Compressed channel estimation for FDD massive MIMO systems without prior knowledge of sparse channel model

Azizipour, Mohammad Javad; Mohamed-pour, Kamal

doi:10.1049/iet-com.2018.5134

Cited by 17 publications

(8 citation statements)

References 26 publications

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“…Some of them do not need to specify the sparsity level in advance. For instance, OMP-based algorithms in [28,29], and Bayesian model-based algorithms in [30][31][32][33]. However, they are not considered in the study, since the knowledge of sparsity level is also very useful to decide the minimal number of required measurements.…”

Section: Problem Formulationmentioning

confidence: 99%

Statistical inference for the block sparsity of complex‐valued signals

Wang¹,

Zhou

Yu³

2020

IET signal process.

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Block sparsity is an important parameter in many algorithms to successfully recover block sparse signals under the framework of compressive sensing. However, it is often unknown and needs to be estimated. Recently there emerges a few research work about how to estimate block sparsity of realvalued signals, while there is, to the best of our knowledge, no investigation that has been conducted for complex-valued signals. In this paper, we propose a new method to estimate the block sparsity of complex-valued signal. Its statistical properties are obtained and verified by simulations. In addition, we demonstrate the importance of accurately estimating the block sparsity in signal recovery through a sensitivity analysis.

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Section: Problem Formulationmentioning

confidence: 99%

Statistical inference for the block sparsity of complex‐valued signals

Wang¹,

Zhou

Yu³

2020

IET signal process.

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“…4) should be repeated according to the number of elements in their real support sets. In general, the number of common and individual coefficients can vary considerably by a small movement of users in the cell [41,42]. Therefore, considering known sparsity order for common and individual sets and even statistical bounds for each would be an unreasonable assumption, particularly for the common non-zero coefficients.…”

Section: Selection Of Thresholds In Algorithm 2 (Fig 4)mentioning

confidence: 99%

“…Therefore, considering known sparsity order for common and individual sets and even statistical bounds for each would be an unreasonable assumption, particularly for the common non‐zero coefficients. In our recent work [41], we introduced an alternative scheme to halt the greedy algorithms when the number of non‐zero elements is unknown. Here, in the complex sparse channel model, we have common and individual support sets separately that defining a specific halting condition is vital for each.…”

Section: Joint Channel Estimation Via Grouped Usersmentioning

confidence: 99%

Channel estimation for FDD multi‐user massive MIMO systems: a greedy approach based on user clustering

Azizipour

Mohamed-pour

2019

IET signal process.

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Obtaining channel state information (CSI) at the transmitter side is essential to take advantage of spectral and energy efficiency in massive multiple-input multiple-output systems. In particular, due to a large number of antennas at the base station (BS) side, the required pilots for downlink channel estimation and the following CSI feedback in the uplink path would be prohibitively large when the system employs frequency-division duplex protocol for data transmission. In this study, the authors propose a novel compressed sensing algorithm which exploits the commonly shared sparsity among nearby users to reduce the estimation error and the number of assigned pilots accordingly. Moreover, to gather users' channel with common sparsity, a clustering procedure is introduced, which groups active users located in the cell according to their mean angle of arrivals received by the uplink signals at the BS antenna array. After clustering users properly, the proposed algorithm can exploit shared support set existing in each cluster efficiently, thereby improving CSI estimation performance. Numerical results demonstrate that the clustering idea along with the proposed algorithm, outperforms other solutions, and is capable of approaching the performance bound when the transmit power is increased.

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“…X. Li et al proposed a two-stage CS scheme, which utilize the sparse and low-rank properties of the angular spreads domain [4].M. J. Azizipour et al proposed a new greedy-based algorithm for sparse channel estimation in [5], and the authors assumed that the massive channel is totally unknown and then exploited the inherent property of the correlation between the measurements and the sensing matrix to estimate the channel. To recover accurate channel state information for wideband mmWave MIMO system, a novel framework jointly exploiting the channel's low-rank and the angular information was proposed by E. Vlachos et al in [6].…”

Section: Introductionmentioning

confidence: 99%

Channel Estimation for MmWave Massive MIMO With Hybrid Precoding Based on Log-Sum Sparse Constraints

Zhang

Cao

Liu

et al. 2021

IEEE Trans. Circuits Syst. II

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Channel estimation is essential for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems with hybrid precoding. However, accurate channel estimation is a challenging task as the number of antennas is huge, while the number of RF chains is limited. Traditional methods of compressed sensing for channel estimation lead to serious loss of accuracy due to channel angle quantization. In this paper, we propose a new iterative reweight-based log-sum constraint channel estimation scheme. Specifically, we exploit the structure sparsity of the mmWave channels by formulating the channel estimation problem as an objective optimization problem. We utilize the log-sum as a constraint, via optimizing an objective function through the gradient descent method, the proposed algorithm can iteratively move the channel estimated angle-ofarrivals (AOAs) and angle-of-departures (AODs) towards the optimal solutions, and finally improve the angle estimation performance significantly. In addition, to ensure the accuracy of channel estimation, we introduce a dynamic regularization factor to control the tradeoff between the channel sparsity and the data fitting error. Numerical experiments demonstrate that the proposed algorithm achieves better convergence behavior than conventional sparse signal recovery solutions.

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Compressed channel estimation for FDD massive MIMO systems without prior knowledge of sparse channel model

Cited by 17 publications

References 26 publications

Statistical inference for the block sparsity of complex‐valued signals

Statistical inference for the block sparsity of complex‐valued signals

Channel estimation for FDD multi‐user massive MIMO systems: a greedy approach based on user clustering

Channel Estimation for MmWave Massive MIMO With Hybrid Precoding Based on Log-Sum Sparse Constraints

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