Abstract
Due to high spectral and power efficiency, continuous phase modulation (CPM) is widely adopted in wireless communications, such as satellite communications. In this letter, we investigate the classification of CPM signals in the presence of unknown fading channels. The time-varying phases of CPM are first formulated as a hidden Markov model (HMM) by observing its memorable property. Then, a likelihood-based classifier is proposed using the Baum-Welch algorithm, which is able to estimate the unknown parameters in the HMM. Simulation results show that the proposed algorithm outperforms the existing scheme using approximate entropy in terms of classification performance.
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