Robust tensor beamforming for polarization sensitive arrays

Liu, Long; Xie, Jian; Zhang, Zhaolin; Zhu, Yongjia

doi:10.1007/s11045-018-0580-6

Cited by 9 publications

(9 citation statements)

References 27 publications

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“…Therefore a judicious choice of R which balances the performance-complexity trade-off is preferred. end for 9: until convergence criterion triggers 10: Form tensor filter W using (11) and (13) By isolating w d,r from the other (D − 1) factors, we get:…”

Section: B Low-rank Tensor Mmse (Lr-tmmse) Filtermentioning

confidence: 99%

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Low-Rank Tensor MMSE Equalization

Ribeiro

Almeida

Mota

2019

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

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New-generation wireless communication systems will employ large-scale antenna arrays to satisfy the increasing capacity demand. This massive scenario brings new challenges to the channel equalization problem due to the increased signal processing complexity. We present a novel low-rank tensor equalizer to tackle the high computational demands of the classical linear approach. Specifically, we propose a method to design a canonical polyadic tensor filter to minimize the mean square error criterion. Our simulation results indicate that the proposed equalizer needs fewer calculations and is more robust to short training sequences than the benchmark.

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Section: B Low-rank Tensor Mmse (Lr-tmmse) Filtermentioning

confidence: 99%

“…11: w ← vec(W) Assuming structure(11), the filter coefficients can be written asw n1,...,nD = R r=1 D d=1 [w d,r ] n d ,which allows us to recast the equalizer output y[k] as follows y[k] = N1,...,ND n1,...,nD=1 w * n1,...,nD x n1,...,nD [kr ] * n1 . .…”

mentioning

confidence: 99%

Low-Rank Tensor MMSE Equalization

Ribeiro

Almeida

Mota

2019

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

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“…Apart from the abovementioned algorithms, the last few years have also witnessed the growing popularity of tensor operations in the field of signal processing owing to their advantage of being inherently multidimensional. Previous studies [9][10][11] introduced the technique of array beamforming, which is based on the decomposition of multidimensional signal tensor models. For example, in [11], methods using tensor operations were investigated to improve the robustness of array beamforming.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies [9][10][11] introduced the technique of array beamforming, which is based on the decomposition of multidimensional signal tensor models. For example, in [11], methods using tensor operations were investigated to improve the robustness of array beamforming. Therein, a tensor decomposition method, which is mainly used to construct a distortionless response model with a minimum variance, was incorporated into the improved conjugate gradient least squares (CGLS) method to achieve better robustness.…”

Section: Introductionmentioning

confidence: 99%

Signal parameter estimation through hierarchical conjugate gradient least squares applied to tensor decomposition

et al. 2020

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A hierarchical iterative algorithm for the canonical polyadic decomposition (CPD) of tensors is proposed by improving the traditional conjugate gradient least squares (CGLS) method. Methods based on algebraic operations are investigated with the objective of estimating the direction of arrival (DoA) and polarization parameters of signals impinging on an array with electromagnetic (EM) vector‐sensors. The proposed algorithm adopts a hierarchical iterative strategy, which enables the algorithm to obtain a fast recovery for the highly collinear factor matrix. Moreover, considering the same accuracy threshold, the proposed algorithm can achieve faster convergence compared with the alternating least squares (ALS) algorithm wherein the highly collinear factor matrix is absent. The results reveal that the proposed algorithm can achieve better performance under the condition of fewer snapshots, compared with the ALS‐based algorithm and the algorithm based on generalized eigenvalue decomposition (GEVD). Furthermore, with regard to an array with a small number of sensors, the observed advantage in estimating the DoA and polarization parameters of the signal is notable.

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“…We observe that our approach yields better system identification accuracy. Other classical signal processing algorithms such as the generalized sidelobe canceler and the minimum variance distortionless beamformers have been implemented by separable filters in [9] and [10], respectively. The works previously mentioned reveal that separable filters can drastically reduce the computational costs with small performance degradation at massive filtering problems.…”

Section: Introductionmentioning

confidence: 99%

Separable Least-Mean Squares Beamforming

Ribeiro¹,

Sokal²,

Almeida³

et al. 2018

Anais De XXXVI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Large-scale antenna systems have attracted much research efforts due to its application to modern mobile communications systems. Designing such arrays, however, can be challenging due to the computational efforts of classical signal processing methods. To tackle this issue, we investigate Kroneckerseparable adaptive filtering methods. Simulation results suggest that this approach exhibits better convergence properties in some scenarios compared to the classical adaptive filtering benchmark.

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Robust tensor beamforming for polarization sensitive arrays

Cited by 9 publications

References 27 publications

Low-Rank Tensor MMSE Equalization

Low-Rank Tensor MMSE Equalization

Signal parameter estimation through hierarchical conjugate gradient least squares applied to tensor decomposition

Separable Least-Mean Squares Beamforming

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