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A low-profile folded transmitarray antenna (FTA) with independent amplitude and phase control is proposed for the applications of millimeter-wave (mm-wave) communication. The proposed FTA is composed of three functional parts, i.e., an independent amplitude-phase controlling metasurface (MS) on the top, a polarization-converter on the bottom, and a feeding antenna. Particularly, by using spatially designed amplitude on the aperture of the top MS with the specification of the Taylor distribution, much lower side-lobe levels (SLLs) could be effectively realized. The ray-tracing method is used to reduce the profile height and integrate both the feeding antenna and the polarization converter. The proposed FTA, which is fully integrated and fabricated using low-cost printed circuit board (PCB) technology, is verified by the theoretical analysis, full-wave simulation and experimental measurements of a prototype covering the entire 5G mm-wave n257 bands (26.5 to 29.5 GHz). The measured results show that the proposed FTA has a 3 dB gain bandwidth of 19% from 24.8 to 30 GHz and a peak gain of 21.08 dBi with an aperture efficiency of 18.3%. Especially, compared to traditional FTAs with phase modulations only, the proposed FTA performs an average SLLs reduction of more than 5.0 dB. The simulated and measured results coincide to confirm the feature and effectiveness of the FTA with the excellent performance.

Multi-beam antenna is an essential subsystem for concurrent wireless communication systems in satellite and massive multi-input multi-output (MIMO) applications. In this brief, we presented the first experimental demonstration of a concurrent two-dimension (2D) steerable multi-beam antenna (SMBA) based on multi-feed reconfigurable reflectarray. The features and practicality of the 2D SMBA are verified with theoretical analysis, full-wave simulation and experimental measurements based on a prototype working at 9.3 GHz with around 300 MHz bandwidth. With element’s tunable range of more than 290°, the SMBA can be steered continuously up to $\theta =\pm 49.4^{\circ }\,\,(\varphi \,\,{=}$ 0 ~ 360°) in two-dimension. Meanwhile, with multiple beams steered simultaneously, the 2D SMBA can eliminate the crossover regions between adjacent beams of conventional multi-beam systems. The architecture would be potentially used in many wireless communication applications, such as satellite communications.

This brief presents the design of a dual-band outphasing power amplifier (OPA) using multiple topology fitting. By adding extra transmission lines to the traditional noncommensurate transmission lines, the design space for the output combiner is largely expanded. Different line combinations with good load modulation effects can be chosen according to frequency and circuit dimension. As a proof of concept, a prototype OPA operating at 2.6 and 3.5 GHz is fabricated based on two 10-W GaN HEMTs. The fabricated circuit achieves saturation output power of 44.4 and 44.3 dBm at the two selected frequency bands, with accompanying peak drain efficiencies of 69.5% and 69.2%. Meanwhile, the corresponding efficiencies at 6-dB power back-off points are 62.4% and 63.5%, respectively.

Solving inverse scattering problems (ISPs) is challenging because of its intrinsic ill-posedness and the nonlinearity. When dealing with highly nonlinear ISPs, i.e., those scatterers with high contrast and/or electrically large size, the traditional iterative nonlinear inversion methods converge slowly and take lots of computation time, even maybe trapped into local wrong solution. To alleviate the above challenges, a learning-assisted (LA) inversion approach termed as the LA inversion method (LAIM) with advanced generative adversarial network (GAN) in virtue of a new recently established contraction integral equation for inversion (CIE-I) is proposed to achieve a good balance between the computational efficiency and the accuracy of solving highly nonlinear ISPs. The preliminary profiles composed of only small amount of low-frequency components can be got efficiently by the Fourier bases expansion of CIE-I inversion (FBE-CIE-I). The physically exacted information can be taken as the input of the neural network to recover super-resolution image with more high-frequency components. A weighted loss function composed of the adversarial loss, mean absolute percentage error (MAPE), and structural similarity (SSIM) is used under the pix2pix GAN framework. In addition, the self-attention module is used at the end of the generator network to capture the physical distance information between two pixels and enhance the inversion accuracy of the feature scatterers. To further improve the inversion efficiency, the data-driven method (DDM) is used to achieve real-time imaging by cascading U-net and pix2pix GAN, where U-net is used to replace FBE-CIE-I in the LAIM. Compared with other LA inversion, both the synthetic and experimental examples have validated the merits of the proposed LAIM and DDM.

Nowadays, light detection and ranging (LiDAR) plays an important role in environmental perception technology; however, LiDAR cannot detect some blocked objects, and it can even detect some incorrect and unimportant objects as target objects due to the sparsity and disorder of the point cloud and the limited receiving area. Indeed, the detection accuracy can be improved by setting multiple cameras and LiDAR, but the cost can be unacceptable. In order to resolve these issues, a transformer-based architecture that can connect voxels with each other by neighborhood sparse attention is proposed. A standardized transformer, however, cannot be applied directly to sparse point cloud space because it contains a large number of empty voxels. Hence, a scheme of neighborhood sparse voxel attention to window attention (NSAW) is introduced, which is an efficient and accurate single stride transformer for connecting 3-D backbone to 2-D bird-eye-view (BEV) feature map. In this architecture, neighborhood window attention (NWA) and neighborhood voxel attention (NVA) are proposed to effectively extract features from no-empty voxels, which can achieve 84.30% average precision with 40 recall points (AP3D(R40)) and bring 2.68% AP3D(R40) improvement for Voxel region-based convolutional neural network (R-CNN) on the moderate car class on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) test split. On the premise of computational efficiency, the proposed NSAW demonstrates quite a few improvements over the traditional baselines on KITTI3D dataset.