Duration: 18 months
Cubesats and other miniaturized satellites are revolutionizing the way in which scientific and telecommunication missions are being provided from space. Space-based communication networks based on these satellites are enabling greater connectivity by bringing the internet into space. Nevertheless, the data intensity of these missions necessitates the use of high-gain antenna apertures to sustain enlarged link budgets. Compact satellite form factors create engineering challenges in accommodating the large apertures needed to sustain such links, especially at higher frequencies. The requirements that antennas provide service over two or more bands and polarizations adds further complexity to the design. Nevertheless, antenna solutions used in conventional satellites are not necessarily optimal for smaller satellites. While deployable antenna solutions exist for small satellites, fixed low-profile solutions that do not require deployment may be preferred. Electromagnetic metasurfaces that convert surface waves launched along the aperture into the desired radiation are a very attractive solution, since they are flat and eliminate the complexity associated with other solutions. Nevertheless, realizing dual-band and dual-polarization behavior from the constituent meta-atoms is challenging due to the complexity in realizing the necessary frequency dispersion and anisotropy. In the proposed project, the design and optimization of meta-atoms addressing this challenge will be carried out using generative machine learning networks. The project's overall objective will be to design and implement the low-profile, direct-radiating metasurface antenna that can be integrated with the satellite meeting technical requirements to yield a truly low-profile, stowable beam-forming solution. The project is being completed in collaboration with Kepler Communications and Burloak Technologies in Canada, who will supply essential input on requirements and manufacturing considerations.
