Title :
GMSK neural network based demodulator
Author :
Aiello, Andrea ; Grimaldi, Domenico ; Rapuano, Sergio
Author_Institution :
Fac. di Ingegneria, Univ. del Sannio, Benevento, Italy
Abstract :
The pattern recognition characteristics of the Artificial Neural Networks are used to realise a real demodulator for Gaussian Minimum Shift-Keying signals, used in the GSM telecommunications. The demodulator utilizes the learning vector quantization (LVQ) neural network. It offers both greater efficiency in demodulating and less sensitivity to noise. In order to solve the problem regarding input signal synchronization, a pre-processing phase is organised. The demodulator prototype has been realised by implementing the pre-processing phase and the LVQ neural network on TMS320C30 digital signal processor. The demodulator has been tested according to the European Telecommunication Standard Institute recommendations
Keywords :
demodulators; minimum shift keying; neural nets; pattern recognition; radio access networks; vector quantisation; GMSK neural network based demodulator; GSM telecommunications; Gaussian Minimum Shift-Keying signals; LVQ neural network; TMS320C30 digital signal processor; input signal synchronization; learning vector quantization neural network; noise sensitivity; pattern recognition characteristics; Artificial neural networks; Demodulation; Digital signal processors; GSM; Neural networks; Pattern recognition; Prototypes; Telecommunication standards; Testing; Vector quantization;
Conference_Titel :
Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, International Workshop on, 2001.
Conference_Location :
Crimea
Print_ISBN :
0-7803-7164-X
DOI :
10.1109/IDAACS.2001.941967
