Modulation and Classification of Mixed Signals Based on Deep Learning

TL;DR

This paper explores the use of various deep learning models, including CNN variants, LSTM, and CLDNN, to classify mixed signals in complex scenarios, improving accuracy over traditional methods.

Contribution

It introduces new deep learning approaches for mixed signal modulation classification and analyzes the impact of training data and methods on recognition accuracy.

Findings

Deep learning models improve classification accuracy for mixed signals.

Model complexity correlates with recognition performance.

Training data type and quantity significantly affect results.

Abstract

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.