SmartUT: Receive Beamforming for Spectral Coexistence of NGSO Satellite Systems

In this paper, we investigate downlink co-frequency interference (CFI)
mitigation in non-geostationary satellites orbits (NGSOs) co-existing systems.
Traditional mitigation techniques, such as Zero-forcing (ZF), produce a null
towards the direction of arrivals (DOAs) of the interfering signals, but they
suffer from high computational complexity due to matrix inversions and required
knowledge of the channel state information (CSI). Furthermore, adaptive
beamformers, such as sample matrix inversion (SMI)-based minimum variance,
provide poor performance when the available snapshots are limited. We propose a
Mamba-based beamformer (MambaBF) that leverages an unsupervised deep learning
(DL) approach and can be deployed on the user terminal (UT) antenna array, for
assisting downlink beamforming and CFI mitigation using only a limited number
of available array snapshots as input, and without CSI knowledge. Simulation
results demonstrate that MambaBF consistently outperforms conventional
beamforming techniques in mitigating interference and maximizing the
signal-to-interference-plus-noise ratio (SINR), particularly under challenging
conditions characterized by low SINR, limited snapshots, and imperfect CSI.