Blind classification for linear and non-linear modulations based on the fusion of multiple features
- Resource Type
- Conference
- Authors
- Lei, Guowei; Shu, Qiang; Liao, Wenliang
- Source
- 2021 2nd International Conference on Computer Communication and Network Security (CCNS) CCNS Computer Communication and Network Security (CCNS), 2021 2nd International Conference on. :25-28 Jul, 2021
- Subject
- Computing and Processing
Wireless communication
Training
Backpropagation
Neurons
Modulation
Network security
Entropy
linear and non-linear modulations
multiple features
BP neural networks
blind classification
- Language
Blind identification for modulations is an important issue in signal processing and wireless communications. The role of modulation identification is to find out which type of modulations for the signals received. To investigate the classification for both linear and non-linear modulations, the fusion of multiple features is studied in terms of the cumulants, approximate entropy and kurtosis. The features are combined as the input vector of back propagation neural network, which is designed to discriminate multiple modulations. Training and test are verified via simulations finally.