Machine learning empowered Modulation detection for OFDM-based signals
This addresses modulation detection for OFDM technologies without prior signal knowledge, but it is incremental as it builds on existing ML methods for a specific domain.
The paper tackles blind modulation detection for OFDM-based signals in realistic environments by using a ResNet to classify scatter plots from extracted symbols, achieving over 80% accuracy at 10 dB SNR and 95% at 25 dB SNR.
We propose a blind ML-based modulation detection for OFDM-based technologies. Unlike previous works that assume an ideal environment with precise knowledge of subcarrier count and cyclic prefix location, we consider blind modulation detection while accounting for realistic environmental parameters and imperfections. Our approach employs a ResNet network to simultaneously detect the modulation type and accurately locate the cyclic prefix. Specifically, after eliminating the environmental impact from the signal and accurately extracting the OFDM symbols, we convert these symbols into scatter plots. Due to their unique shapes, these scatter plots are then classified using ResNet. As a result, our proposed modulation classification method can be applied to any OFDM-based technology without prior knowledge of the transmitted signal. We evaluate its performance across various modulation schemes and subcarrier numbers. Simulation results show that our method achieves a modulation detection accuracy exceeding $80\%$ at an SNR of $10$ dB and $95\%$ at an SNR of $25$ dB.