Modulation Classification in MIMO Systems With Distribution Test Ensemble

Abstract

In classification of signal modulation types in MIMO systems, it is difficult to achieve both high accuracy and high computational efficiency at the same time. State-of-the-art likelihood based methods incur massive increase in computational complexity when the number of transmitting antennas and the order of modulation increase. To make modulation classification feasible in time critical systems, we propose a low complexity algorithm with an ensemble of distribution tests. Three goodness-of-fit and a novel variance based distribution tests are employed to examine the mismatch between unknown signal and different modulation hypotheses. The results from all tests are combined by a multilayer perceptron classifier for improved robustness under a variety of channel conditions including AWGN channel and slow fading channels. The resulting solution achieves performance close to the maximum likelihood classifier at high SNR. Yet, it requires much lower computational complexity in all cases.

A Semi-Supervised Modulation Identification in MIMO Systems: A Deep Learning Strategy

“…Also, it has been shown that this approach exhibited superior performance compared to the J48 classifier. Gao et al proposed Distribution Test Ensemble (DTE) classifier to classify M-QAM and M-PSK modulations in MIMO systems [32]. They included weather data such as cloud cover and temperature as predictors.…”