In this communication, a novel dual-band dual-polarized filtering antenna with high selectivity is proposed. By etching four slots at each side of a square radiating patch, dual operation bands are observed as well as a natural radiation null between the two bands. Another two radiation nulls are generated by introducing a novel feeding structure, which is composed of a microstrip line with open-circuit stepped-impedance resonators (OCSIR). Thus, a dual-band bandpass filtering response of the realized gain is obtained. Each polarization of the antenna is differentially fed, and the differential port isolation is better than 37 dB. A prototype of the antenna element and a 1×4 array is fabricated and tested to validate our design. Measured results show that the antenna element has dual operation bands of 3.28-3.71 GHz and 4.8-5.18 GHz for VSWR<1.5. Besides, a high out-of-band gain-suppression level which reaches 17.7 dB is also obtained. Good performance of the proposed antenna makes it a promising candidate for 5G sub-6 GHz base station systems.

In the intelligent transportation system, it is an efficient way for the intelligent vehicle to use the integrated radar and communications system (IRCS) to obtain the range and velocity estimations of other vehicles and simultaneously communicate with other facilities, such as vehicles and base stations. In the IRCS, the transmitted waveform is the orthogonal frequency division multiplexing (OFDM) integrated radar and communications waveform that contains communications information. Due to the existence of communications information, the traditional range and velocity estimation methods in radar can not be directly utilized in the IRCS. Moreover, to improve the resolution of range and velocity estimations, the signal bandwidth and coherent processing interval (CPI) are usually required to be increased, which will result in the increase of system cost and the reduction of update rate. To solve these problems, a auto-paired super-resolution range and velocity estimation method is proposed by using the OFDM integrated radar and communications waveform. First, the communications information in the received signals is compensated. Then, the frequency smoothing is performed to reduce the correlation between the echoes reflected by different targets. Finally, the auto-paired super-resolution range and velocity estimations are obtained by exploiting the translational invariance structure of the underlying signal model in both frequency and pulse domains. Furthermore, Cramr-Rao bounds (CRBs) on range and velocity estimations with the OFDM integrated radar and communications waveform are derived. Several numerical examples are presented to verify the effectiveness of the proposed method.

A reconfigurable antenna with radiation pattern and polarization reconfiguration capabilities is investigated in this paper. The antenna consists of a central dielectric resonator antenna (DRA) and two parasitic DRAs made of a liquid solution with low dielectric loss. Utilizing the flowing of liquid, the existing of parasitic DRA can be changed to produce different patterns. An omnidirectional pattern can be realized when only the central DRA radiates, while a unidirectional pattern can be obtained using the parasitic DRAs. Furthermore, the polarization of the antenna with unidirectional radiation can be changed by forming different parasitic DRAs, which can be realized via liquid flowing. Based on this principle, three different radiation states, namely, omnidirectional radiation and unidirectional radiation with x- or y-polarization, can be obtained. To verify the design, a prototype of the antenna operating at 2.4 GHz was simulated, fabricated, and measured. The measurement results indicate that the antenna provides effective impedance matching over the band of interest as well as radiation pattern and polarization reconfiguration capabilities.

A pattern-reconfigurable Yagi–Uda antenna based on liquid metal is presented. The antenna consists of a balun-fed active dipole and a pair of stretchable passive parasitic dipoles, which are implemented by eutectic gallium-indium (EGaIn) alloy embedded in microfluidic channels. The parasitic dipoles are driven at each end by two low-cost, three-dimensional printed media rods. Afterward, spinning the rods at different angles leads to varying degrees of stretching upon the stretchable dipoles. Note the length of passive parasitic dipoles is a vital factor for antenna radiation reconfiguration. The antenna exhibits bidirectional radiation if the parasitic dipoles are equal in length. Otherwise, directional radiation toward the shorter parasitic dipole direction can be obtained. Based on the above-mentioned working principle, a pattern-reconfigurable antenna working in wireless local area network (WLAN) band is fabricated and measured. Apart from the reconfigurable capability, the proposed antenna keeps operating in the WLAN band of 2.4–2.48 GHz during the whole shape deformation.

In this article, we propose an equivalent circuit theory-assisted deep learning approach to accelerate the design of metasurfaces. By combining the filter equivalent circuit theory and a sophisticated deep learning model, designers can achieve efficient metasurface designs. Compared with most existing metasurface generative design methods that rely on arbitrarily generated training dataset (TDS), the proposed method can adaptively produce highly relevant and low-noise training samples under the guidance of filter equivalent circuit theory, resulting in a significantly narrowed target solution space and improved model training efficiency. Furthermore, we select the variational autoencoder (VAE) as a generative model, which can compress the raw training samples into a lower-dimensional latent space where optimization methods, such as genetic algorithm, can be more efficiently executed to find the optimal design than a brute-force search. To verify the effectiveness of the proposed method, we apply it in the creation of three examples of frequency selective surfaces (FSSs), presenting wide-band, dual-band, and band-stop responses. Experimental results show that the proposed method can realize much faster and more stable convergence than deep learning design methods without domain knowledge.

A wideband fish-bone-shaped end-fire antenna utilizing odd-mode Spoof Surface Plasmon Polaritons (SSPPs) is proposed in this communication. Consisting of a microstrip-to-slotline converter and a differential-mode exciter, the feeding structure of this antenna can excite odd-mode signal on the SSPPs structure. The fish-bone-shaped SSPPs radiator can realize end-fire radiation, which is equivalent to a row of small current sources. A tapering end is introduced to the SSPPs radiator, realizing broader upper bandwidth. A prototype of this antenna is fabricated. Measured results exhibit that a wide impedance bandwidth of 70.3% (2.4.5.0 GHz) for |S11|<-10 dB is obtained. Stable end-fire radiation patterns and low cross polarization below -20 dB are also realized in desired frequency band. The gain varies from 4.4 to 8.6 dBi within the whole operating band while a radiating efficiency of 90% can be observed. This antenna has a compact size of 1.36λc ×0.86λc ×0.012λc (λc is the free space wavelength at 3.7GHz)