Localized Pseudo-Skeleton Approximation Method for Electromagnetic Analysis on Electrically Large Objects

Zhang, Yong; Lin, Hai

doi:10.2528/pierl15070601

Cited by 5 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, for the application of energy-efficient management in IoT networks, a low-rank matrix with incomplete sampling values is recovered based on SVD [29]. Considering the physical description of data, a pseudo-skeleton approximation technique called matrix-CUR decomposition is applied to nearfield compression and electromagnetic analysis, where C and R represent the actual columns and rows from the data matrix respectively, and U is a constructed matrix to guarantee the approximation [18], [30], [31]. Moreover, a tensor is obtained by a volume sampling and estimation technique based on tensor singular value decomposition (t-SVD) [32], [33].…”

Section: A Data-driven Methods For Near-field Sampling and Reconstruc...mentioning

confidence: 99%

Model-Driven Compressive Spherical Array Sampling of Base-Station-Antenna Near Field Using Accelerated Pseudo-Skeleton Tensor Approximation

Sun¹,

Wu²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>Spherical array sampling plays an important role in obtaining near-field samples (NFSs) of base-station antennas for wireless internet of things performance analysis and optimization. Due to the large sampling area that a semicircular array of probes (SAP) has to cover, spherical array sampling is often time-consuming. In order to reduce sampling time, this paper proposes a model-driven compressive spherical array sampling technique based on pseudo-skeleton tensor approximation. In the proposed method, the Huygens’ principle and the method of moments are applied to construct a mapping tensor firstly, which relates the vector consisting of Huygens’ equivalent source samples with the matrix constituted by spherical near-field samples. Then by analyzing the physical meaning of the mapping tensor, it is revealed that skeleton sampling positions of SAP could be obtained by searching skeleton slices of the mapping tensor. The skeletonization process provides the relationship between NFSs in skeleton and non-skeleton sampling positions. This means NFSs in non-skeleton sampling positions can be computed from those in skeleton sampling positions, and the sampling effort is thus reduced. Moreover, in order to efficiently search skeleton slices of the mapping tensor, a fast ordered-subset selection algorithm is developed. Numerical experiments show that the proposed sampling method can reduce the number of samples by 50%, and the proposed fast algorithm is 22 times faster than traditional algorithms from the perspective of computation efficiency. Since the proposed sampling method is model-driven, it is independent of sampling data, which makes the method attractive to multi-beam multiple-input multiple-output (MIMO) antennas.</p>

show abstract

Section: A Data-driven Methods For Near-field Sampling and Reconstruc...mentioning

confidence: 99%

Model-Driven Compressive Spherical Array Sampling of Base-Station-Antenna Near Field Using Accelerated Pseudo-Skeleton Tensor Approximation

Sun¹,

Wu²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This process is carried out iteratively until p rows and columns are found and stored. Please refer to [17] for more information.…”

Section: 2mentioning

confidence: 99%

Efficient and Memory Saving Method Based on Pseudoskeleton Approximation for Analysis of Finite Periodic Structures

Luo

Zhang

Lin

2018

International Journal of Antennas and Propagation

Self Cite

View full text Add to dashboard Cite

An efficient and memory saving method based on pseudoskeleton approximation (PSA) is presented for the effective and accurate analysis of finite periodic structures. Different from the macro basis function analysis model, our proposed method uses the formulations derived by the local Rao-Wilton-Glisson basis functions. PSA is not only used to accelerate the matrix-vector product (MVP) inside the single unit but also adopted to decrease the calculation burden of the coupling between the different cells. Moreover, the number of decomposed coupling matrices is minimized due to the displacement invariance of the periodic property. Consequently, even compared with the multilevel fast multipole algorithm (MLFMA), the new method saves much more memory resources and computation time, which is also demonstrated by the numerical examples.

show abstract

A GPU parallel randomized CUR compression method for the Method of Moments

Lopez-Menchon

Heldring

Úbeda

et al. 2023

Computer Physics Communications

View full text Add to dashboard Cite

Localized Pseudo-Skeleton Approximation Method for Electromagnetic Analysis on Electrically Large Objects

Cited by 5 publications

References 11 publications

Model-Driven Compressive Spherical Array Sampling of Base-Station-Antenna Near Field Using Accelerated Pseudo-Skeleton Tensor Approximation

Model-Driven Compressive Spherical Array Sampling of Base-Station-Antenna Near Field Using Accelerated Pseudo-Skeleton Tensor Approximation

Efficient and Memory Saving Method Based on Pseudoskeleton Approximation for Analysis of Finite Periodic Structures

A GPU parallel randomized CUR compression method for the Method of Moments

Contact Info

Product

Resources

About