  With the rapid development of information nowadays, spectrum resources are
becoming more and more scarce, leading to a shift in the research direction
from the modulation classification of a single signal to the modulation
classification of multiple signals on the same channel. Therefore, the
emergence of an effective mixed signals automatic modulation classification
technology have important significance. Considering that NOMA technology has
deeper requirements for the modulation classification of mixed signals under
different power, this paper mainly introduces and uses a variety of deep
learning networks to classify such mixed signals. First, the modulation
classification of a single signal based on the existing CNN model is
reproduced. We then develop new methods to improve the basic CNN structure and
apply it to the modulation classification of mixed signals. Meanwhile, the
effects of the number of training sets, the type of training sets and the
training methods on the recognition accuracy of mixed signals are studied.
Second, we investigate some deep learning models based on CNN (ResNet34,
hierarchical structure) and other deep learning models (LSTM, CLDNN). It can be
seen although the time and space complexity of these algorithms have increased,
different deep learning models have different effects on the modulation
classification problem of mixed signals at different power. Generally speaking,
higher accuracy gains can be achieved.
