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

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.

5 pages;7 figures; 1 Table

References in corpus (2)

• Conditional Generative Adversarial Nets
• DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications

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• On the Mean Square Error Optimal Estimator in One-Bit Quantized Systems
• Beamforming Inferring by Conditional WGAN-GP for Holographic Antenna Arrays
• A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection in OFDM Systems
• RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems