# A GAN-CNN Fusion Framework for Deep Learning-Based DOA Estimation in Low-SNR Environments

## TL;DR

This paper proposes a new deep learning framework combining GAN and CNN to improve direction of arrival estimation in low signal-to-noise environments.

## Contribution

A two-stage GAN-CNN framework with attention and phase-consistent loss for enhanced DOA estimation in low-SNR conditions.

## Key findings

- The framework achieves 72.2% DOA accuracy and 3.9° RMSE at -10 dB SNR with 500 snapshots.
- It maintains 93.8% accuracy with only 50 snapshots, showing robustness to limited data.
- The method outperforms conventional and deep learning baselines in low-SNR environments.

## Abstract

Direction of Arrival (DOA) estimation faces significant performance degradation under low Signal-to-Noise Ratio (SNR) conditions, where traditional algorithms and deep learning models struggle due to corrupted spatial information and limited training data. To address these challenges, this paper introduces a novel two-stage framework that integrates a Generative Adversarial Network (GAN) for signal enhancement with a complex-valued Convolutional Neural Network (CNN) for DOA estimation. The proposed GAN incorporates an attention mechanism and a dedicated phase-consistent loss function to suppress noise while preserving spatial phase information critical for accurate direction finding. Enhanced signals are transformed into covariance matrices and processed by a complex-valued CNN designed to extract robust spatial features. Extensive experiments demonstrate that the proposed method achieves a DOA accuracy of 72.2% and a Root Mean Square Error (RMSE) of 3.9° at —10 dB SNR with 500 snapshots, substantially outperforming conventional and deep learning baselines. The framework also shows strong robustness to limited data, maintaining 93.8% accuracy with only 50 snapshots. The framework offers a practical solution for reliable DOA estimation in low-SNR and data-scarce environments.