Enhanced Compressive Downlink CSI Recovery for FDD Massive MIMO Systems Using Weighted Block ${\ell _1}$-Minimization

Tseng, Chyuan-Yow; Wu, Jwo-Yuh; Lee, Ta-Sung

doi:10.1109/tcomm.2016.2520945

Cited by 48 publications

(50 citation statements)

References 27 publications

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“…Namely, the number of transmitting antennas at BS M = 160, the number of receiving antennas at MS N = 2, the number of users K = 40, and the number of training pilot symbols T = 45. The proposed algorithm outperforms the other algorithms, though it does show worse normalised mean square error (NMSE) at low SNR (<3 dB) than the Tseng's method 16 . As shown in Fig.…”

Section: Experiments Resultsmentioning

confidence: 93%

“…We compare the performance of the proposed algorithm to the J-OMP recovery algorithm 14 , weighted block L1-minimisation algorithm (hereafter referred to as WB L1-minimisation) 16 , and JBOMP algorithm 17 . Our simulation experiments consider a narrow band (flat fading) massive MU-MIMO system in FDD mode, where the BS is equipped with M transmitting antennas and there are K users, each of which has N receiving antennas.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…The angle of departures is uniformly and randomly distributed over [0,2 ] π , and we assume that all spatial paths have same path loss. Figure 3 compares the proposed algorithm, J-OMP, WB L1-minimisation algorithm, and JBOMP for different SNRs and the parameters were set according to our references 16 . Namely, the number of transmitting antennas at BS M = 160, the number of receiving antennas at MS N = 2, the number of users K = 40, and the number of training pilot symbols T = 45.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Rao 15 , et al then proposed a modified subspace pursuit (SP) algorithm to solve conventional CSbased CSIT estimation problems by exploiting the prior support adaptively based on quality information. In addition to existing greedy-based signal reconstruction methods, other researchers utilised the same sparsity structure proposed by Rao 14 , et al to build an L1-minimisation-based downlink CSIT recovery scheme 16 . By exploiting the block sparsity of channel matrices in the virtual angular domain among different users, Xu 17 , et al proposed a joint block orthogonal matching pursuit (JBOMP) algorithm to estimate CSIT.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Channel Estimation for Frequency Division Duplexing Multi-user Massive MIMO Systems via Tensor Compressive Sensing

Wang

Wei

Zhu³

2017

Def. Sc. Jl.

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To make full use of space multiplexing gains for the multi-user massive multiple-input multiple-output, accurate channel state information at the transmitter (CSIT) is required. However, the large number of users and antennas make CSIT a higher-order data representation. Tensor-based compressive sensing (TCS) is a promising method that is suitable for high-dimensional data processing; it can reduce training pilot and feedback overhead during channel estimation. In this paper, we consider the channel estimation in frequency division duplexing (FDD) multiuser massive MIMO system. A novel estimation framework for three dimensional CSIT is presented, in which the modes include the number of transmitting antennas, receiving antennas, and users. The TCS technique is employed to complete the reconstruction of three dimensional CSIT. The simulation results are given to demonstrate that the proposed estimation approach outperforms existing algorithms.

show abstract

Section: Experiments Resultsmentioning

confidence: 93%

Section: Experiments Resultsmentioning

confidence: 99%

Section: Experiments Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Channel Estimation for Frequency Division Duplexing Multi-user Massive MIMO Systems via Tensor Compressive Sensing

Wang

Wei

Zhu³

2017

Def. Sc. Jl.

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“…Specifically, a non-orthogonal DL pilot design and a distributed sparsity adaptive matching pursuit algorithm were proposed in [21] by exploiting the common sparsity across different subcarriers. Weighted l 1 -minimization frameworks were adopted in [22] to enhance the performance of sparse reconstructions over the greedy-based algorithms. The work [23] proposed a closedloop resource adaptation scheme to handle the uncertainty of channel sparsity level, and the study [24] combined CS technique and the conventional least squares (LS) algorithm by assuming slow-varying channel statistics.…”

Section: Introductionmentioning

confidence: 99%

Virtual Angular-Domain Channel Estimation for FDD Based Massive MIMO Systems with Partial Orthogonal Pilot Design

Zhao

Hanzo

et al. 2020

IEEE Trans. Veh. Technol.

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This paper proposes an virtual angular-domain channel estimation scheme for massive multiple-input multipleoutput systems operating in frequency division duplex (FDD) mode. Different from the conventional scheme where orthogonal pilots are transmitted on different antennas, we propose to transfer the channel estimation problem to the virtual angular domain and utilize the channel sparsity to reduce the training and feedback overhead. An orthogonal matching pursuit with Gram-Schmidt orthogonalization algorithm is proposed to construct the unitary transformation between the spatial domain and the virtual angular domain, which achieves higher sparsity than the existing approaches. Furthermore, we propose to estimate the downlink (DL) dominant angular set, which captures most of the channel power with only a few elements, by utilizing the directional reciprocity of FDD systems, where a calibration algorithm is introduced to handle the different wavelengths of uplink and DL transmissions. Based on the estimated dominant sets, we introduce a partial orthogonal criterion for virtual angular-domain pilot design and further propose two pilot assignment algorithms which minimize pilot overhead and pilotreuse interference, respectively. Theoretical analyses on pilot overhead and the mean square error (MSE) performance are also presented. Simulation results demonstrate that our proposed virtual angular-domain channel estimation scheme provides excellent MSE performance with much reduced pilot overhead and, consequently, enjoys much larger per-user achievable rate in comparison to the conventional schemes.

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Compressive Channel Estimation Based on Weighted IRLS in FDD Massive MIMO

Lu¹,

Wang²,

Fang³

et al. 2018

Wireless Pers Commun

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Enhanced Compressive Downlink CSI Recovery for FDD Massive MIMO Systems Using Weighted Block ${\ell _1}$-Minimization

Cited by 48 publications

References 27 publications

Channel Estimation for Frequency Division Duplexing Multi-user Massive MIMO Systems via Tensor Compressive Sensing

Channel Estimation for Frequency Division Duplexing Multi-user Massive MIMO Systems via Tensor Compressive Sensing

Virtual Angular-Domain Channel Estimation for FDD Based Massive MIMO Systems with Partial Orthogonal Pilot Design

Compressive Channel Estimation Based on Weighted IRLS in FDD Massive MIMO

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