Deep Learning for Massive MIMO Channel State Acquisition and Feedback
نویسندگان
چکیده
منابع مشابه
Deep Learning for Massive MIMO CSI Feedback
In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead. In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recov...
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Massive multiple-input multiple-output (MIMO) is becoming a key technology for future 5G wireless communications. Channel feedback for massive MIMO is challenging due to the substantially increased dimension of MIMO channel matrix. In this letter, we propose a compressive sensing (CS) based differential channel feedback scheme to reduce the feedback overhead. Specifically, the temporal correlat...
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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 larg...
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The objective of this article is to review and benchmark strategies to acquire channel state information (CSI) in time-division duplex (TDD) Massive MIMO systems. In particular, we consider the use of statistical CSI at the base stations (BSs), together with non-orthogonal pilot sequences. Such techniques can theoretically reduce the amount of spectral resources dedicated to channel sounding, t...
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ژورنال
عنوان ژورنال: Journal of the Indian Institute of Science
سال: 2020
ISSN: 0970-4140,0019-4964
DOI: 10.1007/s41745-020-00169-2