A compact low-profile reconfigurable metasurface (MTS) antenna is presented in this letter, occupying an overall size of 0.65λ ₀ × 0.65λ ₀ × 0.067λ ₀ . The proposed antenna is mainly composed of a 3 × 3 square MTS, a ring-shaped pixel structure, a square microstrip patch, a metal ground plane, and a simple feeding network. Through the characteristic mode analysis, two orthogonal degenerate modes are excited to realize the reconfigurable two linear polarizations and one circular polarization (CP). Furthermore, by loading an annular pixel structure around the MTS, the designed antenna can produce the steerable beam directions of ±40° with horizontal polarization, ±20° with vertical polarization, and ±25° with CP in the yz plane. Due to the symmetry of the layout, polarization and pattern diversities can also be realized in the xz plane. The proposed reconfigurable antenna is manufactured and measured to demonstrate the proposed design. The measured results show that the designed antenna acquires polarization and pattern diversities in C-band at the center frequency of 5 GHz, and the test results generally are in consistent with the simulation ones, which demonstrates the superiority of the proposed antenna.

A miniaturized two-element microstrip antenna array is proposed for the millimeter wave band of 5-th Generation (5G) wireless communication systems. A surface of electromagnetic band gap structures (EBG) is applied as the ground for two closely packed patch antennas operating in 5G New Radio (NR) (26500 MHz - 29500 MHz) frequency band. With the help of the proposed EBG ground, the two E-shaped microstrip antenna elements can be placed in close proximity to each other by 0.3 wavelength center to center distance in free space at the center frequency, yet the mutual coupling between them can still reach to more than 23 dB within the whole band of interest, which is about 10 dB larger than that on normal ground. All major radiation characteristics of the two-element array are well reserved with the EBG ground. The proposed design can be used in multiple-input-multiple-output (MIMO) applications or as the building subarray of larger phased arrays at millimeter wave bands for mobile communication systems.

An extremely broadband circularly polarized (CP) antenna is presented. The proposed antenna consists of a crossed bowtie dipole loaded with four unequal parasitic cross-slots, four parasitic bowtie patches, and four parasitic rectangular strips. The cross-slots can generate a CP mode due to the sequential phases of the crossed bowtie dipole. The parasitic bowtie patches are gap-coupled with two arms of the crossed bowtie dipole, and they are sequentially rotated along diagonals to generate an additional CP mode. The parasitic strips also generate one CP mode due to the sequentially rotated configuration parallel to the crossed bowtie dipole. By properly combing with two CP modes of the crossed bowtie dipole, broadband CP operation is achieved. The experimental and simulated results are in agreement, and the measured results show that the proposed antenna exhibits a very wide impedance bandwidth of 93.1% (2.21-6.06 GHz) and an excellent axial ratio (AR) bandwidth of 90.9% (2.2-6.4 GHz). In addition, the proposed antenna produces a right-hand circular polarization (RHCP) with a peak gain of 8.6 dBi.

The beampattern synthesis of antenna array is always known as a computationally cost task that needs efficient strategies to deal with. In this article, a novel artificial neural network (ANN)-based array synthesis method is proposed. By establishing an encoder–decoder-based ANN framework, the mask-constrained beampattern in terms of focused or shaped beam for linear arrays with arbitrary given array geometry is successfully synthesized. More in detail, the encoder serves as an array synthesizer, while the decoder behaves as an array analyzer. Thanks to the designed pretrained decoder, such an approach is computationally efficient so that real-time array synthesis can be potentially achieved. Moreover, the proposed method allows one to consider both ideal and actual linear arrays with mutual coupling effects and nonidealities. The results of a wide numerical validation involving amplitude-only, phase-only, and amplitude–phase excitation syntheses are presented to assess the flexibility and the versatility of the proposed method also in comparison to the competitive state-of-the-art synthesis techniques.

In this paper, transmitting metasurface using single dielectric substrate for orbital angular momentum (OAM) generation is presented in Ku band. A dimension extension method is introduced in designing the unit cell of the proposed transmitting metasurface to achieve required phase shift and transmission magnitude for generating OAM. A prototype of the proposed transmitting metasurface is fabricated and measured. Measured results suggest that the proposed transmitting metasurface can generate OAM vortex wave at 18 GHz. The proposed method can be applied in wireless communication systems requiring low-profile transmitted OAM generator.

The tremendous progress in artificial intelligence promotes the wide application of machine learning (ML) technology in the field of electronic science. Recently ML-based antenna optimization provides a distinct candidate and attracts considerable attention. However, the large number of training samples generated through time-consuming EM simulations becomes a significant challenge. In this article, an efficient online data-driven enhanced-XGBoost (E-XGBoost) method for antenna optimization is proposed, which is mainly composed of two parts, i.e., an input variable filter module (IVFM) and an antenna optimization module (AOM). Specifically, IVFM serves as a variable sensitivity analyzer, which is accomplished by E-XGBoost to efficiently reduce the dimension of design variable and hence save the training samples. Next, the design variables obtained by IVFM are fed into AOM to find the near-optimal solution. In AOM, an online learning strategy is proposed to train a local E-XGBoost model to evaluate the population in the metaheuristic optimization algorithm (MOA). Compared to the global ML model that can mimic the entire design space, this local E-XGBoost model can further cut down the training samples. To verify the performance of the proposed method, several different antenna examples, i.e., U-slot patch antenna, Fabry–Perot resonant antenna, and dual-polarized cross dipole antenna and 5G MIMO antenna array, are simulated. Numerical results support the proposed method in terms of its superior performance and potential advantage of saving computational overhead.