Nonlocal metasurfaces based on advanced Bayesian learning for satellite communications

Beam-steering antennas, based on sub-wavelength deflectors, aim to facilitate information exchange with satellite communication (SatCom) on-the-move systems. Recent beam-deflection solutions are often based on the phase-gradient metasurfaces (PGM) concept to generate the desired wavefront. However, the performance remains inadequate for advanced communication technologies due to bandwidth limitations and insufficient efficiency. Here, a highly-efficient broadband beam-deflection system is design, fabricate, and characterize experimentally for SatCom applications. To achieve that, an advanced Bayesian learning strategy, accompanied by an innovative nonlocal metasurface (NLM) configuration, is employed. This non-trivial broadband design leverages a sophisticated coupling between adjacent sub-wavelength elements, together with varying thicknesses along the propagation direction to manipulate the incoming beam. The novel beam-deflector is illuminated with a circularly polarized wave and is designed to operate in the Ka-Tx band from 27.5 to 31 GHz. The optimized structure demonstrates exceptionally high performance compared to the conventional phase-gradient synthesizing counterparts. The experimental validation exhibits an excellent agreement with the numerical simulation, highlighting the potential of this optimization-assisted design to advance metasurface technology for various beam-steering applications