In this paper, a multiscale compressed and spliced Sherman-Morrison-Woodbury algorithm (MSCS-SMWA), based on the characteristic basis function method (CBFM), has been presented to accelerate direct solutions of electromagnetic scattering. The impedance matrix or reduced matrix can be compressed into block diagonal matrices with the adaptive cross approximation (ACA) and easily obtained the matrix inverse with this method. The MSCS-SMWA method is combined with a multiscale compressed ACA and a multiscale spliced singular value decomposition (SVD). The numerical results of several perfect electric conducting (PEC) objects are validated to demonstrate the performance of this method. Compared with the conventional methods, SMWA with the ACA and CBFM, the simulation results of the proposal method have been shown that the computation time and storage requirements have reduced about 10.77% and 44.22%, for the sphere with 10λ radius. Also, it costs 49.16% less CPU time and 1.54% less memory than the conventional method with SVD.

The design of periodic structures is a very time-consuming process because it requires many cycles of full-wave simulations to obtain the desired electromagnetic (EM) properties. This challenge is amplified when designing reconfigurable periodic structures. This article proposes an intelligent method for the reconfigurable periodic structure design (RPSD) based on machine learning. First, an improved physical and mathematical modeling approach is proposed for reconfigurable periodic structures, which enables the application of machine learning in RPSD. Thereafter, we set up distributed predictors for different reconfigurable states to avoid the one-to-many mapping problem. In addition, the networks of these predictors are trained using variational autoencoders (VAEs) to reduce the associated learning difficulties. These enable the obtention of accurate predictors with small sample data. Moreover, the training of these predictors can be easily accelerated by parallel computation because of their independence. Finally, reconfigurable periodic structures can be intelligently designed by the combination of the trained predictors and a stochastic optimization technique. Some examples are provided to verify the practicality and accuracy of the proposed method.

In this article, a nested fast adaptive cross approximation (NFACA) algorithm is presented to accelerate the solution of electromagnetic scattering problems. It is based on the fast adaptive cross approximation (FACA) and the nested cross approximation (NCA). In the NFACA, the FACA is applied instead of the ACA or the Chebyshev nodes, to enhance the efficiency. It is shown to have O(N) storage and computational complexity for electrically small problems. Different from the conventional NCA or the fast NCA (FNCA), the pivot selection does not rely on a preliminary selection with Chebyshev nodes or ACA for each group. The NFACA permits the selection of the optimum pivots independently at each level to make the accuracy controllable. Numerical results of several objects are presented to demonstrate the performance of this method. Compared with the FACA and NCA in solving the electromagnetic scattering problems, the NFACA is more efficient both in computational cost and storage. Compared with the FNCA, the NFACA is both faster and more accurate.

In this letter, an accelerated fast direct solver based on the multiscale compressed block decomposition (MSCBD) and the multilevel characteristic basis function method (MLCBFM) is presented to analyze electrically large problems. The MSCBD is used to enhance the efficiency of the characteristic basis functions generation to accelerate the process of linear system solving in the MLCBFM. In addition, the MLCBFM can reduce the number of unknowns and improve the efficiency of MSCBD process. That means the MSCBD and MLCBFM can accelerate each other. The correctness and efficiency of the method are demonstrated with numerical results for several perfect electric conducting objects, by comparing with other methods.

The simultaneous use of multiple devices for covert communication with the assistance of UAVs can easily lead to device matching errors, resulting in information leakage and resource wastage. To address this issue, we propose a tripartite matching game model in UAV-assisted covert communication network (T-UCN). The model incorporates the reverse VCG auction and second-price sealed auction algorithms while considering computational efficiency, Nash equilibrium, and Individual rationality. Finally, simulation verifies the effectiveness and robustness of the algorithms we propose in T-UCN.

This article addresses the efficient numerical analysis of sparsely distributed small modifications in a large structure, that is, the sparsely distributed partial modification problem (SDPMP). A hierarchical matrix (H-matrix)-based fast direct solver is adopted to solve the SDPMP in a very short time for each new set of modifications. The main idea of this method is to reuse the parts of the compressed inverse matrix operator that do not change and only recompute the modified parts. In contrast with already existing algorithms designed to tackle a single localized modification, this new approach is still very fast for sparsely distributed small modifications, so it becomes a very attractive option to solve optimization problems efficiently, due to the very cheap analysis of the modified structure at each iteration of the optimization procedure. The efficiency of the solution of SDPMP is demonstrated for several cases involving passive and active metasurfaces and a reconfigurable antenna. As a practical example, a programmable metasurface reflector is optimized with particle swarm optimization to obtain a prescribed reflection pattern.