Compressive Channel Estimation Exploiting Block Sparsity in Multi-User Massive MIMO Systems

Xu, Wenbo; Shen, Tao; Tian, Ye; Wang, Yifan; Lin, Jiaru

doi:10.1109/wcnc.2017.7925513

Cited by 9 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…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. Whether based on the greedy algorithm or convex optimisation algorithm of CS, the CSIT estimation for large and high-order channel coefficient matrices in multi-user massive MIMO requires that each individual user be accounted for; this wastes training pilots and results in excessive feedback overhead.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Wang

Wei

Zhu³

2017

Def. Sc. Jl.

View full text Add to dashboard Cite

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: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang

Wei

Zhu³

2017

Def. Sc. Jl.

View full text Add to dashboard Cite

show abstract

“…Assuming that the estimated amplitudes of non-zero coefficients are channel gains of each path, applying sparse exploration is to compare the pairs of angle of departure (AoD) and angle of arrival (AoA) of each path in mmWave systems [8]. Most existing CE methods use narrow-band flat channel models [8][9][10]. However, in practice, mmWave channels generally have wideband and frequency-selective fading characteristics.…”

Section: Prior Workmentioning

confidence: 99%

Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systems

et al. 2020

View full text Add to dashboard Cite

This paper presents a novel variational Bayesian learning‐based channel estimation scheme for hybrid pre‐coding‐employed wideband multiuser millimetre wave multiple‐input multiple‐output communication systems. We first propose a frequency variational Bayesian algorithm, which leverages common sparsity of different sub‐carriers in the frequency domain. The algorithm shares all the information of the support sets from the measurement matrices, significantly improving channel estimation accuracy. To enhance robustness of the frequency variational Bayesian algorithm, we develop a hierarchical Gaussian prior channel model, which employs an identify‐and‐reject strategy to deal with random outliers imposed by hardware impairments. A support selection frequency variational Bayesian channel estimation algorithm is also proposed, which adaptively selects support sets from the measurement matrices. As a result, the overall computational complexity can be reduced. Validated by the Bayesian Cramér‐Rao bound, simulation results show that, both frequency variational Bayesian and support selection‐frequency variational Bayesian algorithms can achieve higher channel estimation accuracy than existing methods. Furthermore, compared with frequency variational Bayesian, support selection‐frequency variational Bayesian requires significantly lower computational complexity, and hence, it is more practical for channel estimation applications.

show abstract

“…The uplink CSI can be more easily obtained at the BS due to the less number of single-antenna users and the strong capability of processing at BS. And then by leveraging the channel reciprocity property, the CSI in the downlink can be directly tacked [6,7]. However, due to the fact that radio frequency chains suffer from calibration error and restricted coherence time, the CSI obtained in the uplink may not be correct for the downlink [8,9].…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

Waseem

Naveed

Ali³

et al. 2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Massive multiple-input multiple-output (MIMO) is believed to be a key technology to get 1000x data rates in wireless communication systems. Massive MIMO occupies a large number of antennas at the base station (BS) to serve multiple users at the same time. It has appeared as a promising technique to realize high-throughput green wireless communications. Massive MIMO exploits the higher degree of spatial freedom, to extensively improve the capacity and energy efficiency of the system. Thus, massive MIMO systems have been broadly accepted as an important enabling technology for 5th Generation (5G) systems. In massive MIMO systems, a precise acquisition of the channel state information (CSI) is needed for beamforming, signal detection, resource allocation, etc. Yet, having large antennas at the BS, users have to estimate channels linked with hundreds of transmit antennas. Consequently, pilot overhead gets prohibitively high. Hence, realizing the correct channel estimation with the reasonable pilot overhead has become a challenging issue, particularly for frequency division duplex (FDD) in massive MIMO systems. In this paper, by taking advantage of spatial and temporal common sparsity of massive MIMO channels in delay domain, nonorthogonal pilot design and channel estimation schemes are proposed under the frame work of structured compressive sensing (SCS) theory that considerably reduces the pilot overheads for massive MIMO FDD systems. The proposed pilot design is fundamentally different from conventional orthogonal pilot designs based on Nyquist sampling theorem. Finally, simulations have been performed to verify the performance of the proposed schemes. Compared to its conventional counterparts with fewer pilots overhead, the proposed schemes improve the performance of the system.

show abstract

Compressive Channel Estimation Exploiting Block Sparsity in Multi-User Massive MIMO Systems

Cited by 9 publications

References 17 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

Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systems

Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

Contact Info

Product

Resources

About