DSP-based hierarchical neural network modulation signal classification

• Published in: IEEE transactions on neural networks Vol. 14; no. 5; pp. 1065 - 1071
• Main Authors: Namjin Kim, Kehtarnavaz, N., Yeary, M.B., Thornton, S.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2003
• Subjects: Amplitude modulation; Digital modulation; Digital signal processing; Digital signal processors; Feature extraction; Genetic algorithms; Neural networks; Pattern classification; Signal processing; White noise
• ISSN: 1045-9227; 1941-0093
• DOI: 10.1109/TNN.2003.816037

This paper discusses a real-time digital signal processor (DSP)-based hierarchical neural network classifier capable of classifying both analog and digital modulation signals. A high-performance DSP processor, namely the TMS320C6701, is utilized to implement different kinds of classifiers including a hierarchical neural network classifier. A total of 31 statistical signal features are extracted and used to classify 11 modulation signals plus white noise. The modulation signals include CW, AM, FM, SSB, FSK2, FSK4, PSK2, PSK4, OOK, QAM16, and QAM32. A classification hierarchy is introduced and the genetic algorithm is employed to obtain the most effective set of features at each level of the hierarchy. The classification results and the number of operations on the DSP processor indicate the effectiveness of the introduced hierarchical neural network classifier in terms of both classification rate and processing time.