  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.
