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Deep Learning-Based Frequency-Selective Channel Estimation for Hybrid mmWave MIMO Systems
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Channel Estimation for Millimeter-Wave Massive MIMO With Hybrid Precoding Over Frequency-Selective Fading Channels
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CNN-Based Precoder and Combiner Design in mmWave MIMO Systems
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Toward Higher Performance for Channel Estimation With Complex DnCNN
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Sparse Representation for Wireless Communications: A Compressive Sensing Approach
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Massive MIMO in Sub-6 GHz and mmWave: Physical, Practical, and Use-Case Differences
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Channel Estimation in Massive MIMO Under Hardware Non-Linearities: Bayesian Methods Versus Deep Learning
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Angle-Based Multipath Estimation and Beamforming for FDD Cell-free Massive MIMO
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A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO
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Frequency Selective Hybrid Precoding for Limited Feedback Millimeter Wave Systems
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Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems
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Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO
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On the Achievability of Cramér–Rao Bound in Noisy Compressed Sensing
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Beamspace Channel Estimation for Millimeter Wave Massive MIMO System With Hybrid Precoding and Combining
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inferredcites →https://doi.org/10.1109/tvt.2019.2937543openalex-snapshot
Channel Estimation for Cell-Free mmWave Massive MIMO Through Deep Learning
inferredcites →https://doi.org/10.1109/twc.2018.2804943openalex-snapshot
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inferredcites →https://doi.org/10.1109/twc.2019.2956146openalex-snapshot
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