Open AccessGMSK NEURAL NETWORK BASED DEMODULATOR
Author(s) -
Andrea Aiello,
Domenico Grimaldi,
Sergio Rapuano
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.1.1.77
Subject(s) - demodulation , learning vector quantization , artificial neural network , computer science , minimum shift keying , gsm , electronic engineering , telecommunications , phase shift keying , artificial intelligence , bit error rate , engineering , channel (broadcasting)
In this paper, the pattern recognition characteristics of the Artificial Neural Net works are used to realise a real demodulator for Gaussian Minimum ShiftKeying signals, used in the GSM telecommunications. The demodulator utilises the Learning Vector Quantisation (LVQ) neural network. It offers both greater efficiency in demodulating and less sensitivity to noise. In order to solve the problem regarding input signal synchronisation, a preprocessing phase is organised. The demodulator prototype has been realised by implementing the preprocessing phase and the LVQ neural network on TMS320C30 Digital Signal Processor. The demodulator has been tested according to the European Telecommunication Standard Institute Recommendations.