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 480k instances across four different modulations which include BP SK, QP SK, 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.
Machine Learning (ML) technologies have been widely used for blind signal classification problem (Modulation Classification) to automatically identify the modulation schemes of Radio Frequency (RF) signal from complex- valued IQ (In-phase Quadrature) samples. Traditional ML methods usually have two stages where the first stage is to manually extract the features of the IQ symbols by subject matter experts and the second stage is to feed the features to an ML algorithm (e.g., a support vector machine) to develop the classifier. The state- of-art technology is to apply Deep Learning (DL) technologies such as Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) directly to the complex-value IQ symbols to train a multi-class classifier. In this effort we are focused on the modulation and coding rate classification problems for multi-carrier Orthogonal Frequency-Division Multiplexing (OFDM) signals. The generated OFDM data set consists of over 480k instances across four different modulations and three different coding rates which include BPSK + 1=2, BPSK+3=4, QPSK+1=2, QPSK+3=4, QAM16+1=2, QAM16+3=4, QAM64+2=3 and QAM64+3=4. Deep Learning (DL) models can successfully catch the features of OFDM modulations and identify the modulation scheme from the baseband IQ symbols. Four DL models including Residual Neural Network 34(Resnet34), Resnet18, Squeezenet and Long Short-Term Memory (LSTM) trained over the data set can achieve over 94% overall accuracy for signals across 10dB to 10dB with step size of 4dB. Among them, Squeezenet and LSTM models have much smaller model sizes which can be easily loaded in resource-limited edge computing platforms. To the best of our knowledge, coding rate classification of OFDM signals has not been studied in previous works. Therefore, three different DL models including a single-stage coding rate classifier, a combination of modulation and coding rate classifier, and a two-stage coding rate classifier are developed to identify the coding rate. However, coding rates can only be identified for low-order modulation schemes BPSK and QPSK but not for high-order modulation schemes QAM16 and QAM64. Further investigation is needed to understand the coding rate classification for high-order QAM signals.
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