论文部分内容阅读
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.
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.