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Automatic Modulation Classification for Hardware-Impaired OTFS Transceiver Systems
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DOI：10.1109/LWC.2025.3632246.png)
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Abstract
En 中文
This letter presents the first comprehensive study on automatic modulation classification (AMC) for orthogonal time-frequency space (OTFS) systems under practical hardware-impairments (HIs), such as in-phase/quadrature imbalance (IQI), DC offset (DCO), phase noise (PN), and carrier frequency offset (CFO). Unlike prior OTFS-AMC studies that assume ideal transceivers, this letter incorporates hardware non-idealities and systematically analyzes the individual and joint effects of multiple HIs on AMC performance under Extended Vehicular A (EVA) channel conditions. A 2D convolutional neural network (2D-CNN) model, tailored to capture the sparse structure of 2D OTFS frames, serves as a consistent baseline to assess OTFS robustness under practical HIs. Experimental results demonstrate that receiver-side HIs are more detrimental than transmitter-side. Moreover, it is observed that severe PN leads to severe AMC performance degradation, limiting the overall AMC accuracy to 11%. Furthermore, analysis also confirms that OTFS consistently outperforms conventional orthogonal frequency-division multiplexing (OFDM), which struggles with severe inter-carrier interference in doubly-spread wireless channels. Increasing the OTFS frame size enhances the robustness of AMC performance, providing a 3–5% accuracy improvement under various HI conditions. Collectively, these findings offer valuable design insights for developing HI-resilient OTFS-AMC systems suitable for practical high-mobility wireless networks.
Keywords:
Automatic modulation classification (AMC)
hardware impairments (HIs)
OTFS
2D-CNN
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