Design and Analysis of Convolutional Neural Network for RF Signal Modulation Classification for In-Orbit Deployment

To effectively transmit data to and from satellites requires a complex and robust RF communication system. Using adaptive link techniques, several different types of signal modulations may be used to maximize satellite efficiency depending on a variety of unexpected channel impairments. We propose a neural network algorithm capable of learning these RF signal modulations using a supervised learning technique designed for low power, high-efficiency in-orbit deployment. Thus, as signal modulation changes, a receiving satellite would be able to recognize this modulation update without adjustment from an operator, or any delay due to control signal transmission.The work presented demonstrates a convolutional neural network (CNN) capable of learning and recognizing a set of modulation schemes commonly used to transmit RF information. We are capable of recognizing the modulation scheme from the I and Q data channels directly, with no preprocessing or data conversion required other than breaking the incoming signal into a set of uniform normalized samples. We perform a network design and size analysis, showing that we can obtain reasonably high accuracy using networks with a relatively low number of trainable parameters. Given that a user of a system such as this may wish to receive a signal using a modulation scheme that the network has not previously learned, we demonstrate that transfer learning can learn new modulation schemes by retraining only the fully connected layers in the CNN. Thus, this type of network would excel in outer space deployment using high-efficiency transfer learning hardware. Modulation recognition can be performed through rapid feedforward computation, and the CNN training process is significantly simplified when learning new modulations is required.