TFZ-Tree: An Ultra-Lightweight Waveform Classification Framework for Resource-Constrained Devices
The TFZ-Tree framework offers an innovative solution for waveform classification in resource-constrained devices, particularly in the context of 6G IoT. It utilizes a cooperative Z-test tree for efficient classification of various waveform types, achieving high accuracy rates. This research addresses a significant gap in existing signal identification methods, paving the way for real-time recognition of multiple IoT waveforms.
- ▪The TFZ-Tree framework is designed for classifying physical-layer waveform types in 6G IoT environments.
- ▪It achieves an average accuracy of 99.5% under AWGN and 87.4% under TDL-C multipath channels.
- ▪The framework employs low-complexity time-domain feature extraction and a ZTree optimized by Z-statistical testing.
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Electrical Engineering and Systems Science > Signal Processing arXiv:2605.15656 (eess) [Submitted on 15 May 2026] Title:TFZ-Tree: An Ultra-Lightweight Waveform Classification Framework for Resource-Constrained Devices Authors:Hao Wang, Kuang Zhang, Yonggang Chi, Tianqi Zhao, Yanbo Fu, Jiaxing Guo View a PDF of the paper titled TFZ-Tree: An Ultra-Lightweight Waveform Classification Framework for Resource-Constrained Devices, by Hao Wang and 5 other authors View PDF HTML (experimental) Abstract: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.
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