Modulation Classification for MIMO-OFDM Signals via Approximate Bayesian Inference

TL;DR

This paper introduces Bayesian inference techniques, including Gibbs sampling and variational inference, for classifying modulation types in MIMO-OFDM systems with unknown channels and SNR, demonstrating improved accuracy and convergence over existing methods.

Contribution

It develops Bayesian modulation classification methods for MIMO-OFDM systems that work under general conditions, outperforming existing non-Bayesian approaches.

Findings

Gibbs sampling converges to the optimal Bayesian solution.

Mean field variational inference improves accuracy for small samples.

Bayesian methods outperform existing non-Bayesian techniques.

Abstract

The problem of modulation classification for a multiple-antenna (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 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 assume 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 and on prior Bayesian methods based on the `superconstellation' method.