Low Complexity Kolmogorov-Smirnov Modulation Classification

TL;DR

This paper introduces a low-complexity, non-parametric modulation classification method using the Kolmogorov-Smirnov test, which improves accuracy and speed over traditional methods in various communication scenarios.

Contribution

It develops a novel low-complexity K-S based modulation classifier that outperforms cumulant-based classifiers in accuracy and efficiency, applicable to AWGN and OFDM-SDMA systems.

Findings

Superior classification performance over traditional methods

Requires fewer signal samples for accurate classification

Effective in both AWGN and multiuser interference scenarios

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).