Large-sample modulation classification using Hellinger representation

Automatic modulation recognition has become important in wireless communications for both civilian and military purposes. Assuming a 5 dB signal-to-noise ratio (SNR), we studied modulation classification by an approach based on Hellinger distance (HD) methods. The advantages of this approach compared to either the likelihood method or the "key features" extraction method are robustness and simplicity. Also, a hierarchy of candidate modulation types can be automatically constructed; then a hierarchical recognition scheme is derived. Visualization of the hierarchy of modulation clustering can be obtained simply. A computational study of 15 modulation types is given.