Optimal decision fusion based automatic modulation classification by using wireless sensor networks in multipath fading channel

Automatic modulation classification (AMC) is deployed, as the intermediate step between signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted signal at the receiver; the problem will be more challenging in the multipath fading channel. The proposed AMC method based on optimal decision fusion by using wireless sensor networks provides a more accurate classification result than any one of the individual signal alone. Wireless sensor networks offer increased reliability and optimal decision fusion provides huge gains in overall classification performance as compared to that of the single sensor. Thus, optimal decision fusion based AMC by using wireless sensor networks greatly enhances classification performance of weak signals in non-cooperative communication environment. Classification performances of optimal decision fusion based AMC by using wireless sensor networks in the multipath fading channel are investigated and evaluated in terms of correct classification probability. Through Monte Carlo simulations, we demonstrate that the proposed AMC algorithm can greatly outperform that of single sensor in multipath fading channel.