Pub Date : 2024-09-02DOI: 10.1109/TAP.2024.3450298
Chenbo Shi;Jin Pan;Xin Gu;Shichen Liang;Le Zuo
This article presents a novel approach for computing substructure characteristic modes. This method leverages electromagnetic (EM) scattering matrices and spherical wave expansion to directly decompose EM fields. Unlike conventional methods that rely on the impedance matrix generated by the method of moments (MoMs), our technique simplifies the problem into a small-scale ordinary eigenvalue problem, improving numerical dynamics and computational efficiency. We have developed analytical substructure characteristic mode solutions for a scenario involving two spheres, which can serve as benchmarks for evaluating other numerical solvers. A key advantage of our method is its independence from specific MoM frameworks, allowing for the use of various numerical methods. This flexibility paves the way for substructure characteristic mode decomposition to become a universal frequency-domain technique.
{"title":"Scattering-Based Characteristic Mode Theory for Structures in Arbitrary Background: Computation, Benchmarks, and Applications","authors":"Chenbo Shi;Jin Pan;Xin Gu;Shichen Liang;Le Zuo","doi":"10.1109/TAP.2024.3450298","DOIUrl":"10.1109/TAP.2024.3450298","url":null,"abstract":"This article presents a novel approach for computing substructure characteristic modes. This method leverages electromagnetic (EM) scattering matrices and spherical wave expansion to directly decompose EM fields. Unlike conventional methods that rely on the impedance matrix generated by the method of moments (MoMs), our technique simplifies the problem into a small-scale ordinary eigenvalue problem, improving numerical dynamics and computational efficiency. We have developed analytical substructure characteristic mode solutions for a scenario involving two spheres, which can serve as benchmarks for evaluating other numerical solvers. A key advantage of our method is its independence from specific MoM frameworks, allowing for the use of various numerical methods. This flexibility paves the way for substructure characteristic mode decomposition to become a universal frequency-domain technique.","PeriodicalId":13102,"journal":{"name":"IEEE Transactions on Antennas and Propagation","volume":"72 10","pages":"7860-7871"},"PeriodicalIF":4.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1109/TAP.2024.3450317
Keshav Sewraj;Matthys M. Botha
The efficient method of moments (MoM) analysis of very large antenna arrays of disjoint elements, using macro basis function (MBF) schemes, is considered. Directional cross approximation (DCA), which is a nested, multilevel, algebraic, low-rank factorization scheme suitable for electrically large structures, is used for fast reduced MBF matrix setup and matrix-vector products (MVPs). A DCA far-field sampling strategy suitable for planar arrays is employed. Optimal log-linear DCA memory scaling is demonstrated. The performance of static MBF formulations is investigated, namely, the characteristic basis function method (CBFM) and windowed MBF (WMBF) schemes, which establish MBFs once as a preprocessing step. Static MBF approximation errors are difficult to control. Dynamic MBFs are iteratively refined to obtain a solution within user-specified error tolerance. Residual-driven (RD) CBFM, RD WMBFs, RD Krylov subspace MBFs, and block-Jacobi MBFs (both original and RD) are considered. Effective solution accuracy control is demonstrated. Runtime of all schemes is studied. Given optimal DCA acceleration, the results give a realistic view of relative efficiencies. Static MBFs are much less efficient than dynamic ones. Among dynamic schemes, RD static MBFs are less efficient. Krylov MBFs can perform better than the original block-Jacobi scheme, but the latter requires no parameter choice. RD block-Jacobi and a hybrid Krylov-Jacobi (K-J) scheme sometimes outperform all others.
{"title":"Macro Basis Function Methods With Multilevel DCA Acceleration for Antenna Array Analysis","authors":"Keshav Sewraj;Matthys M. Botha","doi":"10.1109/TAP.2024.3450317","DOIUrl":"10.1109/TAP.2024.3450317","url":null,"abstract":"The efficient method of moments (MoM) analysis of very large antenna arrays of disjoint elements, using macro basis function (MBF) schemes, is considered. Directional cross approximation (DCA), which is a nested, multilevel, algebraic, low-rank factorization scheme suitable for electrically large structures, is used for fast reduced MBF matrix setup and matrix-vector products (MVPs). A DCA far-field sampling strategy suitable for planar arrays is employed. Optimal log-linear DCA memory scaling is demonstrated. The performance of static MBF formulations is investigated, namely, the characteristic basis function method (CBFM) and windowed MBF (WMBF) schemes, which establish MBFs once as a preprocessing step. Static MBF approximation errors are difficult to control. Dynamic MBFs are iteratively refined to obtain a solution within user-specified error tolerance. Residual-driven (RD) CBFM, RD WMBFs, RD Krylov subspace MBFs, and block-Jacobi MBFs (both original and RD) are considered. Effective solution accuracy control is demonstrated. Runtime of all schemes is studied. Given optimal DCA acceleration, the results give a realistic view of relative efficiencies. Static MBFs are much less efficient than dynamic ones. Among dynamic schemes, RD static MBFs are less efficient. Krylov MBFs can perform better than the original block-Jacobi scheme, but the latter requires no parameter choice. RD block-Jacobi and a hybrid Krylov-Jacobi (K-J) scheme sometimes outperform all others.","PeriodicalId":13102,"journal":{"name":"IEEE Transactions on Antennas and Propagation","volume":"72 11","pages":"8621-8634"},"PeriodicalIF":4.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1109/TAP.2024.3450328
Miao Wang;Shilong Sun;Dahai Dai;Yongsheng Zhang;Yi Su
In this article, we have improved the quantitative inversion performance of the cross-correlated contrast source inversion (CC-CSI) method by incorporating the subspace optimization strategy. The proposed method is called the cross-correlated subspace optimization method (CC-SOM). Meanwhile, multifrequency data are used to improve the inversion performance of high-contrast scatterers, where the L-curve method is introduced to select the regularization parameters of each frequency point without relying on experience. Finally, a fast algorithm is implemented by using the property of singular value decomposition (SVD) to simplify the large-scale matrix, and the fast Fourier transform (FFT) to accelerate the calculation. Synthetic and experimental inversion results demonstrate that both CC-SOM and CC-CSI show better robustness than SOM. In comparison to CC-CSI, CC-SOM is superior in terms of inversion accuracy when the regularization parameters have been appropriately selected. However, these advantages come at the cost of higher computational complexity.
在本文中,我们通过加入子空间优化策略,提高了交叉相关对比源反演(CC-CSI)方法的定量反演性能。所提出的方法被称为交叉相关子空间优化方法(CC-SOM)。同时,利用多频数据来提高高对比度散射体的反演性能,其中引入了 L 曲线方法来选择各频点的正则化参数,而无需依赖经验。最后,利用奇异值分解(SVD)简化大规模矩阵的特性和快速傅立叶变换(FFT)加速计算的特性,实现了一种快速算法。合成和实验反演结果表明,CC-SOM 和 CC-CSI 都比 SOM 表现出更好的鲁棒性。与 CC-CSI 相比,如果正则化参数选择得当,CC-SOM 在反演精度方面更胜一筹。然而,这些优势是以更高的计算复杂度为代价的。
{"title":"Cross-Correlated Subspace-Based Optimization Method for Solving Electromagnetic Inverse Scattering Problems","authors":"Miao Wang;Shilong Sun;Dahai Dai;Yongsheng Zhang;Yi Su","doi":"10.1109/TAP.2024.3450328","DOIUrl":"10.1109/TAP.2024.3450328","url":null,"abstract":"In this article, we have improved the quantitative inversion performance of the cross-correlated contrast source inversion (CC-CSI) method by incorporating the subspace optimization strategy. The proposed method is called the cross-correlated subspace optimization method (CC-SOM). Meanwhile, multifrequency data are used to improve the inversion performance of high-contrast scatterers, where the L-curve method is introduced to select the regularization parameters of each frequency point without relying on experience. Finally, a fast algorithm is implemented by using the property of singular value decomposition (SVD) to simplify the large-scale matrix, and the fast Fourier transform (FFT) to accelerate the calculation. Synthetic and experimental inversion results demonstrate that both CC-SOM and CC-CSI show better robustness than SOM. In comparison to CC-CSI, CC-SOM is superior in terms of inversion accuracy when the regularization parameters have been appropriately selected. However, these advantages come at the cost of higher computational complexity.","PeriodicalId":13102,"journal":{"name":"IEEE Transactions on Antennas and Propagation","volume":"72 11","pages":"8575-8589"},"PeriodicalIF":4.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1109/tap.2024.3450308
Riku Takahashi, Anirban Ghosh, Minghe Mao, Minseok Kim
{"title":"Channel Modeling and Characterization of Access, D2D and Backhaul Links in a Corridor Environment at 300 GHz","authors":"Riku Takahashi, Anirban Ghosh, Minghe Mao, Minseok Kim","doi":"10.1109/tap.2024.3450308","DOIUrl":"https://doi.org/10.1109/tap.2024.3450308","url":null,"abstract":"","PeriodicalId":13102,"journal":{"name":"IEEE Transactions on Antennas and Propagation","volume":"163 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1109/TAP.2024.3450304
Yu Luo;Shuaijie Duan;Zhi Ning Chen;Ningning Yan;Wenxing An;Kaixue Ma
An efficient beamforming synthesis method is proposed for high-order-mode dipoles using artificial neural networks (ANNs). Beamformed radiation pattern features and antenna parameters are set as the inputs and outputs of an ANN model to expedite antenna design by reducing the complexity and training volume of ANN. The flat-top beamforming of compressed high-order-mode dipoles is used as an example to validate the proposed beamforming synthesis method based on a proposed continuous current source over a high-order-mode dipole with the current distribution determined by designed compression coefficients. Then, the desired compression coefficients are implemented using a meandered structure. The numerical results indicate that the ANN can achieve a training loss of $1.16times 10^{-4}$