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Neural Network-Based Intelligent Reflecting Surface Assisted Direction of Arrival Estimation under Non-Line-of-Sight Scenario

Direction-of-Arrival (DoA) estimation assisted with an Intelligent Reflecting Surface (IRS) is crucial for various wireless applications, especially in challenging Non-Line-of-Sight (NLoS) environments. This paper presents a novel neural network-based architecture to address this challenge. The key innovation is the introduction of a dedicated, learnable IRS layer integrated integrated within a carefully designed network structure established upon the physical and geometrical basis of the problem. Unlike conventional neural network layers, this specific layer incorporates block diagonal sinusoidal weight constraints, where the phase arguments of these sinusoids are learned during training to directly emulate the phase shifts of the IRS elements. This allows the network to optimize the IRS configuration for enhanced DoA estimation, eliminating the need for separate IRS optimization algorithms. Moreover, various trainable DoA regressors, including a proposed structure, are presented and examined. Numerical simulations, conducted under various conditions and noise levels, where controlled coherent multi-path components are introduced due to the presence of the IRS, demonstrate the superior performance of the proposed approach compared to others and highlight its potential to significantly improve the accuracy of DoA estimation in complex IRS-assisted wireless systems.

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