Deep Learning-Based Autodetection of 5G NR mm Wave Waveforms

Wireless use cases such as spectrum sharing and Massive Machine Type Communications (mMTC) can benefit from the detection of unknown signals, which includes estimating their received power as well as other key characteristics such as bandwidth, modulation type, and waveform. While conventional signal detection methods are susceptible to noise, deep learning (DL) models offer a more robust alternative. Previously, DL models were used for solving simpler problems, focusing mainly on modulation recognition. We propose an advanced DL neural network structure that extracts the parameters of 5G NR frequency range 2 (FR2) mmWave test model waveforms. We evaluate our framework on a state-of-the-art signal generator and vector signal analyzer (VSA) that mimics real-world detection. Our work shows that incorporating curriculum training (CT) on both additive white Gaussian noise (AWGN) and frequency shift error enhances the model's accuracy across all SNR and frequency shift ranges. We further enhance the accuracy by employing the error vector magnitude (EVM) function to prioritize the top five scored parameters and validate selected parameters. As a result, our method consistently achieves an accuracy rate exceeding 90% when extracting the key parameters from 5G NR FR2 mmWave waveforms at diverse noise levels.