用多任务学习识别共享频谱中的设备与协议，准确率超90%。
Transmitter Identification and Protocol Categorization in Shared Spectrum via Multi-Task RF Classification at the Network Edge
- 设计多通道卷积网络，融合信号特征应对重叠与环境变化。
- 在POWDER平台数据上实现90%协议分类、100%基站识别准确率。
- 适合无线安全、频谱管理领域的研究人员与工程师参考。
随着频谱共享日益重要，频谱监控与发射源识别对执行频谱使用政策、提升频谱利用效率及保障网络安全至关重要。本文提出一种基于边缘计算的多任务射频信号分类框架，可在同一频段内同时识别传输协议（如4G LTE、5G-NR、IEEE 802.11a）和不同基站，并区分其组合。采用卷积神经网络（CNN）处理信号特征重叠与环境波动等挑战，通过多通道输入策略提取有效特征。在POWDER平台采集的射频数据上，该方法在协议分类任务中达到90%准确率，基站识别达100%，联合分类任务为92%。结果表明该方法显著提升了现代无线网络中频谱监控、管理与安全能力。
原文摘要 · Abstract (English)
As spectrum sharing becomes increasingly vital to meet rising wireless demands in the future, spectrum monitoring and transmitter identification are indispensable for enforcing spectrum usage policy, efficient spectrum utilization, and network security. This study proposed a robust framework for transmitter identification and protocol categorization via multi-task RF signal classification in shared spectrum environments, where the spectrum monitor will classify transmission protocols (e.g., 4G LTE, 5G-NR, IEEE 802.11a) operating within the same frequency bands, and identify different transmitting base stations, as well as their combinations. A Convolutional Neural Network (CNN) is designed to tackle critical challenges such as overlapping signal characteristics and environmental variability. The proposed method employs a multi-channel input strategy to extract meaningful signal features, achieving remarkable accuracy: 90% for protocol classification, 100% for transmitting base station classification, and 92% for joint classification tasks, utilizing RF data from the POWDER platform. These results highlight the significant potential of the proposed method to enhance spectrum monitoring, management, and security in modern wireless networks.
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