Structured Tensor Decomposition-Based Channel Estimation for Wideband Millimeter Wave MIMO

Lin, Yuxing; Jin, Shi; Matthaiou, Michail; You, Xiaohu

doi:10.1109/ieeeconf44664.2019.9048762

Cited by 10 publications

(16 citation statements)

References 14 publications

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“…3 be a tensor with factor matrices A (n) ∈ C In×R , n ∈ {1, 2, 3}. Let A (1) and A (2) be Vandermonde matrices. Define X [2] = ( A (1) ⊙ A (2) ) ΛA (3)T as a matricization of X following (4), then S 1 ( X [2] ) = (…”

Section: B Spatial Smoothingmentioning

confidence: 99%

“…Let X ∈ C I 1 ×I 2 ×I 3 be a tensor with factor matrices A (n) ∈ C In×R , n ∈ {1, 2, 3}. 8 If kr(A (1) ) + kr(A (2) ) + kr(A (3)…”

Section: B Uniqueness Conditionmentioning

confidence: 99%

“…We turn to the realization of CP decomposition (25), which can be accomplished by solving min B (1) , B (2)…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…One may choose the pair (K 3 , L 3 ) as defined in Algorithm 1 such that the dimensions of the matrices B (K 3 ,3) ⊙B (2) and B (L 3 ,3) ⊙B (1) are similar, with the inequalities K 3 −1 ≥ ⌈L/P ⌉ , L 3 ≥ ⌈L/Q⌉ satisfied. At the cost of computational complexity, one can process the Y with multiple pairs of (K 3 , L 3 ) and calculate the average values of recovered parameters to reduce the variance of estimation error.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…We formulate the training signal as a trilinear tensor fitting the canonical polyadic (or CANDECOMP/PARAFAC, CP) model, where the factor matrices contain the multipath parameters [22], [23]. Expanding our prior work [1], we utilize the Vandermonde factor matrix and the spatial smoothing method to develop a Structured CP Decomposition-based channel estimation algorithm, abbreviated as SCPD [24], [25].…”

Section: Introductionmentioning

confidence: 99%

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Tensor-Based Channel Estimation for Millimeter Wave MIMO-OFDM With Dual-Wideband Effects

Lin

Jin

Matthaiou

et al. 2020

IEEE Trans. Commun.

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We consider the channel estimation problem in millimeter wave (mmWave) multiple-input multipleoutput orthogonal frequency division multiplexing (MIMO-OFDM) systems with hybrid analog-digital architectures. Leveraging the spatial-and frequency-wideband (dual-wideband) effects in massive MIMO scenarios, we derive a spatial-frequency channel model with dual-wideband effects that incorporates the multipath parameters, i.e., time delay, complex gain, angle of departure/arrival. We adopt a successive beam training scheme and formulate the training OFDM signal as a third-order low-rank tensor fitting a canonical polyadic (CP) model with factor matrices containing the channel parameters. Exploiting the Vandermonde nature of factor matrices, we propose a structured CP decomposition-based channel estimation strategy aided by the spatial smoothing method, where two dedicated algorithms with particular tensor modeling and parameter recovery operations are developed. The proposed scheme leverages standard linear algebra, and, hence, avoids the random initialization problem and iterative procedure. An analysis of the uniqueness condition of CP decomposition is also pursued. Simulation results indicate that the proposed strategy achieves enhanced estimation performance, which outperforms the traditional approaches in terms of accuracy, robustness and complexity.

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Section: B Spatial Smoothingmentioning

confidence: 99%

“…Let X ∈ C I 1 ×I 2 ×I 3 be a tensor with factor matrices A (n) ∈ C In×R , n ∈ {1, 2, 3}. 8 If kr(A (1) ) + kr(A (2) ) + kr(A (3)…”

Section: B Uniqueness Conditionmentioning

confidence: 99%

“…We turn to the realization of CP decomposition (25), which can be accomplished by solving min B (1) , B (2)…”

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tensor-Based Channel Estimation for Millimeter Wave MIMO-OFDM With Dual-Wideband Effects

Lin

Jin

Matthaiou

et al. 2020

IEEE Trans. Commun.

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Tensor Dictionary Manifold Learning for Channel Estimation and Interference Elimination of Multi-User Millimeter-Wave Massive MIMO Systems

et al. 2022

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Millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) systems with hybrid analog-digital architectures can greatly increase system capacity and communicate with multiple users at the same time. Accurate channel estimation is crucial for multi-user communications, but its accuracy is limited as the number of antennas and users increases. In the matrix high-dimensional operation for multi-user channel estimation, not only is it computationally intensive, but also the estimation accuracy is low. It makes our work turn to channel estimation of the user group within a certain region to improve the accuracy of estimation. In this paper, we propose a tensor dictionary manifold learning method for channel estimation and interference elimination of the multi-user mmWave massive MIMO system. A multi-user digital-analog mixed received signal model is presented. The tensor dictionary manifold learning scheme is proposed to model the received signal as a third-order low-rank tensor to handle the high-dimensional user, antenna, and channel. After segmentation, clustering and manifold learning, multiple tensor dictionary manifold models containing a group of user signals are fitted. Tensor dictionary manifold learning can take advantage of the inherent multi-domain properties of signals in the frequency, time, code and spatial domains to maintain inter-user correlation within a user group while reducing the high-dimensional channels of the user group. Using the convex relaxation property of the tensor alternating direction method, we propose a strategy to eliminate interference from other groups. And with the help of the multi-signal classification method, the channel parameters of user groups are obtained to improve the accuracy of multiuser channel estimation. This method can perform channel estimation for multiple users with only a few pilots, and improve the performance of the system. Numerical results confirm the good performance of this method.INDEX TERMS MIMO; channel estimation; tensor; dictionary learning; manifold; ADMM; MUSIC. I. INTRODUCTIONMassive multiple-input multiple-output (MIMO) is the key technology of the future 5G wireless communication system [1]. It configures a large number of antenna elements in a small space to obtain large multiplexing gain [2] and improve the capacity of wireless communication [3]. Therefore, the massive MIMO system supports communication with multiple users. However, the communication between multiple users needs to ensure strict service quality and requires advanced signal processing techniques [4], [5], such as channel estimation, channel equalization and channel coding. Channel

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Slow-Moving Channel Estimation via Vandermonde Structured Tensor Decomposition in RIS-Aided MIMO Systems

He,

Harms,

Yang

2024

IEEE Access

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This paper studies the problem of slow-moving channel estimation (CE) in the physical layer of a reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) communications system. Acquiring accurate channel state information (CSI) in such a system is one of the critical challenges due to a large number of RIS elements, especially under mobility with its characteristic Doppler effect. In this paper, we propose a channel estimation framework based on the tensor decomposition method to decouple the conventional cascaded channel. We formulate the uplink training signals as a third-order tensor, which admits a CANDECOMP/PARAFAC (CP) model with three factor matrices containing the CSI and phase information of the RIS. We further utilize the third factor matrix featured in the Vandermonde structure and decompose the received tensor signals with a higher rank into three factor matrices. Additionally, we develop algorithms to estimate parameters of the low-velocity user terminal (UT) -RIS channel from the resultant factor matrices. The numerical results indicate that our proposed tensor-based algorithms allow the estimation of individual UT -RIS and RIS -base station (BS) channels with expected accuracy and strong robustness.

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Structured Tensor Decomposition-Based Channel Estimation for Wideband Millimeter Wave MIMO

Cited by 10 publications

References 14 publications

Tensor-Based Channel Estimation for Millimeter Wave MIMO-OFDM With Dual-Wideband Effects

Tensor-Based Channel Estimation for Millimeter Wave MIMO-OFDM With Dual-Wideband Effects

Tensor Dictionary Manifold Learning for Channel Estimation and Interference Elimination of Multi-User Millimeter-Wave Massive MIMO Systems

Slow-Moving Channel Estimation via Vandermonde Structured Tensor Decomposition in RIS-Aided MIMO Systems

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