Deep Learning-based Modulation Classification of Practical OFDM Signals for Spectrum Sensing
<|reference_start|>Deep Learning-based Modulation Classification of Practical OFDM Signals for Spectrum Sensing: In this study, the modulation of symbols on OFDM subcarriers is classified for transmissions following Wi-Fi~6 and 5G downlink specifications. First, our approach estimates the OFDM symbol duration and cyclic prefix length based on the cyclic autocorrelation function. We propose a feature extraction algorithm characterizing the modulation of OFDM signals, which includes removing the effects of a synchronization error. The obtained feature is converted into a 2D histogram of phase and amplitude and this histogram is taken as input to a convolutional neural network (CNN)-based classifier. The classifier does not require prior knowledge of protocol-specific information such as Wi-Fi preamble or resource allocation of 5G physical channels. The classifier's performance, evaluated using synthetic and real-world measured over-the-air (OTA) datasets, achieves a minimum accuracy of 97\% accuracy with OTA data when SNR is above the value required for data transmission.<|reference_end|>
@article{kim2024deep,
    title={Deep Learning-based Modulation Classification of Practical OFDM Signals
  for Spectrum Sensing},
    author={Byungjun Kim, Christoph Mecklenbr"auker, Peter Gerstoft},
    journal={arXiv preprint arXiv:2403.19292},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19292},
    primaryClass={cs.NI}
}