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A compact and wideband differentially fed dual-polarized antenna with high common-mode suppression is investigated in this paper. A square patch with crossed-slot consisting of four slant funnelshaped slots is used as the radiator, generating slant ±45 • linear polarizations. Two orthogonal placed baluns, each connected with two open-end stubs, act as the feeding network. Under differential-mode (DM) excitation, the resonating mode of the square patch and quarter-wavelength mode of the funnel-shaped slots can be excited at low and high frequency band, respectively, which enhances the operation bandwidth of the designed antenna. By using differential excitation, a high level of common-mode (CM) suppression can be realized. To verify the design, the prototype of the designed antenna and an antenna array consisting of four elements are fabricated and measured. The experimental results indicate that a DM reflection coefficient better than −15 dB and a CM suppression level better than 1 dB are achieved within a wide impedance bandwidth of 55.3% (1.66 to 2.93 GHz). A high isolation and a stable radiation pattern can be observed within the operating band. Besides, the proposed antenna maintains a small aperture size of 0.36λ 0 ×0.36λ 0 , where λ 0 is the free-space wavelength at the center frequency of operation band. INDEX TERMS Common-mode suppression, differentially fed, dual-polarized antenna, multi-mode, slot antenna, wideband antenna.

A broadband low-profile circularly polarized (CP) antenna with a stepped sequential feeding structure is proposed in this letter. The CP antenna consists of four dipoles and parasitic structures. A novel feeding network using parallel transmission lines is introduced to realize phase difference required for circular polarization. Four dipoles are excited by the feeding structure to achieve CP radiation, and parasitic elements are loaded to broaden 3-dB axial ratio bandwidth (ARBW). To verify this design, the proposed antenna is fabricated and measured. Measured results show that a 10-dB impendence bandwidth of 42% (1.82 to 2.8 GHz) and 3-dB ARBW of 36.4% (1.89 to 2.73 GHz) are achieved, respectively. Moreover, the designed antenna has a low profile of 0.13λ (λ is the free-space wavelength at the center frequency) and a gain of average 9.5 dBi with less than 1.5-dB variation within the 3-dB ARBW.

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Modern electromagnetic (EM) device design generally relies on extensive iterative optimizations by designers using simulation software (e.g. CST), which is a very timeconsuming and tedious process. To relieve human engineers and boost productivity, we proposed a machine learning framework to solve the problem of automated design for EM tasks. The proposed approach combines advanced reinforcement learning (RL) algorithms and deep neural networks (DNNs) in an attempt to simulate the decision-making process of human designers to realize automation learning. Specifically, the RL-based agent can interact with the EM design software without engaging human designers, allowing for automated design. Besides, the data accumulated during EM software simulation in the early design stage are reused as training data to build a DNN surrogate model to replace the time-consuming EM simulation and further accelerate the training of RL to achieve better optimization of EM design. Two types of antenna array decoupling including 1×2 and 1×4 arrays working at 3.5 GHz are used as test vehicles to validate the proposed method. The decoupling metasurfaces designed by the proposed fully automated method based on RL showed satisfactory results comparable to the results achievable by human designers. This indicates that the proposed method can be used to build powerful tools to boost the design efficiency of EM devices.