Compressed CSI Acquisition in FDD Massive MIMO: How Much Training is Needed?

Shen, Juei-Chin; Zhang, Jun; Alsusa, Emad; Letaief, Khaled B.

doi:10.1109/twc.2016.2535310

Cited by 91 publications

(75 citation statements)

References 40 publications

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“…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%

“…15 By exploiting channel support in the previous frame, a modified subspace pursuit (MSP) method was proposed to further reduce the training overhead. In the work of Shen et al, 16 the CSIT acquisition was solved by a weighted l 1 minimization formulation was proposed to exploit the prior support information. 17 In the work of Zhang et al, 18 based on the support information in the previous frame, the channel vectors among multiple subcarriers to be estimated in current frame can be separated into the dense channel part and sparse channel part.…”

Section: Introductionmentioning

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: Channel Modelmentioning

confidence: 99%

Section: Introductionmentioning

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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“…For example, in the proposed channel model in the report 3GPP TS36.900 [12], the number of dominating physical path is six. So there is channel sparsity in angular domain.…”

Section: Mismatch Of Mimo Channel Basismentioning

confidence: 99%

“…In [11], it separates the channel vector into a dense vector and a sparse vector and makes use of the previous channel to predict the dense vector by least squares algorithm and applies CS to estimate the sparse vector. In [12], it examines the impacts on the training overhead in FDD downlink channel estimation when previous channel support information is applied into a weighted l 1 minimization framework. In multiuser scenario, in [13], it proposes a close-loop pilot and CSIT feedback resource adaptation framework for MU massive MIMO, and the joint sparsity among users is used for compressive channel estimation.…”

Section: Introductionmentioning

confidence: 99%

Downlink compressive channel estimation with support diagnosis in FDD massive MIMO

Lu¹,

Wang²,

Fang³

et al. 2018

J Wireless Com Network

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Downlink channel state information (CSI) is critical in a frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system. We exploit the reciprocity between uplink and downlink channels in angular domain and diagnose the supports of downlink channel from the estimated uplink channel. While the basis mismatch effects will damage the sparsity level and the path angle deviations between uplink and downlink transmission paths will induce differences in channel supports, a downlink support diagnosis algorithm based on the DBSCAN (density-based spatial clustering of applications with noise) which is widely used in machine learning is presented. With the diagnosed supports of downlink channel in angular domain, a weighted subspace pursuit (SP) channel estimation algorithm for FDD massive MIMO is proposed. The restricted isometry property (RIP)-based performance analysis for the weighted SP algorithm is given out. Both the analysis and the simulation results show that the proposed downlink channel estimation with diagnosed supports is superior to the standard iteratively reweighted least squares (IRLS) and SP without channel priori or with the assumption of the common supports for uplink and downlink channels in angular domain.

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“…Numerical results in [4] also showed that a significant overhead reduction can be achieved via the partial support information obtained from the uplink. These results have given evidence to the possibility of exploiting the correlation between uplink and downlink for downlink CSI acquisition, which motivates the work of this paper.…”

Section: Introductionmentioning

confidence: 97%

Efficient downlink channel reconstruction for FDD transmission systems

Han

Hsu

Wen

et al. 2018

2018 27th Wireless and Optical Communication Conference (WOCC)

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Abstract-In this paper, a novel scheme is proposed to reconstruct downlink channel for frequency-division-duplex (FDD) systems from uplink channel state information (CSI) with limited feedback. We adopt the Newtonized orthogonal matching pursuit (NOMP) algorithm to estimate the frequency-independent parameters of the delays of a multi-path channel through uplink training. The gain of each path is further refined through downlink training using least squares (LS) algorithm and sent back to the base station (BS). Using the uplink-estimated delays and the downlink-refined gains, the BS reconstructs the downlink multipath channel. Simulations and over-the-air tests are conducted to examine the performance of the proposed NOMP-LSbased reconstruction scheme for the downlink channel. Results confirm the effectiveness of the NOMP-LS scheme and that the reconstructed channel is accurate.

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Compressed CSI Acquisition in FDD Massive MIMO: How Much Training is Needed?

Cited by 91 publications

References 40 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

Downlink compressive channel estimation with support diagnosis in FDD massive MIMO

Efficient downlink channel reconstruction for FDD transmission systems

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