Compressed Sensing-Aided Downlink Channel Training for FDD Massive MIMO Systems

Han, Yong-Hee; Lee, Jung Woo; Love, David J.

doi:10.1109/tcomm.2017.2691700

Cited by 79 publications

(70 citation statements)

References 47 publications

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“…The channel coefficients in angular domain are generated from complex Gaussian distribution with mean zero and unit variance. 30 For the proposed PS-CBIHT algorithm, the step size is set to be = 0.01;…”

Section: Simulation Resultsmentioning

confidence: 99%

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Prior CSIT estimation for FDD massive MIMO system with 1‐bit feedback per dimension

Zhang

Gao

et al. 2019

Trans Emerging Tel Tech

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In massive multiple-input-multiple-output system, the accurate channel state information at the transmitter (CSIT) is required to achieve the high beamforming gain and spectral efficiency. To reduce the feedback overhead for CSIT acquisition in frequency division duplex system, a prior CSIT estimation with 1-bit feedback per dimension is proposed to alleviate the overhead burden.Specifically, by exploiting the slow variation of channel support between the consecutive frames, the channel support in the previous frame is regarded as the prior information for CSIT estimation of current frame. Then, the received compressed channel measurements at the user side are quantized to 1 bit per measurement and only the sign information is fed back to the base station (BS). At the BS side, the CSIT recovery from the 1-bit channel feedback information is formulated as a 1-bit sparse complex-value signal recovery problem with prior information. Finally, a prior support complex binary iterative hard thresholding algorithm is proposed to perform the CSIT estimation at the BS side. Simulation results demonstrate that the proposed scheme can reduce both the pilot and feedback overhead for CSIT estimation.Trans Emerging Tel Tech. 2019;30:e3644.wileyonlinelibrary.com/journal/ett

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Section: Simulation Resultsmentioning

confidence: 99%

“…Consider a physical channel model, which captures the propagation structure between BS and UE. 7 When the uniform linear array is installed at the BS, the downlink channel vector is expressed as 16,30…”

Section: Channel Modelmentioning

confidence: 99%

Prior CSIT estimation for FDD massive MIMO system with 1‐bit feedback per dimension

Zhang

Gao

et al. 2019

Trans Emerging Tel Tech

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“…with the definition of ψ i,k [ ] in (24) for X k = X, ∀k, to increase the robustness of the decision. Thus, the angles are identified as the L larger values of J i .…”

Section: Musicmentioning

confidence: 99%

FDD Channel Estimation Via Covariance Estimation in Wideband Massive MIMO Systems

González-Coma

Suárez-Casal

Castro

et al. 2020

Sensors

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A method for channel estimation in wideband massive Multiple-Input Multiple-Output (MIMO) systems using covariance identification is developed. The method is useful for Frequency-Division Duplex (FDD) at either sub-6GHz or millimeter wave (mmWave) frequency bands and takes into account the beam squint effect caused by the large bandwidth of the signals. The method relies on the slow time variation of the channel covariance matrix and allows for the utilization of very short training sequences thanks to the exploitation of the channel structure, both in the covariance matrix identification and the channel estimation stages. As a consequence of significantly reducing the training overhead, the proposed channel estimator has a computational complexity lower than other existing approaches.

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“…8. By using the structure in the figure, several CS algorithms have been proposed in [8], [55], [56] to recover massive MIMO channels.…”

Section: Compressive Channel Estimation and Feedbackmentioning

confidence: 99%

Sparse Representation for Wireless Communications: A Compressive Sensing Approach

Qin

Fan

Liu

et al. 2018

IEEE Signal Process. Mag.

217

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Sparse representation can efficiently model signals in different applications to facilitate processing. In this article, we will discuss various applications of sparse representation in wireless communications, with focus on the most recent compressive sensing (CS) enabled approaches. With the help of the sparsity property, CS is able to enhance the spectrum efficiency and energy efficiency for the fifth generation (5G) networks and Internet of Things (IoT) networks. This article starts from a comprehensive overview of CS principles and different sparse domains potentially used in 5G and IoT networks. Then recent research progress on applying CS to address the major opportunities and challenges in 5G and IoT networks is introduced, including wideband spectrum sensing in cognitive radio networks, data collection in IoT networks, and channel estimation and feedback in massive MIMO systems. Moreover, other potential applications and research challenges on sparse representation for 5G and IoT networks are identified. This article will provide readers a clear picture of how to exploit the sparsity properties to process wireless signals in different applications.

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Compressed Sensing-Aided Downlink Channel Training for FDD Massive MIMO Systems

Cited by 79 publications

References 47 publications

Prior CSIT estimation for FDD massive MIMO system with 1‐bit feedback per dimension

Prior CSIT estimation for FDD massive MIMO system with 1‐bit feedback per dimension

FDD Channel Estimation Via Covariance Estimation in Wideband Massive MIMO Systems

Sparse Representation for Wireless Communications: A Compressive Sensing Approach

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