Neural Network Based Automatic Modulation Classification with Online Training

The use of wireless communication has been growing significantly. Given the limited radio spectrum available, it is important to use modulation techniques to squeeze the most information in the least amount of spectrum. To improve the throughput and reliability of communication, Automatic Modulation Classification (AMC) has become an essential topic in the research of Cognitive Radio (CR). AMC is a technique to identify the modulation scheme of the received radio signals without any transmission parameters. The state-of-the-art approach is to use a pre-trained classifier to determine the modulation in use. Unfortunately, all forms of distortions and interference are impossible to predict, and thus these approaches fail when new distortions or interference appear that were not previously learned. In this work, we developed a technique where the transmitter periodically sends out a known ordered sequence of signals of all the modulations to be used by the receiver. The receiver knows the transmission sequence and thus knows how to label these received signals containing each required modulation. We used an over-the-air (OTA) radio transmission between software-defined radio (SDR) devices, and the collected dictionary dataset was used to train a neural network at the receiver. Our results show that training the network in real-time while the radio is in use can enable significantly better performance than any of the existing approaches.