Byungjun Kim, Mecklenbrauker, C., & Gerstoft, P. (2024). Deep Learning-based Modulation Classification of Practical OFDM Signals for Spectrum Sensing. In IEEE INFOCOM 2024 - IEEE Conference on Computer Communications (pp. 1611–1620). https://doi.org/10.1109/INFOCOM52122.2024.10621421
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.
