Low Complexity Kolmogorov-Smirnov Modulation Classification
Abstract
Kolmogorov-Smirnov (K-S) test-a non-parametric method to measure the goodness of fit, is applied for automatic modulation classification (AMC) in this paper. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic derived from the received signal, and comparing it with the CDFs of the signal under each candidate modulation format. The K-S-based modulation classifier is first developed for AWGN channel, then it is applied to OFDM-SDMA systems to cancel multiuser interference. Regarding the complexity issue of K-S modulation classification, we propose a low-complexity method based on the robustness of the K-S classifier. Extensive simulation results demonstrate that compared with the traditional cumulant-based classifiers, the proposed K-S classifier offers superior classification performance and requires less number of signal samples (thus is fast).
Cite
@article{arxiv.1102.5593,
title = {Low Complexity Kolmogorov-Smirnov Modulation Classification},
author = {Fanggang Wang and Rongtao Xu and Zhangdui Zhong},
journal= {arXiv preprint arXiv:1102.5593},
year = {2016}
}
Comments
This paper is accepted by IEEE WCNC 2011