Title :
A 1-Dimension Structure Adaptive Self-Organizing Neural Network for QAM Signal Classification
Author :
Cheng, Hanwen ; Han, Hua ; Wu, Lenan ; Chen, Liang
Author_Institution :
Southeast Univ. Sipailou, Nanjing
Abstract :
The 1-dimension structure adaptive self-organizing neural network (1-DSASONN) has been presented as an extended 1-dimension version of the self-organizing map, which has better performance in a modulation classification method proposed in this paper for QAM signals. 1-DSASONN can start with arbitrary number of neuron, grow or prune many neurons and adoptively adjust network structure as well as weights. This feature improves the efficiency of classification algorithm which utilizes the number of sets of equal amplitude as classification feature. Simulation results show that the modulation classification method is robust in the presence of phase estimation error.
Keywords :
neural nets; quadrature amplitude modulation; self-organising feature maps; signal classification; QAM signal classification; adaptive self-organizing neural network; classification feature; modulation classification method; phase estimation error; self-organizing map; Adaptive systems; Clustering algorithms; Constellation diagram; Digital modulation; Gaussian noise; Neural networks; Neurons; Pattern classification; Quadrature amplitude modulation; Surveillance;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.3
