轻量级波形识别框架，可在嵌入式设备上实时区分10种6G物联网波形。
TFZ-Tree: An Ultra-Lightweight Waveform Classification Framework for Resource-Constrained Devices
- 基于时频多维特征与Z检验决策树，低复杂度提取时域特征
- 在AWGN下平均准确率达99.5%，多径信道下达87.4%，单次推理延迟<4毫秒
- 首次实现资源受限设备上十类物联网波形的实时识别，适合边缘终端部署
在6G物联网多波形共存趋势下，智能接收机需先识别物理层波形类型才能正确解调与调度资源。现有研究多聚焦符号级调制分类，针对物理层波形（如OFDM、OTFS、LoRa）的识别方法稀缺且严重依赖深度神经网络与复杂时频变换，难以在资源受限终端部署。符号调制分类也无法绕过‘先识别波形’的前提。为填补此双重空白，本文提出一种基于时频多维特征的超轻量级波形分类框架——TFZ-Tree，采用低复杂度时域特征提取，分类后端使用经Z统计检验优化的协作Z检验决策树（ZTree），通过假设检验置信度自动控制树分裂与规模，确保在资源受限处理器上的高效运行。在包含OFDM、OTFS、DSSS、LoRa、NB-IoT等10种6G候选波形的数据集上测试，该方法在AWGN下平均准确率达99.5%，在TDL-C多径信道下为87.4%，主要混淆发生在OTFS与LoRa之间。在x86平台以C语言实现，单次推理延迟低于4~ms。据我们所知，这是首个实现在资源受限设备上对十类物联网波形进行实时识别的工作。未来工作将推进在嵌入式MCU上的部署加速。代码与数据集已开源：https://github.com/Einstein-sworder/IoT-wave。
原文摘要 · Abstract (English)
Under the trend of multi-waveform coexistence in 6G IoT, intelligent receivers must first identify physical-layer waveform types before performing correct demodulation and resource scheduling. However, existing signal identification research largely focuses on symbol-level modulation classification. Research directly targeting physical-layer waveform types (e.g., OFDM, OTFS, LoRa) is not only extremely scarce but also heavily reliant on deep neural networks and complex time-frequency transforms, making deployment on resource-constrained terminals difficult. Symbol modulation classification methods themselves cannot circumvent the prerequisite of ``waveform identification first.'' To address this dual gap, we propose an ultra-lightweight waveform classification framework based on time-frequency multidimensional features with a cooperative Z-test tree (ZTree). The framework employs low-complexity time-domain feature extraction, and the classification backend adopts a ZTree optimized by Z-statistical testing, which uses hypothesis testing confidence to automatically control decision tree splitting and size, ensuring efficient execution on resource-limited processors. Tested on ten 6G candidate waveforms including OFDM, OTFS, DSSS, LoRa, and NB-IoT, the method achieves 99.5\% average accuracy under AWGN and 87.4\% under TDL-C multipath channels, with main confusion between OTFS and LoRa. Implemented in C on an x86 platform, single inference latency is under 4~ms. To the best of our knowledge, this is the first work achieving real-time recognition of ten IoT waveform types. Future work will target deployment acceleration on embedded MCUs. Code and dataset are open-sourced at: https://github.com/Einstein-sworder/IoT-wave.
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