Abstract-This paper presents a design of two compact, light, rigid and low-cost 3D-printed millimeter-wave antennas for 5G communication system. The proposed antennas consist of a radiating slot that is surrounded by a rectangular cavity and corrugations which boost the gain performance of the antennas. Furthermore, the proposed antennas are fabricated using 3Dprinting technology and they are metallized using novel, simple and low cost techniques which utilizes commercial conducive spray-coating technology. The proposed antennas operate at 28-GHz band, where the first design is fed by a waveguide to prove the performance, while the second design is fed by a microstrip line to demonstrate the ability to be integrated into a compact structure. Measurement results show a wide impedance bandwidth which enables the proposed antenna design to be a strong candidate for 5G applications.

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Conformal, steerable lens antennas are of particular interest for mm-wave antenna designers, as they enable low cost solutions for applications such as 5G mobile communications, radio-wave imaging and satellite communications. Recent advances in additive manufacturing technology have opened up new possibilities for realising graded-dielectric electromagnetic devices. In this letter a compressed Luneburg lens fabricated from multi-material 3D printing is presented. Such a device has a steep dielectric gradient and cannot be easily realised using an effective medium approach that has become typical of 3D printed graded-index devices. Instead, 5 different dielectric filaments were used to print the lens with a 100% filament fillfactor. The lens is excited by a WR-10 open-ended waveguide probe across the 75-110 GHz band, and achieves a bore-sight gain of 22 dBi, and -3 dBi scan angle of 25°at 84 GHz.

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Surface symmetry breaking and disorder have been recently explored to overcome operation bandwidth, unwanted diffraction, and polarization dependence issues in the conventional metasurface designs thanks to their increasing degrees of design freedom. However, efficient full‐wave simulation and optimization of electrically large electromagnetic structures have been a longstanding problem. Herein, an interactive learning approach is developed to build new meta‐atom datasets which include the effect of mutual coupling. A deep learning‐based model is developed to extract features of incident/reflection waves and their neighboring interaction responses from a limited number of known meta‐atoms. Finally, the deep neural network is incorporated with optimization algorithms to design, as an example, large‐scale metasurfaces for beam manipulation and wideband scattering reduction. The results demonstrate that the proposed architecture can be successfully applied to rapidly design aperture‐efficient metasurfaces or metalenses at large scales of over tens of thousands of meta‐atoms.