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In this paper, a novel method is proposed to design a hybrid network (HN) to increase isolation and shape radiation patterns for multiple-input multiple-output (MIMO) antenna systems. The HN is a combination of a decoupling feeding network (DFN) and a defected ground network (DGN), which are populated by several surface-mounted reactive components whose reactances are determined by the N-ary optimization algorithm. Two decoupling examples are presented to validate the design methodology and elaborate on the design procedure. Measurement results show that the HN helps to realize impedance matching with reflection coefficients below -10 dB, isolation improvement from -5.4 dB/-8.9 dB to below -20 dB, and low envelope correlation coefficient below 0.06 for MIMO antennas with the element separation of 0.160/0.240. Moreover, in both examples, the decoupling case with omnidirectional radiation patterns achieves a better throughput performance, compared with another decoupling case with directional radiation. In comparison to prior decoupling networks only focusing on isolation enhancement, the proposed HN achieves high isolation and desired radiation patterns simultaneously with a very accurate design methodology.

This paper presents a novel method to design multiple-input multiple-output (MIMO) antennas based on characteristic mode analysis (CMA), which is different from utilizing individual excitation of each characteristic mode for high isolation. The CMA is performed to design a pair of symmetric slots on two connected dipole antennas (a MIMO dipole pair), so that two desired characteristic modes can be generated at the same frequency, one of which is in common mode and the other in differential mode. Hence, when one antenna element is excited, the two characteristic modes combine and cancel each other at the other antenna element, leading to high port isolation. The designed dipole pair is applied for use in 2G/3G/4G MIMO base-station arrays. Simulation and measurement results show that the antenna achieves a wide impedance bandwidth of 1.7-2.7 GHz (45.5%), low coupling below -20 dB, low cross-polarization below -20 dB, high efficiencies over 85%, and stable radiation patterns within the entire band. The results verified the advances of the proposed decoupling method, i.e., simplicity, effectiveness, wide bandwidth, good radiation, etc.

The sea clutter, referred to as the time-varying radar backscatter from the ocean surface, plays a significant role in marine radar development. The ocean's complex hydrodynamics cause it to exhibit non-Gaussian and nonstationary characteristics, which brings challenges in the sea clutter modeling, especially for establishing its spatial-temporal correlated and coherent model. In this article, a data-driven method based on the Koopman mode decomposition (KMD) is proposed for modeling spatial-temporal correlated complex sea clutter. The method decomposes the coherent sea clutter dynamic behavior in terms of Koopman modes and corresponding temporal patterns. Then, these spatiotemporal patterns are used to construct the sea clutter state over time according to the approximate solution. Furthermore, this proposed state-of-the-art data-driven approach is benchmarked by the measured sea clutter data from intelligent PIXel processing radar (IPIX). It is demonstrated that the proposed approach accurately models the complex sea clutter with actual statistic characteristics, phase information, and spatial-temporal correlations. The mean absolute error (MAE) and root mean square error (RMSE) between the obtained and actual sea clutter are only 0.1817 and 0.2349, respectively. This work offers a practical approach for modeling sea clutter, especially when the spatial-temporal correlation and coherence information is needed.

In this work, a novel Robin transmission condition (RTC) enhanced discontinuous Galerkin (DG) method is proposed for the DC IR-Drop analysis of power distribution networks with Joule Heating effects included. Unlike the conventional DG method, the proposed DG method straightforwardly applied to discretize the second-order spatial partial differential governing equations for the electrostatic potential Φ and the steady-state temperature T, respectively. The numerical flux in DG used to facilitate the information exchange among neighboring subdomains introduces two additional variables: the current density J for the electrical potential equation and the thermal flux q for the thermal equation. To solve them, at the interface of neighboring subdomains a RTC is presented as the second equation to establish another connection for solutions in neighboring subdomains. With this strategy, the number of degrees of freedom (DoF) involved in the proposed RTC-DG method is dramatically reduced compared with the traditional DG method. The finalized matrix system is solved in a FETI-like procedure, namely, the unknowns are obtained in a subdomain-by-subdomain scheme. Finally, the accuracy and the efficiency of the proposed RTC-DG method is validated by serval representative examples.

Deep learning is applied to assist the joint inversion for audio-magnetotelluric and seismic travel time data. More specifically, deep residual convolutional neural networks (DRCNNs) are designed to learn both structural similarity and resistivity–velocity relationships according to prior knowledge. During the inversion, the unknown resistivity and velocity are updated alternatingly with the Gauss–Newton method, based on the reference model generated by the trained DRCNNs. The workflow of this joint inversion scheme and the design of the DRCNNs are explained in detail. Compared with describing the resistivity–velocity relationship using empirical equations, this method can avoid the necessity in modeling the correlations in rigorous mathematical forms and extract more hidden prior information embedded in the training set, meanwhile preserving the structural similarity between different inverted models. Numerical tests show that the inverted resistivity and velocity have similar profiles, and their relationship can be kept consistent with the prior joint distribution. Furthermore, the convergence is faster, and final data misfits can be lower than separate inversion.

The orbital angular momentum (OAM) based communication is a promising technology in the wireless communication system for its improving spectrum efficiency. However, for the OAM demultiplexing, it requires a very large aperture size in the receiving end due to the OAM beam divergence and the amplitude null in the beam center, which restricts the full development of OAM based communication. In this paper, we propose a novel partial arc sampling receiving (PASR) scheme based on the augmented dynamic mode decomposition (ADMD) to solve this issue. We explore the use of the phase delay coordinates in the conventional DMD to sort the arbitrary OAM modes with arc sampling receiving. Simulation results demonstrate the accuracy and robustness of the proposed approach in sorting both integral and fractional OAM modes with the PASR scheme. Our work offers a practical approach for demultiplexing OAM beams in radio communication systems with fewer receiving devices, especially for the long-distance transmission case.

In this communication, a novel dipole configuration with reactive loadings is proposed for the design of low-profile dual-band dual-polarized stacked antenna arrays. For lower band (LB1, 3.3–3.6 GHz) dipole, its inductive loadings are implemented at the end of each dipole arm to suppress the basic resonant mode for band rejection at higher frequencies. For higher band (HB2, 4.8–5.0 GHz) dipole, its LC loadings are implemented at the beginning of each dipole arm, where the inductor and the capacitor, respectively, increase the $Q$ -factor for improving the out-of-band rejection and balance the impedance matching. Hence, LB1 and HB2 dipoles can be closely stacked with high isolation. Moreover, a dual-band artificial magnetic conductor (AMC) reflector is adopted for reducing the distance between the antenna and the ground plane. Consequently, the overall height of the proposed one is only 0.16 $\lambda _{L}$ ( $\lambda _{L}$ being the free-space wavelength at the center frequency of LB1), which is smaller than the previous dual-band stacked designs. The measurement results demonstrate that the array achieves good impedance matching, high isolation, and stable radiation patterns simultaneously, indicating it as a promising candidate for sub-6 GHz base-station services.

This article presents a novel and simple decoupling method to increase the isolation between two closely spaced patch antennas sharing a common thick substrate. The decoupling can be simply realized by adding a pure dielectric block (DB) above the coupled array. By means of DB to modify the space permittivity (propagation constant), the space-wave coupling can be controlled to cancel surface-wave coupling for isolation enhancement. Five benchmarks of combinations of two patch antennas with different positions or orientations are investigated to validate the decoupling concept and elaborate on the design procedure. The results show that the proposed method could provide over 20 dB isolation enhancements for patch antennas with $0.1\lambda _{0}$ or $0.027\lambda _{0}$ separation distances. Besides, wide isolation bandwidths and good radiation performances can be achieved for the DB-loaded antennas without reduction in total efficiency, front-to-back ratio (FBR), boresight gain, or polarization purity. Notably, the DB can be designed independently of the original array, making this method potential for some previously fabricated arrays without requiring modifying or replacing them with new ones.

This communication presents a novel decoupling device called a cascaded power dividing decoupling network (C-PDDN). It is cascaded with two antennas operating in adjacent or even contiguous frequency bands to reduce the mutual coupling. To achieve high isolations at $n$ desired frequency points, an $n$ -order C-PDDN can be accordingly designed. Each order PDDN consists of two power dividers (PDs), two sections of transmission lines, and one reactive component. Explicit design formulas are presented to calculate the design parameters of the C-PDDN to meet the required isolation criteria. To validate the decoupling concept, a two-order C-PDDN is designed and applied to a testing array that consists of two base-station dipole antennas, one of which serves the time-division long-term evolution (2300–2400 MHz) and the other for wireless fidelity system (2400–2483.5 MHz). The simulated and measured results demonstrate that the proposed two-order C-PDDN could decrease the mutual coupling by over 20 dB, verifying the decoupling methodology.

The emergence of highly directional beamforming technology makes millimeter wave frequency band communication possible in future wireless communication networks. Based on the multipath characteristics of millimeter wave frequency communication, a high-precision multipath channel estimation algorithm based on signal subspace is proposed. In the mobile terminal, an iterative heuristic radio frequency combination algorithm based on spatial points is proposed. The analog precoding at the base station uses deep learning to accelerate the calculation, and then the multi-user communication is modeled to design the digital precoding. The simulation results show that the multi-channel estimation algorithm can estimate 4 paths with an error of no more than 0.3 rad. The proposed DL algorithm takes only 20% of the time when it is close to the 87% spectral efficiency of the traditional algorithm.

We propose a stochastic hybrid implicit–explicit finite-difference time-domain method (S-HIE-FDTD) to compute the mean and variance of the electromagnetic (EM) fields using a single simulation, given those of the conductivity and permittivity in the computation domain. The mean and variance field update equations underlying the proposed method are derived from the field update equations of the “traditional” deterministic HIE-FDTD. The Courant–Friedrichs–Lewy condition of the S-HIE-FDTD depends on the spatial discretization sizes only in two dimensions; therefore, for computation domains with fine geometric features only in the remaining dimension, it uses a time step size that is larger than that of fully explicit schemes. Indeed, numerical results demonstrate that the proposed method is faster than the previously developed stochastic FDTD in computing the mean and variance of the EM fields in two different problems: wave propagation through a multilayer human tissue and transmission through a frequency selective surface.

In this letter, a hybrid translation computation method for the multilevel fast multipole algorithm (MLFMA) is proposed based on machine learning. The hybrid method combines both generalized regression neural network (GRNN) and artificial neural network (ANN) to replace the traditional translation procedure during the solving process of the MLFMA. Based on the data corresponding to every one of the interaction list boxes at each level, the hybrid neural network is trained. Comparing with the previous machine learning method in this field, the proposed model is more general, and with lower complexity since it inherits the accuracy of the GRNN as well as the efficiency of the ANN. To the best of our knowledge, it is for the first time that the ANN is integrated into the MLFMA. Numerical examples are benchmarked to illustrate the reliability and capability, thus making it possible to solve similar problems during the fast inhomogeneous plane wave algorithm solving process in the multilayered medium.

This article presents a novel dual-band decoupling network (DN) to increase the isolation between two antennas. It is composed of two dual-band Wilkinson power dividers, four additional transmission lines (TLs), and two reactive components. The decoupling formulas are derived from systematic design procedures. Hence, the design parameters of the power divider, lengths of TLs, and reactances of reactive components can be precisely determined to realize high port isolation. Two benchmarks of the two-element symmetric and asymmetric monopole arrays are used to elaborate the design procedure and demonstrate the advantages of proposed DN. Results show that the proposed DN can offer good impedance matching, high port isolation, high efficiency, and low envelop correlation coefficient (ECC) at both designated frequencies, which verified the proposed decoupling concept and theory.

Upon excitation by a surface magnetic current, a power/ground plate-pair supports only $\mathrm{TM}^{z}$ modes. This means that the magnetic field has only azimuthal components permitting a simple but effective domain decomposition method (DDM) to be used. In the proximity of an antipad, field interactions are rigorously modeled by a quasi-two-dimensional (Q-2D) finite element method (FEM) making use of three-dimensional (3D) triangular prism mesh elements. Since high-order $\mathrm{TM}^{z}$ modes are confined in the close proximity of the antipad, field interactions in the region away from the antipad only involve the fundamental mode and are rigorously modeled by a 2D FEM. This approach reduces 3D computation domain into a hybrid 2D/Q-2D domain. The discretization of this hybrid domain results in a global matrix system consisting of two globally coupled matrix equations pertinent to 2D and Q-2D domains. In this article, these two matrix equations are “decoupled” using a Riemann solver and the information exchange between the two domains is facilitated using numerical flux. The resulting decoupled two matrix equations are iteratively solved using the Gauss–Seidel algorithm. The accuracy, efficiency, and robustness of the proposed DDM are verified by four representative examples.

Electromagnetic radiations in complex electronic systems such as cell phones and computers are complex spatial–temporal correlations signals. Decomposing the electromagnetic radiation measured in the spatial–temporal domain is useful to gain physical insights into the process that generates radio frequency interference and the radiated spurious emission. In this article, a novel data-driven characterization method is proposed to analyze the electromagnetic radiations of both linear and nonlinear circuits. It employs the dynamic mode decomposition to simultaneously extract the temporal patterns and their corresponding dynamic modes. The temporal patterns show high-order harmonics generated by the nonlinearity. Then, these spatial–temporal coherent patterns provide the physical meaning of the radiation and fast predictions of future states in the circuit and electromagnetic systems. Two benchmarks, including victim and aggressor lines and the Schottky diode, are provided to demonstrate the validity of the proposed new analysis method. Finally, a discussion on the advantages and practical applications of the proposed method is addressed.

This letter proposes a novel deep learning (DL) approach to resolve the electromagnetic inverse scattering (EMIS) problems. The conventional approaches of resolving EMIS problems encounter assorted difficulties, such as high contrast, high computational cost, inevitable intrinsic nonlinearity, and strong ill-posedness. To surmount these difficulties, a novel DL approach is proposed based on a novel complex-valued deep fully convolutional neural network structure. The proposed complex-valued deep learning model for solving EMIS problems composes of an encoder network and its corresponding decoder network, followed by a final pixel-wise regression layer. The complex-valued encoder network extracts feature fragments from received scattered field data, while the role of the complex-valued decoder network is mapping the feature fragments to retrieve the final contrasts (permittivities) of dielectric scatterers. Hence, the proposed deep learning model functions as an “heterogeneous” transformation process, where measured scattering field data is converted into the corresponding contrasts of scatterers. As a consequence, the EMIS problem could be resolved accurately even for extremely high-contrast targets. Numerical benchmarks have illustrated the feasibility and accuracy of our proposed approach. The proposed approach opens a novel path for the deep learning-based real-time quantitative microwave imaging for high-contrast scatterers.

In this article, a wideband decoupling network (DN) is presented based on the Wilkinson power divider (WPD). The DN is composed of 2 two-way unequal WPDs, three transmission lines (TLs), and two reactive components. Two techniques are introduced for the wideband performance: 1) by using the WPD with high output isolation, the antenna matching becomes independent of antenna decoupling. So, the decoupled antennas with the proposed DN could perform broad impedance bandwidths without using matching networks and 2) the DN is designed to minimize antennas’ coupling coefficients at two frequencies, which collectively contribute to a broader decoupling bandwidth. In addition, rigorous design formulas are presented along with systematic design procedures. Based on this, the TL lengths and component reactances can be precisely calculated instead of being fitted to achieve high antenna isolation. Two decoupling cases using the proposed DN and its simplified model are presented and compared to give an intuitive illustration of the wideband mechanism. The measurement results for decoupling case #1 show that a very wide isolation bandwidth of 23.7%, total efficiency over 65%, and the envelope correlation coefficients (ECCs) less than 0.05 can be achieved simultaneously using the proposed DN.

A general and systematic approach is the first time proposed in this paper to design the neutralization line (NL) for isolation enhancement in multiple-input multiple-output (MIMO) antenna arrays. Each NL consists of a metal strip and a reactive component attached at its middle position. It is connected with every two antenna radiators to generate an additional coupling path against the original antenna coupling for isolation enhancement. A general network model that leads to a systematic design procedure is developed to meet the required isolation criteria. Three practical examples are presented and used to verify the proposed NL-based decoupling technique (DT). The measurement results align well with simulations. For all these examples, good impedance matching, isolation over 25 dB, total radiation efficiencies over 70%, and very low envelop correlation coefficients (ECCs) can be achieved simultaneously after adopting the proposed NL DT. Compared with previous decoupling works using parasitic elements, decoupling networks or NLs, the proposed method can achieve either a much wider decoupling bandwidth or a much smaller antenna separation distance while keeping a low design complexity along with systematic design procedure.