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Channel Estimation for Millimeter-Wave Massive MIMO Systems Based On Fast and Flexible Denoising Network

Published: 28 February 2024 Publication History

Abstract

Channel estimation is one of the main challenges in realizing millimeter-wave massive multiple-input multiple-output (MIMO) systems. For mmWave massive MIMO, by exploiting the sparsity of the beamspace channel, academics have formulated the beamspace channel estimation problem as a sparse signal recovery problem. To address this problem, scholars have recently used the classical Approximate Message Passing (AMP) algorithm and Learned Message Passing Algorithm (LAMP) implemented by Deep Neural Networks (DNNs) with impressive success. However, these existing schemes fall short of achieving satisfactory estimation accuracy. In this paper, we propose a fast and flexible FFDNET-based channel estimator for channel estimation in the beam space. Numerical results show that the FFDNET-based channel estimator outperforms the algorithms of conventional channel estimators, compressed sensing techniques, and LDAMP in terms of normalized mean square error.

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    ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
    October 2023
    589 pages
    ISBN:9798400707988
    DOI:10.1145/3633637
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 28 February 2024

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    Author Tags

    1. DNN
    2. FFDNET
    3. channel estimation
    4. massive MIMO
    5. millimeter wave

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