Classification of digital modulation schemes using neural networks
Ganesh Arulampalam, Visalakshi Ramakonar, Abdesselam Bouzerdoum, Daryoush Habibi · 2003
Modulation recognition systems have to be able to correctly classify the incoming signal's modulation scheme in the presence of noise. This paper addresses the problem of automatic modulation recognition of digital communication signals using neural networks. Seven digital modulation schemes have been considered and seven features have been used as inputs to the neural network (NN) to perform the classification. Several NN structures have been tested that perform at over 99% accuracy at signal-to-noise ratios (SNR) of 10 dB. Design considerations for the NN classifier are discussed and the implementation of these has been shown to produce significant reduction in network size. The performance of the NN-based classifier has also been compared with that of a decision-theoretic classifier; it was found that the NN slightly outperforms the decision-theoretic classifier.