It is the pursuit of a multiple-input-multiple-output (MIMO) system to approach and even break the limit of channel capacity. However, it is always a big challenge to efficiently characterize the MIMO systems in complex space and get better propagation performance than the conventional MIMO systems considering only free space, which is important for guiding the power and phase allocation of antenna units. In this manuscript, an Electromagnetic-Information-Theory (EMIT) based model is developed for efficient characterization of MIMO systems in complex space. The group-T-matrix-based multiple scattering fast algorithm, the mode-decomposition-based characterization method, and their joint theoretical framework in complex space are discussed. Firstly, key informatics parameters in free electromagnetic space based on a dyadic Green’s function are derived. Next, a novel group-T-matrix-based multiple scattering fast algorithm is developed to describe a representative inhomogeneous electromagnetic space. All the analytical results are validated by simulations. In addition, the complete form of the EMIT-based model is proposed to derive the informatics parameters frequently used in electromagnetic propagation, through integrating the mode analysis method with the dyadic Green’s function matrix. Finally, as a proof-or-concept, microwave anechoic chamber measurements of a cylindrical array is performed, demonstrating the effectiveness of the EMIT-based model. Meanwhile, a case of image transmission with limited power is presented to illustrate how to use this EMIT-based model to guide the power and phase allocation of antenna units for real MIMO applications.

This article presents a new optimization method for complex power distribution networks (PDNs) with irregular shapes and multilayer structures using deep reinforcement learning (DRL), which has not been considered before. A fast boundary integration method is applied to compute the impedance matrix of a PDN structure. Subsequently, a new DRL algorithm based on proximal policy optimization (PPO) is proposed to optimize the decoupling capacitor (decap) placement by minimizing the number of decaps while satisfying the desired target impedance. In the proposed approach, the PDN structure information is encoded into matrices and serves as the input of the DRL algorithm, which increases the flexibility of the method to be extended and generalized to different PDN configurations. Also, the output of the algorithm determines the selection of decap types and locations collaboratively, making it easier to find the optimal solution in a huge search space. The proposed method is compared with the state-of-the-art approaches and shows consistent advantages in reducing the number of decaps in different testing cases.

Selection of decoupling capacitors (decaps) is important for power distribution network (PDN) design in terms of lowering impedance and saving cost. Good PDN designs typically mean satisfying a target impedance with as less decaps as possible. In this paper, an inductance-based method is utilized to calculate the port priority fist, and afterwards deep reinforcement learning (DRL) with deep neural network (DNN) is applied to optimize the assignment of decaps on the prioritized locations. The DRL algorithm can explore by itself without any prior physical knowledge, and the DNN is trained with the exploration experience and eventually converges to an optimum state. The proposed hybrid method was tested on a printed-circuit-board (PCB) example. After some iterations of training the DNN successfully reached to an optimum design, which turned out to be the minimum number of decaps that can satisfy the target impedance. The usage of DRL with DNN makes the algorithm promising to include more variables as input and handle more complicated cases in the future.

This article presents an efficient methodology based on boundary integration to calculate the dc and ac impedance of power distribution networks (PDNs) for arbitrary-shape and multilayer printed circuit boards (PCBs). The proposed method adopts a boundary element method to extract inductances for the arbitrary parallel-plane shapes. Subsequently, the equivalent circuit formed by the via inductances and plane capacitances is solved using the node voltage method. By merging the parallel and serial inductances and simplifying the equivalent circuit, the computation time can be significantly reduced for a large number of vias and layers. This matrix manipulation strategy can be applied to various PCB structures without human intervention or commercial circuit solvers. Moreover, a contour integral method is employed to calculate the dc resistances for arbitrary shape and multilayer PDNs. Therefore, the wideband PDN impedance from dc to ac frequencies can be efficiently calculated through 1-D boundary integration, which can be performed more quickly than full-wave simulations. The proposed method can aid in the development of electronic design automation tools for PDN design.

This article proposes a broadband inverse analysis method for the higher order modes inside the resistively loaded monocone transverse electromagnetic (TEM) cell. The analytic solutions of the field pattern for each higher order mode are derived in the frequency domain. The deep learning technique is employed to effectively predict coefficients of these modes over a wide frequency range (1.885–300 MHz) only given the $E$ -field amplitude distribution. The predicted results agree well with measurement ones, which validates the accuracy of the deep neural network method, and furthermore, it is analyzed in the time domain with fast Fourier transform (FFT). This work reveals the mechanism of higher order mode-induced field distortion in both the frequency domain of broadband and the time domain, provides guidance for elimination of higher order modes in monocone TEM cells, and can be extended to mode analysis for other kinds of wideband antennas.

This article presents a novel and efficient batch data selection method based on active and unsupervised learning in real-time near-field scanning. The new approach shows a remarkable advantage over random sampling in reducing the number of scanning data samples and the total scanning time. Moreover, careful hyperparameter tuning is unnecessary, as the performance is insensitive to the hyperparameter values. After randomly selecting some initial points, three key steps are sequentially and iteratively implemented. First, the query-by-committee (QBC) method is adopted to evaluate the uncertainties of the unobserved positions using different interpolation functions and select an “uncertain” group with the largest variances. Second, the weighted K-means clustering (WKMC) method divides the “uncertain” group into multiple clusters and selects the representative samples by choosing the most uncertain point in each cluster to ensure diversity. Finally, an estimated variance change (EVC) method is proposed to select the most representative samples to enhance diversity further. The proposed approach of this article, referred to as the QWE (QBC $+$ WKMC $+$ EVC) method, can well balance uncertainty and diversity with high efficiency. The newly proposed QWE approach is validated in both simulations and measurements of near-field scanning. The batch data selection method of this article can be extended to other multidimensional regression modeling problems in which data acquisition is expensive.