2015/09/30 by Yu Liu, Osvaldo Simeone, Alexander M. Haimovich +2 · 13 citations
Computer Science · Engineering · Mathematics · #Advanced Wireless Communication Techniques #Bayesian inference #Bayesian probability #Bayesian statistics #Dirichlet distribution #Gibbs sampling #Inference #Modulation (music) #Pattern recognition (psychology) #Prior probability #Wireless Communication Security Techniques #Wireless Signal Modulation Classification #cs.IT #math.IT
paper · pdf · doi:10.1109/tvt.2016.2537791
published in IEEE Transactions on Vehicular Technology, 1 (Institute of Electrical and Electronics Engineers) · To be appear in IEEE Trans. Veh. Technology
openalex publication_date 2016/01/01 · arxiv created 2016/04/08 · arxiv updated 2016/04/11 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
The problem of modulation classification for a multiple-antenna (multiple-input multiple-output (MIMO)) system employing orthogonal frequency-division multiplexing (OFDM) is investigated under the assumption of unknown frequency-selective fading channels and signal-to-noise ratio (SNR). The classification problem is formulated as a Bayesian inference task, and solutions are proposed based on Gibbs sampling and mean field variational inference. The proposed methods rely on a selection of the prior distributions that adopts a latent Dirichlet model for the modulation type and on the Bayesian network (BN) formalism. The Gibbs sampling method converges to the optimal Bayesian solution, and using numerical results, its accuracy is seen to improve for small sample sizes when switching to the mean field variational inference technique after a number of iterations. The speed of convergence is shown to improve via annealing and random restarts. While most of the literature on modulation classification assumes that the channels are flat fading, that the number of receive antennas is no less than that of transmit antennas, and that a large number of observed data symbols are available, the proposed methods perform well under more general conditions. Finally, the proposed Bayesian methods are demonstrated to improve over existing non-Bayesian approaches based on independent component analysis (ICA) and on prior Bayesian methods based on the “superconstellation” method.