Modulation Classification for MIMO-OFDM MIMO OFDM Signals via Approximate Bayesian Inference
Abstract: The problem of modulation classification for a multiple-antenna multiple antenna (multiple-input (multiple multiple-output output (MIMO)) system employing orthogonal frequency frequency-division multiplexing (OFDM) is investigated under the assumption of unknown frequencyfrequency selective fading channels aand signal-to-noise 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 distributi distributions ons 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 Bayesian approaches based on independent component