Multi-receiver modulation classification for non-cooperative scenarios based on higher-order cumulants

Abstract

Modulation Classification (MC) is a difficult task that can increase awareness in Cognitive Radio (CR) applications. Much of the research in MC has been for single antenna and single user scenarios. With multiple users, blind source separation (BSS) techniques have successfully been used to separate a linear mixture of signals. This work demonstrates that results for MC in a single-user MIMO communications system can be extended to MC in a multi-user scenario with the use of blind source separation techniques. However, a number of difficulties exist with the use of blind source separation techniques that make a simple extension (difficult) possible. First, since the number of users is unknown, BSS techniques must attempt to separate signals with the assumption that a larger number of users exist (than are actually present). Second, BSS techniques can separate signals up to an ambiguity in phase, order, and magnitude—further complicating an extension of common classification methods. Lastly, well-known BSS techniques sometimes fail to properly separate even common digital modulations. The proposed approach to solve these issues comprises of the fastICA BSS technique for signal separation Hyvarinen and Oja (Neural Netw Off J Int Neural Netw Soc 13(4–5):411–430, 2000), fourth and sixth-order cumulants as distinguishing features for several digital modulations, and support vector machines with a radial basis function for classification. Given four common modulation schemes BPSK, QPSK, 8-PSK, and 16-QAM, the proposed approach classifies correctly more than 50% of the time for signal to noise ratios higher than 0 dB.