Tracking changes in transmitter modulation type in a non-cooperative environment

Abstract

Automatic modulation classification (AMC) is a challenging task in a non-cooperative environment where channel state information and signal parameters are not always available. Non-cooperative transmissions in military environments may be hampering or threatening to a user's own goals. In this environment signals can use never before seen modulation types or even modulation types that are specifically designed to avoid interception, detection and classification. Modulation is in effect used here as another layer of encryption. Modulation types thus have to be classified blindly, that is, without the use of a priori signal and channel state information. Adaptive modulation techniques complicate the task of classifying adversaries' signals even more. It is desirable to be able to track the changes in an adversary emitter's modulation type, because the transmitter may be identified or their messages may be recovered, which is a critical aid in supporting battlefield decision making. The objective of this study is to classify and track changes of modulation types from a communications transmitter in a non-cooperative environment without channel state information. The secondary objective is to develop the method in such a way that the digital signal processing components thereof can be implemented on a hardware platform provided by the CSIR. Communication signals with modulation types Amplitude Shifts Keying (ASK) of order two and four, Phase Shift Keying (PSK) of order two and four, and Frequency Shift Keying (FSK) of order two and four were considered. The channel effects that were considered were AWGN noise and flat fading in a static multipath Rayleigh fading channel. A literature study was first performed to identify candidate algorithms for AMC that can be implemented on a hardware platform and the best classification algorithm that met the research objectives was selected. The performance of the selected algorithm was evaluated in both software and hardware under varying channel conditions whereafter the results were analysed and compared. The tracking of changes from one modulation type to another was performed by logging the modulation type over time. Feature based classification was selected to classify and track modulation types of a signal. Features based on the instantaneous amplitude, phase and frequency of a signal were used for feature extraction and a decision tree was used for classification. The method was tested under varying SNR conditions from 0 dB to 30 dB in an AWGN channel and flat fading conditions in a multipath Rayleigh fading channel at an SNR of 30 dB and 10 dB. Classification accuracy higher than 99 % was achieved on average for the SNR conditions. Classification performance of 97% and 93% was achieved on average for the fading conditions at 30 dB and 10 dB SNR respectively in software. The classification performance for hardware was 89% and 71% on average for the fading conditions at an SNR of 30 dB and 10 dB respectively. It was found that signal length has a significant effect on the classification performance.