Multi-Sensor Signal Fusion Based Modulation Classification by Using Wireless Sensor Networks

Abstract

Automatic blind modulation classification (MC) is deployed, as the intermediate step between signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is still a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted signal at the receiver. The proposed MC scheme based on multi-sensor signal fusion makes the premise that the combined signal from multiple sensors provides a more accurate description than any one of the individual signal alone. Multi-sensor signal fusion offers increased reliability and huge gains in overall performance as compared to the single sensor one, thus making automatic modulation classification (AMC) of weak signals in non-cooperative communication environment more reliable and successful. Modulation constellations improvements using multi-sensor signal fusion in the AWGN channel are studied first by using numerical simulations. In order to further study SNR improvement through multi-sensor signal fusion, Q-PSK signal SNR estimations using the M2M4 method after multi-sensor signal fusion with 10 sensors versus SNR are also presented. Finally, classification performances based on multi-sensor signal fusion in the AWGN channel are investigated and evaluated in terms of correct classification probability by taking the effects of timing synchronization, phase jitter, phase offset and frequency offset into consideration, respectively. Through Monte Carlo simulations, we demonstrate that the proposed multi-sensor signal fusion based AMC algorithm can greatly outperform other existing AMC schemes.