HDnGAN: A Channel Estimation Method for Time-Varying mmWave Massive MIMO

Channel estimation stands as a pivotal and challenging task for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication system, especially in a time-varying scenario, where exists a massive number of channel coefficients and severe propagation loss due to the Doppler shifts. Conventional estimation schemes may fail to track the fast varying channels and not be able to fully exploit the unique characteristics of mmWave channels in their model designs. In this work, we leverage the Generative Adversarial Networks (GANs) and meticulously design a novel framework named Homogeneous Denoising Generative Adversarial Network (HDnGAN) to tackle the challenge of time-varying channel estimation for mmWave MIMO system. Our framework incorporates the distinctive traits of mmWave channels, such as temporal and spatial correlations, as well as angular sparsity, into the network architecture design. Theoretically, a special case of our proposed HDnGAN with a linear structure is demonstrated to be not inferior to the linear minimum mean squared error (LMMSE) estimator. Numerical simulations underscore the superiority of HDnGAN over existing channel estimation methods, particularly in low signal-to-noise ratio (SNR) regions. Furthermore, it exhibits robustness across varying scenarios. Notably, it remains applicable in out-of-distribution situations and in the absence of ground truth.