SmartUT: Receive Beamforming for Spectral Coexistence of NGSO Satellite Systems

TL;DR

This paper introduces MambaBF, a deep learning-based beamformer for NGSO satellite systems that effectively mitigates interference without requiring CSI, outperforming traditional methods especially in low-SINR and snapshot-limited scenarios.

Contribution

The paper presents a novel unsupervised deep learning beamformer, MambaBF, designed for interference mitigation in NGSO satellite downlink systems without needing channel state information.

Findings

MambaBF outperforms conventional beamformers in interference mitigation.

MambaBF maintains high SINR with limited snapshots and imperfect CSI.

Simulation results validate the effectiveness of MambaBF in challenging conditions.

Abstract

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.