Abstract
In this paper, a new likelihood-based method for classifying phase-amplitude-modulated signals in Additive White Gaussian Noise (AWGN) is proposed. The method introduces a new Markov Chain Monte Carlo (MCMC) algorithm, called the Adaptive Metropolis (AM) algorithm, to directly generate the samples of the target posterior distribution and implement the multidimensional integrals of likelihood function. Modulation classification is achieved along with joint estimation of unknown parameters by running an ergodic Markov Chain. Simulation results show that the proposed method has the advantages of high accuracy and robustness to phase and frequency offset.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
