Automatic modulation classification in optical wireless communication systems based on cancellable biometric concepts

Automatic modulation classification (AMC) has recently acquired a lot of interest in the optical wireless communication (OWC) community. The OWC channel has variable characteristics. Hence, there is a need for an adaptive modulation scheme to cope with the varying channel characteristics. Adaptive modulation requires the implementation of AMC at the receiver side. Instead of using complex classification with deep learning (DL) techniques, a simple proposed scheme for AMC is introduced in this paper. This scheme is based on chaotic Baker map (CBM), wavelet image fusion, and autocorrelation estimation. It depends on constellation diagrams for eight modulation formats, including (2/4/8/16 PSK), and (8/16/32/64 QAM). First, the constellation diagrams are acquired and scrambled through the CBM, and they are merged using the wavelet image fusion and stored as reference templates in the system database. After that, the classification of each modulation format depends on estimated correlation scores and a thresholding strategy similar to the strategy adopted in cancellable biometric systems that depend on encrypted templates. Simulation results prove good classification for all studied modulation formats.