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

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Document pages: 5 pages

Abstract: Channel estimation is a challenging task, especially in a massivemultiple-input multiple-output (MIMO) system with one-bit analog-to-digitalconverters (ADC). Traditional deep learning (DL) methods, that learn themapping from inputs to real channels, have significant difficulties inestimating accurate channels because their loss functions are not well designedand investigated. In this paper, a conditional generative adversarial networks(cGAN) is developed to predict more realistic channels by adversariallytraining two DL networks. cGANs not only learn the mapping from quantizedobservations to real channels but also learn an adaptive loss function tocorrectly train the networks. Numerical results show that the proposed cGANbased approach outperforms existing DL methods and achieves high robustness inmassive MIMO systems.

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