PCMA signal modulation identification method based on transfer learning

PCMA (Paired carrier multiple access, PCMA) signal is a new type of digital modulation for satellite communication, which has been widely used because it can save up to 50% of the frequency band resources by mixing and superimposing the transmit and receive carriers of duplex link, and has excellent performance in interception resistance. Blind separation of PCMA signals is an important part of radio spectrum monitoring, high-precision modulation parameter information is the necessary basis for separation. In order to process the PCMA signal normally, it is necessary to distinguish the PCMA signal from the common single-channel satellite digital modulation signal, and then identify the modulation mode of the PCMA signal. The modulation identification of the PCMA signal provides the necessary guidance for parameter estimation and blind processing. In this paper, a PCMA signal modulation type recognition algorithm based on a fusion model of time-frequency analysis, residual network and transfer learning is proposed to address the problems of small data volume, difficulty in extracting feature parameters, and complexity of classifier model parameters of the current PCMA signal modulation type recognition algorithm.