Machine Learning Aided Design of Sub-Array MIMO Antennas for CubeSats Based on 3D Printed Metallic Ridge Gap Waveguides

In this paper, a machine learning-assisted approach is presented for the design of a 3D Printed Metallic Ridge Gap Waveguide-Based array MIMO antenna for inter-cube satellite (CubeSat) communication. The designed antenna has a total dimension of $\boldsymbol{15.5 \times 10.5 \times 5.78} \mathbf{mm}^3$ and is based on aluminum alloy powder (AlSi10Mg) with a conductivity of $\boldsymbol{2.04\times 10^{7}}\ \mathbf{S}/\mathbf{m}$. The antenna exhibits wideband operation in V-band (59.3-66.6 GHz) with a stable realized gain of 10.5 dBi and radiation efficiency of 90% over the operating frequency.

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