Abstract
With the rapid development of intelligent communication systems, classical problems, such as automatic modulation classification (AMC), have gained extensive research interest. This is due to the significant role that AMC plays in many civilian and military applications. In this letter, we consider AMC for millimeter wave-over-fiber (MMWoF) communication. This type of communication is of practical interest because it enables centralized analysis and processing, taking the advantages of low transmission loss of MMW signals over fiber optic channels. In this letter, we use autoencoder neural networks for automatic features extraction and classification, preceded by a pre-processing step applied to the samples of the input signal. The performance of the system under consideration has been thoroughly investigated by simulation and verified experimentally under different impairments, including fiber chromatic dispersion and amplified spontaneous emission noise. The results are presented in terms of the probability of correct classification for different values of optical signal-to-noise ratio and different lengths of fiber channels. The results from simulation are in good match to those obtained experimentally.