Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN

TL;DR

This paper introduces a conditional GAN approach for more accurate channel estimation in one-bit massive MIMO systems, overcoming limitations of traditional deep learning methods.

Contribution

The paper develops a cGAN model that learns to predict realistic channels and adaptively trains the networks, improving estimation accuracy in one-bit massive MIMO.

Findings

Outperforms existing deep learning methods in channel estimation accuracy.

Achieves high robustness in massive MIMO systems.

Effectively learns an adaptive loss function for training.

Abstract

Channel estimation is a challenging task, especially in a massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADC). Traditional deep learning (DL) methods, that learn the mapping from inputs to real channels, have significant difficulties in estimating accurate channels because their loss functions are not well designed and investigated. In this paper, a conditional generative adversarial networks (cGAN) is developed to predict more realistic channels by adversarially training two DL networks. cGANs not only learn the mapping from quantized observations to real channels but also learn an adaptive loss function to correctly train the networks. Numerical results show that the proposed cGAN based approach outperforms existing DL methods and achieves high robustness in massive MIMO systems.