基于多域实例融合的LR-FHSS卫星物联网上行链路复合干扰识别
Compound Interference Recognition for LR-FHSS Satellite IoT Uplinks via Multi-Domain Instance Fusion
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中文总结 AI 辅助
针对LR-FHSS卫星物联网上行链路复合干扰识别难题，提出多域实例融合方法，将其作为多实例多标签学习问题，融合不同域局部实例预测。开发数据集模拟实际条件，考虑特殊场景，实验表明该方法在相关场景下相比基线显著提升准确率。
中文摘要 AI 辅助
长距离跳频扩频（LR-FHSS）是用于大规模低地球轨道卫星物联网的一种有前景的上行链路物理层，低功率终端在有限地面基础设施的广域区域报告短数据包。然而，卫星物联网链路易受外部干扰，多种干扰成分共存会严重降低接收机可靠性并使干扰缓解复杂化。现有识别方法存在局限性。本文将LR-FHSS上行链路复合干扰识别表述为多实例多标签学习问题并提出多域实例融合方法，该方法融合时频域和频域的局部实例并聚合预测进行包级多标签识别。基于US915 LR-FHSS配置开发数据集构建管道并纳入阴影莱斯衰落和时变多普勒以模拟实际卫星通信条件。考虑到实际中获取带标签复合干扰样本的困难，研究了单到复合泛化和少样本复合干扰适应这两种实际接收机部署场景。实验结果表明，所提方法在单到复合泛化中总体精确准确率比最强基线提高了14.71个百分点，在少样本复合干扰适应中对于r = 1提高了14.81个百分点。
英文摘要
Long range-frequency hopping spread spectrum (LR-FHSS) is a promising uplink physical layer for massive low Earth orbit satellite Internet of Things, where low power terminals report short packets from wide area regions with limited terrestrial infrastructure. However, satellite IoT links are exposed to external interference, and the coexistence of multiple interference components can severely degrade receiver reliability and complicate interference mitigation. Existing recognition methods either focus on single interference scenarios or treat each compound interference combination as an independent class, leading to limited generalization or poor scalability. To address this problem, this paper formulates LR-FHSS uplink compound interference recognition as a multi-instance multi-label learning problem and proposes a multi-domain instance fusion method. The proposed method fuses local instances from the time-frequency and frequency domains and aggregates their predictions for bag-level multi-label recognition. A dataset construction pipeline is developed based on the US915 LR-FHSS configuration and incorporates shadowed-Rician fading and time-varying Doppler to emulate practical satellite communication conditions. Considering the difficulty of obtaining labeled compound interference samples in practice, single-to-compound generalization and few-shot compound interference adaptation are investigated as two practical receiver deployment scenarios. Experimental results show that the proposed method improves the overall exact accuracy over the strongest baseline by 14.71 percentage points in single-to-compound generalization and by 14.81 percentage points in few-shot compound interference adaptation for $r=1$.
发表机构
- College of Electronic Science and Technology, National University of Defense Technology（电子科学与技术学院，国防科技大学）
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