Deep Learning Based AoA and AoD Estimation for Millimeter Wave MIMO Systems
Diego Lloria, Sandra Roger, Carmen Botella, Máximo Cobos · 2024
In this work, we propose using a deep learning method for parametric millimeter-wave (mmWave) channel estimation, specifically focusing on angle-of-arrival (AoA) and angle-of-departure (AoD) parameters in the frequency domain. Channel estimation is fundamental in mmWave to efficiently implement beamforming techniques. Our approach involves adapting a residual convolutional neural network (ResNet) to the task, incorporating a technique from topological data analysis to accurately estimate angular frequencies. Additionally, we enhance the model’s performance by including a posterior model fitting to improve the probability of detection. Through simulations, we compare our ResNet-based approach with existing signal processing methods and the Crámer-Rao lower bound. Our findings demonstrate significant enhancements in system robustness, increasing the probability of detection while minimizing estimation errors.