Deep Learning-based Channel Estimation for Beamspace mmWave Massive MIMO Systems

2018/02/05 by Hengtao He, Chao-Kai Wen, He, Hengtao +5 · 30 citations
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Information Theory (cs.IT) #Microwave Engineering and Waveguides #Millimeter-Wave Propagation and Modeling #Wireless Signal Modulation Classification #cs.IT #math.IT

paper · pdf · doi:10.48550/arxiv.1802.01290

4 pages, 6 figures, published in IEEE Wireless Communications Letters; the partial source code of this paper is available on GitHub: https://github.com/hehengtao/LDAMP_based-Channel-estimation

openalex publication_date 2018/02/05 · arxiv created 2019/01/14 · arxiv updated 2019/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Channel estimation is very challenging when the receiver is equipped with a limited number of radio-frequency (RF) chains in beamspace millimeter-wave (mmWave) massive multiple-input and multiple-output systems. To solve this problem, we exploit a learned denoising-based approximate message passing (LDAMP) network. This neural network can learn channel structure and estimate channel from a large number of training data. Furthermore, we provide an analytical framework on the asymptotic performance of the channel estimator. Based on our analysis and simulation results, the LDAMP neural network significantly outperforms state-of-the-art compressed sensingbased algorithms even when the receiver is equipped with a small number of RF chains. Therefore, deep learning is a powerful tool for channel estimation in mmWave communications.

Citations

• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Learned D-AMP: Principled Neural Network based Compressive Image Recovery

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