Conditional Gaussian Models for Channel Estimation in Quantized Environments

As the demand for higher data rates and more reliable communication continues to surge, the importance of deploying massive MIMO and millimeter-wave systems grows. However, the practical implementation of these advanced systems is significantly challenged by the high costs and substantial power consumption associated with the high-resolution. Analog to Digital Convertors (ADCs) required for each antenna’s RF chain. Low-resolution ADCs present a promising alternative by markedly reducing these costs and power demands. Despite their benefits, highly low-resolution ADCs introduce nonlinear distortions that compromise the effectiveness of conventional channel estimation algorithms. This work evaluates the performance of Variational Autoencoders (VAE), Gaussian Mixture Models (GMM) and Mixture of Factor Analyzers (MFA) for quantized channel estimation in wireless communication systems. Thea’s methodology involved simulating a 3GPP channel model with multiple pilots and antennas, where each model was tested across varying Signal-to-Noise Ratios (SNR) and quantization levels. VAE, leveraging Convolutional Neural Networks (CNN) for effective modeling and channel estimation, consistently outperforms the others, achieving the lowest MSE of 0.009 and the highest rate lower bound of 4.3 bps/Hz at 20 dB SNR. Despite higher computational demands, VAE demonstrates superior accuracy, making it a strong candidate for future wireless systems requiring precise channel estimation.