matrices are computed for candidate configurations, and an angular-sector-based class reduction strategy is applied to merge adjacent sectors dominated by the same configuration, resulting in 56 representative classes. Controlled up- and down-sampling produce four dataset variants involving, high and low sample count, balanced and unbalanced datasets, to systematically evaluate the effects of dataset size and class distribution on neural network performance. A lightweight convolutional neural network (CNN) and a deeper ResNet 50 architecture are trained and evaluated using these datasets. Results demonstrate high classification accuracy, with ResNet 50 achieving up to 97.3%, while SINR deviations remain below 1% for most classes and below 5% even for challenging interference angles near broadside. The proposed approach enables robust sparse array selection, maintains strong SINR performance, reduces unnecessary reconfigurations, and provides an effective framework for real-time cognitive sensing and adaptive interference mitigation.