# SmartUT: Receive Beamforming for Spectral Coexistence of NGSO Satellite Systems

arXiv: https://arxiv.org/abs/2505.07714 · HTML: https://mlsanity.com/paper/2505.07714/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 5 of 5)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [A Survey on Non-Geostationary Satellite Systems: The Communication Perspective](https://mlsanity.com/paper/2107.05312/) (2021)
- [Massive MIMO Transmission for LEO Satellite Communications](https://mlsanity.com/paper/2002.08148/) (2020)
- [Deep Learning Based Uplink Multi-User SIMO Beamforming Design](https://mlsanity.com/paper/2309.16603/) (2023)
- [Should We Worry About Interference in Emerging Dense NGSO Satellite Constellations?](https://mlsanity.com/paper/1909.05130/) (2019)

## Abstract (arXiv)

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.