Deep Learning to Predict the Modulation Schemes of Real OFDM Signals

摘要

The goal of this effort is to train Deep Learning (DL) models using synthetic Orthogonal Frequency-Division Multiplexing (OFDM) datasets to predict the modulation schemes of real OFDM signals without transfer learning. To facilitate our study, we generated a synthetic dataset, OFDM-O, that consists of 480/: instances across four different modulations which include BPSK, QPSK, QAM16, and QAM64. Each instance with 16 OFDM symbols consists of 1280 IQ symbols. Since real OFDM instances have lengths of [2,5,44] OFDM symbols, the DL models are trained using short instances in order to overcome the instance length mismatch. Two datasets generated dynamically during training, OFDM-ro and OFDM-riq, are derived from dataset OFDM-O, by randomly choosing 5 consecutive OFDM symbols or 400 consecutive IQ symbols from each instance in OFDM-O at each epoch. 1-D Residual Neural Network (ResNet) models trained using three datasets achieve overall accuracies of 97.8%, 84.5% and 77.6% for OFDM-O, OFDM-ro and OFDM-riq, respectively. Cross validation of the three datasets shows that the ResNet model trained using OFDM-riq predicts the validation datasets of OFDM-O and OFDM-ro with high accuracy. Furthermore, a two-step validation is proposed during training of DL models where DL models are first validated with a synthetic validation dataset and then validated with real OFDM instances. Including a validation set with real signal allows us to terminate training before the DL model is over fit to the synthetic signals. The ResNet model trained using OFDM-riq correctly predicts 5 out of 7 short instances and all 5 long instances in the testing dataset of real signals. Both mis-classifications come from short instances of 2 OFDM symbols. Overall, the ResNet model trained using OFDM-riq can successfully predict the modulation schemes of real OFDM signals with high accuracy.