Joint Massive MIMO CSI Estimation and Feedback via Randomized Low-Rank Approximation

Wei, Ziping; Liu, Hongfu; Li, Bin; Zhao, Chenglin

doi:10.1109/tvt.2022.3167440

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…It should be noted that the number of pilot signals will seriously affect the channel overhead both in the uplink and downlink, where the complexity of CSI estimation and overhead of channel feedback are unbearable for channel when pilots are too long. Ref [18] develops one novel joint CSI estimation and feedback (JCEF) CSIT acquisition scheme by exploiting the random matrix approximation technique, which can effectively reduce the computational complexity of massive MIMO CSI acquisition. In [19], a unified UL/DL channel estimation strategy based on array antenna theory and DFT method is proposed, which require much lower overhead and time complexity.…”

Section: Introductionmentioning

confidence: 99%

Highly accurate millimeter wave channel estimation in massive MIMO system

Zhang

Qiao

et al. 2023

IET Communications

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Accurate channel state information (CSI) is extremely crucial to realize high-accurate hybrid precoding, in millimeter wave communication systems. In order to improve the CSI estimation performance, several traditional channel estimators have been developed by exploiting the sparse or low-rank property, whilst they bring huge channel training overhead and computational complexity. In this work, based on jointly sparse and low-rank property of massive multiple input multiple output (MIMO) channel, one non-convex mmWave channel estimation problem is formulated and a novel scheme to acquire one accurate CSI estimation result with greatly reduced training overhead is proposed. Specifically, the non-convex problem is reformulated as two simple sub-problems, by exploiting the alternating direction method of multipliers (ADMM) technique. Based on the low-rank characteristic, one fast gradient descent matrix completion algorithm is developed to accurately solve the first sub-problem. On this basis, the compressed sensing (CS) technique to acquire the accurate CSI estimation matrix is further utilized. Numerical simulation validates that the method could achieve the much higher channel estimation accuracy, yet only incurs the lower overhead compared with the traditional scheme.

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Section: Introductionmentioning

confidence: 99%

Highly accurate millimeter wave channel estimation in massive MIMO system

Zhang

Qiao

et al. 2023

IET Communications

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“…The RIS is a promising new method for boosting the efficiency of wireless networks 7 , 8 . The RIS has excellent potential for increasing spectrum efficiency and energy efficiency, especially for millimeter-wave (mmWave) communication systems, which are susceptible to high path loss and link blockage 1 , 2 .…”

Section: Introductionmentioning

confidence: 99%

“…As can be seen in 16 , these features make it an attractive candidate for inclusion in a wide range of communication systems. Single-cell multiple-output (MIMO) systems 17 – 21 and multicell MIMO communications 7 can all benefit from it, as can SWIPT systems 8 , 17 , secure communications 22 , mmWave systems 23 – 25 , and THz systems 22 , 26 . It can also be used in single-cell MIMO systems.…”

Section: Introductionmentioning

confidence: 99%

Channel estimation for reconfigurable intelligent surface-assisted mmWave based on Re‘nyi entropy function

Albataineh

Hayajneh

Shakhatreh

et al. 2022

Sci Rep

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This study focuses on channel estimation for reconfigurable intelligent surface (RIS)-assisted mmWave systems, in which the RIS is used to facilitate base-to-user data transfer. For beamforming to work with active and passive elements, a large-size cascade channel matrix should always be known. Low training costs are achieved by using the mmWave channels’ inherent sparsity. The research provides a unique compressive sensing-based channel estimation approach for reducing pilot overhead issues to a minimum. The proposed technique estimates channel data signals in a downlink for RIS-assisted mmWave systems. The mmWave systems often have a sparse distribution of signal sources due to the spatial correlations of the domains. This distribution pattern makes it possible to use compressive sensing methods to resolve the channel estimation issue. In order to decrease the pilot overhead, which is necessary to predict the channel, the proposed method extends the Re‘nyi entropy function as the sparsity-promoting regularizer. In contrast to conventional compressive sensing techniques, which necessitate an initial knowledge of the signal’s sparsity level, the presented method employs sparsity adaptive matching pursuit (SAMP) techniques to gradually determine the signal’s sparsity level. Furthermore, it introduces a threshold parameter based on the signal’s energy level to eliminate the sparsity level requirement. Extensive simulations show that the presented channel estimation approach surpasses the traditional OMP-based channel estimation methods in terms of normalized mean square error performance. In addition, the computational cost of channel estimation is lowered. Based on the simulations, our approach can estimate the channel well while reducing training overhead by a large amount.

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“…The complexity of channel estimation and the overhead of channel feedback will be unbearable when the pilot signal is too long. Ref [13] develops one novel joint CSI estimation and feedback (JCEF) CSIT acquisition scheme by exploiting the random matrix approximation technique. This scheme can effectively reduce the complexity of calculations.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Low-Complexity Channel State Information Estimation

Meng

Wei

Zhang

et al. 2022

Preprint

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In 5G communications, the acquisition of accurate channel state information (CSI) is of great importance to the hybrid beamforming of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. In classical mmWave MIMO channel estimation methods, the exploitation of inherent sparse or low-rank structures has demonstrated to improve the performance. However, most high-accurate CSI estimators incur a high computational complexity and require the prior channel information, which hence present the major challenges in the practical deployment. In this work, we leverage machine learning to design the low-complexity and high-performance channel estimator. To be specific, we first formulate the CSI estimation, in the case of sparse structure, as one classical least absolute shrinkage and selection operator (LASSO) problem. In order to reduce the time complexity of existing compressed sensing (CS) methods, we then approximate the original optimization problem to another one, by imposing the other low-rank constraint that was barely considered by CS. We thus solve this new approximated problem and attain the near-optimal solution of the original problem. One new method excludes any prior channel information, and greatly improves the estimation performance, which only incurs a low time complexity. Simulation results demonstrate the superiority of our proposed method both in the estimation accuracy and time complexity.

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Joint Massive MIMO CSI Estimation and Feedback via Randomized Low-Rank Approximation

Cited by 10 publications

References 24 publications

Highly accurate millimeter wave channel estimation in massive MIMO system

Highly accurate millimeter wave channel estimation in massive MIMO system

Channel estimation for reconfigurable intelligent surface-assisted mmWave based on Re‘nyi entropy function

Machine Learning Based Low-Complexity Channel State Information Estimation

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