Blind Channel Estimation and Symbol Detection for Multi-Cell Massive MIMO Systems by Expectation Propagation

Ghavami, Kamran; Naraghi-Pour, Mort

doi:10.1109/twc.2017.2772837

Cited by 35 publications

(20 citation statements)

References 27 publications

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“…This technique is sensitive to the accuracy of the sample correlation matrix as well as the size of the antenna array. This technique has been exploited in [243] where a blind channel estimation has been performed followed by a symbol detection based on the expectation propagation (EP) algorithm. Briefly, EP aims to find the closest approximation for the conditional marginal distribution of a required variable in an iterative refinement procedure.…”

Section: The Impact Of Channel Estimation and Precoding On Massivmentioning

confidence: 99%

Massive MIMO Detection Techniques: A Survey

Albreem

Juntti

Shahabuddin

2019

IEEE Commun. Surv. Tutorials

340

229

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Section: The Impact Of Channel Estimation and Precoding On Massivmentioning

confidence: 99%

Massive MIMO Detection Techniques: A Survey

Albreem

Juntti

Shahabuddin

2019

IEEE Commun. Surv. Tutorials

340

229

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“…The subspace-based estimation approach can enhance the spectral efficiency since it only uses very short or even no pilot sequences [6], [16]. In [10], an expectation propagation (EP) based blind estimation method is proposed, in which the channel coefficient is initialized by eigenvalue decomposition (EVD) on covariance matrix of the received data signal, and updated by an iterative EP algorithm. However, the subspace-based method is sensitive to the signal to interference plus noise ratio (SINR).…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Subspace MMSE Channel Estimation in Massive MU-MIMO System

Deng

Ohtsuki²

2020

IEEE Access

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Massive multiuser multiple-input multiple-output (massive MU-MIMO) technology is considered as a promising enabler to fulfill the rapid growth of traffic requirement for wireless mobile communications. The massive MU-MIMO system can achieve unlimited capacity when the base station (B-S) has accurate channel state information (CSI). In time-division-duplex (TDD) mode, the BS estimates CSI by receiving pilot signals sent from user terminals (UEs). However, because of using non-orthogonal pilots, pilot contamination happens to degrade the quality of the CSI estimation. To deal with pilot contamination problem, a low-complexity subspace minimum mean square error (MMSE) estimation method is proposed in this paper. Specifically, our approach operates the MMSE estimation in a low-dimensional subspace to avoid large matrix manipulation. Meanwhile, subspace projection helps to discriminate the desired signal and interfering signals in the power domain. Interference analysis shows the MMSE estimation can achieve interference-free estimation even in a low-dimensional subspace with a large number of BS antennas, and non-overlapping angles of arrival (AoAs) between desired and interfering UEs. Furthermore, thanks to the low-rank property of the channel covariance matrix in massive MU-MIMO systems, a two-step covariance matrix subspace projection method is proposed for further computational complexity reduction. The complexity analysis and simulation results indicate that our proposed approach has better channel estimation accuracy with lower complexity than the conventional MMSE estimation when the number of BS antennas is large. INDEX TERMS Massive MU-MIMO, channel estimation, pilot contamination, MMSE, subspace projection.

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“…Accurate and stable channel state information is a necessary condition for high resolution signal transmission in wireless channel, which poses a huge challenge to channel estimation in complex environment [11]- [15]. However, there are many studies on channel estimation [13], [16]- [18], [20]- [28]. From the perspective of prior information of the channel estimation algorithms, it can be divided into three categories.…”

Section: Introductionmentioning

confidence: 99%

“…From the perspective of prior information of the channel estimation algorithms, it can be divided into three categories. One is the blind channel estimation methods [16]- [18], which take advantage of some inherent characteristics of modulated signals that are independent of the specific carrying information, or uses decision feedback to estimate the channel. Another is the semi-blind channel estimation methods [19]- [21], which combine the advantages of blind estimation and training sequence estimation.…”

Section: Introductionmentioning

confidence: 99%

A Sparsity-Based Adaptive Channel Estimation Algorithm for Massive MIMO Wireless Powered Communication Networks

Yuan

Shi

et al. 2019

IEEE Access

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Compressed sensing (CS) based channel estimation methods can effectively acquire channel state information for Massive MIMO wireless powered communication networks. In order to solve the problem that the existing sparsity-based adaptive matching pursuit (SAMP) channel estimation algorithm is unstable under low signal to noise ratio (SNR), an optimized adaptive matching pursuit (OAMP) algorithm is proposed in this paper. First, the channel is pre-estimated. Next, the energy entropy-based order determination is raised to optimize the reconstruction performance of the algorithm. Then, a staged adaptive variable step size method is put forward to further promote the accuracy of channel estimation. Finally, theoretical analysis and simulation results demonstrate that the proposed OAMP algorithm improves the accuracy at the expense of a small amount of time complexity, does not require a priori knowledge of sparsity and its comprehensive performance is superior to other existing channel estimation algorithms.INDEX TERMS Massive MIMO, wireless powered communication networks, sparse channel estimation, sparsity-based adaptive matching pursuit, energy entropy-based order determination, staged adaptive variable step size.

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Blind Channel Estimation and Symbol Detection for Multi-Cell Massive MIMO Systems by Expectation Propagation

Cited by 35 publications

References 27 publications

Massive MIMO Detection Techniques: A Survey

Massive MIMO Detection Techniques: A Survey

Low-Complexity Subspace MMSE Channel Estimation in Massive MU-MIMO System

A Sparsity-Based Adaptive Channel Estimation Algorithm for Massive MIMO Wireless Powered Communication Networks

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