In this letter, a pattern-reconfigurable antenna for indoor communication is proposed, which can provide stable conical and broadside beams. Although this type of pattern-reconfigurable antenna can provide large radiation coverage, it usually suffers complex structure, narrow bandwidth, and distorted pattern. In order to get rid of the complex radiators, a simple loop patch is introduced to generate the conical and broadside beams. Through slotting the loop patch with the aid of characteristic mode analysis, the two desired radiation modes can be well excited in the same band. Meanwhile, the modified loop patch can provide stable radiation patterns by suppressing unwanted higher order modes. For achieving the reconfigurable beams, a reconfigurable feeding network is designed to switch two working modes. Finally, the proposed pattern-reconfigurable antenna is fabricated, and the measured overlapped 10 dB operating bandwidth is about 27.3% ranging from 3.61 to 4.75 GHz. The results show that the proposed pattern-reconfigurable antenna has a wide bandwidth and stable radiation patterns, which exhibits attractive potentials for fifth-generation (5G) indoor communication in sub-6 GHz.

In this paper, a deep neural network (DNN)-based machine learning (ML) approach is presented to synthesize linear sparse arrays (LSAs). Due to the powerful fitting capability of neural networks, a DNN framework is established to map the relation between the desired array pattern and LSA geometry. In this manner, the array synthesis is accomplished by training a proper DNN that can generate the desired LSA geometry. To obtain better training, the density tapering (DT) technique is introduced to the training process as the prior domain knowledge to specify the varying boundaries of each element in the LSA. Consequently, an efficient DT-assisted DNN (D-DNN) synthesis method is developed. The proposed D-DNN method has the ability to simultaneously optimize the element positions and excitations, and directly constrain the inter-element spacings. Furthermore, by introducing the active element pattern (AEP) technique, the presented method can synthesize both ideal arrays and arrays with mutual coupling. Numerical results of a wide variety of synthesis scenarios are presented to demonstrate the validity, efficiency, and flexibility of the proposed method.

A dual-polarized endfire array is presented in this article for millimeter application. The substrate integrated mode-composite transmission line is introduced to exciting the dual-polarized radiation. Specially, the horizontal polarization is excited by the transverse electromagnetic (TEM) mode, whereas the vertical polarization is based on the TE ₁₀ mode. Two metal blocks sandwich the substrates to form a sandwich structure, and an open-ended slot is designed on the block edge to construed complementary sources radiation. Benefiting from the wideband feature of complementary sources, the proposed dual-polarized endfire array achieved a considerable bandwidth performance of 26.9% (24.8–32.5 GHz) based on the standard that both the voltage standing wave ratios of the two ports are less than 2. In terms of the key factor of dual-polarized antenna and isolation, measured results show a high port isolation of over 36 dB and a cross-polarization discrimination rate of over 30 dB. The excellent dual-polarized performance gives the proposed endfire array the potential of application in millimeter-wave mobile communications.

In this paper, a scanning enhanced low-profile broadband phased array is presented. This array is based on the radiator-sharing approach and the defected ground structures (DGS). In this phased array, the radiator-sharing approach has been implemented by using metasurface antenna, and the scanning range has been remarkably enhanced by reducing element spacing. An attractive behavior in broadband impedance matching has still been obtained, in spite that closely-spaced feedings are adopted. Moreover, the scanning performance is considerably improved by applying meander-line slots cut from the ground plane, forming DGS. In order to validate this proposed design, a nine-element linear phased array with DGSs has been fabricated and measured. Based on the uniform magnitude and the progressive phase, the capability of wide-angle scanning can be achieved over the measured bandwidth of 23.1% (4.6 ~ 5.8 GHz). At the center frequency of 5.2 GHz, this array scans up to 50° and the realized gain varies from 14.76 to 11.85 dBi.

This paper presents a novel Galerkin-type method of moments solution of the volume surface integral equation (VSIE), which is developed for the analysis of electromagnetic scattering and radiation from composite metallic and dielectric structures. Our new scheme utilizes curved tetrahedral elements and curved triangular patches for geometric modeling, and the associated higher order hierarchical vector (HOHV) basis functions for volume/surface current modeling. In contrast to the previous studies on higher order VSIE, our scheme, which is reported for the first time in VSIE, is much more flexible. In comparison with the conventional low-order scheme, our scheme requires much less memory and computational time for the same accuracy level. Numerical results are provided to demonstrate the accuracy, efficiency, and flexibility of our scheme.

This paper presents a novel early fusion framework (EFF) of deep learning (DL) for solving electromagnetic (EM) inverse scattering problems (ISPs) in real-time with high accuracy. The EFF integrates the scattered field encoder (SFE) and the backpropagation encoder (BPE). On the one hand, the nonlinear relationships constrained by the multiple scattering phenomenon, are introduced in the SFE, which guarantees better external scattering manifestations. On the other hand, the pre-reconstructed backpropagation approximation (BP) distributions provide prior information in ISPs via the BPE, which is in accordance with the internal scattering manifestations. The novel EFF concatenates the features extracted from the scattered field and the BP approximation, achieving more scattering manifestations in the solving process. Furthermore, the multiple scattering information and BP distributions regularize each other’s encoding process effectively, which achieves faster convergence and more stable inversion results. It is expected that the EFF exploits a general framework for solving ISPs, which can be adopted to different scenarios by importing different kinds of prior information according to the fusion approach. The superior veracity, stability and generalization ability of the EFF have been demonstrated by the synthetic and the experimental results.