Direction of Arrival (DOA) Estimation Using a Deep Unfolded Learned Iterative Shrinkage Thresholding Algorithm (LISTA) Network in a Non-Uniform Metasurface

This paper proposes a novel method for Direction of Arrival (DOA) estimation using a deep unfolded LISTA network in a non-uniform metasurface. Traditional DOA estimation methods often face challenges such as limited accuracy, high computational complexity, and poor adaptability to complex signal environments. To address these issues, we optimize a non-uniform metasurface array to reduce hardware costs and mutual coupling effects while enhancing resolution. Additionally, a deep unfolded Learned Iterative Shrinkage Thresholding Algorithm (LISTA) network is constructed by transforming Iterative Shrinkage Thresholding Algorithm (ISTA) iterative steps into trainable neural network layers, combining model-driven logic with data-driven parameter optimization. Simulation results prove that this method enhances higher precision and reduces computational complexity in comparison with traditional algorithms, especially under low SNR conditions. Furthermore, the method exhibits greater generalization ability, making it a reliable approach for high-precision DOA estimation in practical applications.