用深度学习提升卫星低轨系统下行波束成形抗干扰能力
SmartUT: Receive Beamforming for Spectral Coexistence of NGSO Satellite Systems
- 基于Mamba架构设计无监督深度学习波束成形器，仅需少量阵列采样数据
- 无需信道状态信息，在低信干噪比和采样数少时仍保持高SINR
- 适合部署在用户终端，解决低轨卫星共频干扰难题
本文研究非静止轨道卫星系统共频共存场景下的下行链路同频干扰（CFI）抑制问题。传统方法如零强迫（ZF）需矩阵求逆且依赖信道状态信息（CSI），计算复杂度高；自适应波束成形器如基于样本协方差矩阵求逆（SMI）的最小方差法在可用快照有限时性能下降。为此，提出一种基于Mamba的波束成形器（MambaBF），采用无监督深度学习方法，可部署于用户终端天线阵列，仅以有限数量的阵列快照为输入，无需已知CSI。仿真结果表明，MambaBF在低信干噪比、快照数少及信道信息不完整等挑战性条件下，持续优于传统波束成形技术，在抑制干扰和最大化信号-干扰加噪声比（SINR）方面表现更优。
原文摘要 · Abstract (English)
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.
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