Automatic Modulation Classification in Optical Wireless Communication Systems based on Cancellable Biometrics

Abstract Recently, automatic modulation classification (AMC) has acquired a lot of interest in the optical communication community. Most optical wireless communication systems are intended to transmit multimedia content, especially video and speech signals. The optical wireless communication 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 adaptive modulation classification at the receiver end. Instead of using complex classification with deep learning techniques, a simple proposed scheme for AMC is introduced in this paper. This proposed scheme is based on a chaotic Baker map (CBM), wavelet image fusion, and autocorrelation estimation. It depends on constellation diagrams for eight modulation formats, including (B/Q/8/16 PSK), (8/16/32/64 QAM). The constellation diagrams are acquired and utilized 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. Simulation results prove good classification and recognition for all studied modulation formats.