Two-dimensional DOA Estimation for Coprime Planar Array: A Coarray Tensor-based Solution

Zheng, Hang; Zhou, Chengwei; Gu, Yujie; Shi, Zhiguo

doi:10.1109/icassp40776.2020.9053860

Cited by 26 publications

(9 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the last decade, tensor modeling has been employed in a variety of signal processing problems [24]- [29], in particular to solve wireless communications related problems such as semi-blind receivers for MIMO systems [30], [31], channel estimation methods for cooperative communications [32], [33], direction of arrival estimation and beamforming in array processing [34]- [36], and, more recently, compressed channel estimation in massive MIMO systems [37], [38]. This paper links tensor modeling to IRS-based MIMO systems, and shows that exploiting the tensor structure of the received signals provides an effective way to solve the channel estimation problem.…”

Section: Introductionmentioning

confidence: 99%

Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach

Araújo

Almeida

Boyer

2021

IEEE J. Sel. Top. Signal Process.

170

165

View full text Add to dashboard Cite

Intelligent reflecting surface (IRS) is an emerging technology for future wireless communications including 5G and especially 6G. It consists of a large 2D array of (semi-)passive scattering elements that control the electromagnetic properties of radio-frequency waves so that the reflected signals add coherently at the intended receiver or destructively to reduce co-channel interference. The promised gains of IRS-assisted communications depend on the accuracy of the channel state information. In this paper, we address the receiver design for an IRS-assisted multiple-input multiple-output (MIMO) communication system via a tensor modeling approach aiming at the channel estimation problem using supervised (pilot-assisted) methods. Considering a structured time-domain pattern of pilots and IRS phase shifts, we present two channel estimation methods that rely on a parallel factor (PARAFAC) tensor modeling of the received signals. The first one has a closed-form solution by solving rank-1 matrix approximation problems, while the second one is based on an alternating estimation scheme. Uniqueness issues are discussed and design requirements that guide the choice of the system parameters as well as the structures of the pilot signals and IRS phase shifts are discussed. A performance analysis is also carried out by means of the Cramr-Rao lower bound. Numerical results show the effectiveness of the proposed receivers and highlight the involved tradeoffs, while corroborating their superior performance compared to competing solutions.

show abstract

Section: Introductionmentioning

confidence: 99%

Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach

Araújo

Almeida

Boyer

2021

IEEE J. Sel. Top. Signal Process.

170

165

View full text Add to dashboard Cite

show abstract

“…Remark For the one‐dimensional DOA estimation algorithm based on tensor, the construction method of the tensor estimation model based on the Hank matrix and the construction method of the tensor estimation model using the spatial smoothing idea are essentially the same [15, 16]. The former is mainly used here to facilitate readers' understanding.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…However, the newly emerged tensor-based DOA estimation algorithm is favoured by many scholars because of its ability to efficiently process high-dimensional data, strong denoising ability, and being applicable to single snapshot and multiple snapshots [14][15][16][17]. Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [16] proposed a novel coarray tensor‐based two‐dimensional underdetermined DOA estimation method for coprime planar array; the method effectively utilised both the multi‐dimensional information of the received signals and the augmented coarray to solve the problem of performance loss of matrix‐based coarray signal processing method in the potential characteristics of multi‐dimensional signal. Ref.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tensor‐based direction of arrival estimation with array virtual translation technique

Peng

Wang

Song

2022

IET Signal Processing

View full text Add to dashboard Cite

The tensor-based spatial smoothing is a good algorithm for the direction of arrival (DOA) estimation, but it cannot make good use of the physical aperture of the array, so the paper proposes a tensor-based array virtual translation DOA estimation algorithm to solve the problem. Under the framework of tensor-based DOA estimation algorithm, the factor matrix obtained by tensor decomposition is extended to a signal subspace with Vandermonde structure by using array virtual translation technology. In addition, the algorithm expands the available array aperture, breaks through the limitation of physical array aperture on multi-target estimation ability, and further improves the estimation accuracy. Since the processing technique proposed in this paper has nothing to do with the construction of tensors, this technique is suitable for conventional DOA estimation algorithms based on tensors. Theoretical analysis and simulation experiments verify the effectiveness of the algorithm proposed in this paper.

show abstract

“…Moreover, due to its inherent requirements for data sparsity, the number of estimable targets is less under a certain number of elements [13]. However, the newly emerged tensorbased DOA estimation algorithm is favored by many scholars because of its ability to efficiently process high-dimensional data, strong denoising ability, and being applicable to single snapshot and multiple snapshots [14,15,16,17,18,19,20]. Under certain conditions such as SNR and number of snapshots, this kind of algorithm can decompose the approximately pure steering vector matrix as the factor matrix of the estimation tensor model by using the low rank characteristics and internal structure of the received signal data, and then obtain the DOA information of the target by using the rotation invariance characteristics of Vandermonde matrix or other subspace-based DOA estimation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based DOA Estimation for Array Virtual Translation

Peng

2021

Preprint

View full text Add to dashboard Cite

In order to make up for the problem that the tensor-based spatial smoothing DOA estimation algorithm cannot make good use of the physical aperture of the array, this paper proposes a tensor-based array virtual translation DOA estimation algorithm. Under the framework of the tensor-based DOA estimation algorithm, the algorithm applies the array virtual translation technique to the factor matrix obtained after tensor decomposition, which can be expanded into signal subspace and approximately has a Vandermonde structure. Furthermore, the available array aperture of the algorithm is expanded, the estimation accuracy is improved, and the limitation of the physical array aperture on the algorithm’s multi-target estimation ability is broken. Since the processing technique proposed in this paper has nothing to do with the construction of tensors, this technique is suitable for all DOA estimation algorithms based on tensors. Theoretical analysis and numerical simulation verify the effectiveness of the algorithm proposed in this paper.

show abstract

Two-dimensional DOA Estimation for Coprime Planar Array: A Coarray Tensor-based Solution

Cited by 26 publications

References 30 publications

Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach

Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach

Tensor‐based direction of arrival estimation with array virtual translation technique

Tensor-Based DOA Estimation for Array Virtual Translation

Contact Info

Product

Resources

About