Abstract
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.
Highlights
The Artificial Neural Networks (ANNs) are more efficient than the conventional algorithms and represent an interesting tool for advanced research and applications both in measurement and signal processing [1, 2].In particular the ANNs promise to be more ef ficient in recognising and distinguishing complex vectors according to their ability to generalise and to form some internal representations of the sup plied input signal [3, 4].These abilities make them very useful for the demodulation of the Gaussian Minimum Shift Keying (GMSK) signals, used in GSM telecom munications.In the GMSK signal, the information is carried by the phase of the modulated signal
The prototype of the neuralbased GMSK de modulator has been realised by implementing the Learn ing Vector Quantisation (LVQ) neural network on a Digital Signal Processor (DSP)
The preprocessing phase and the LVQ neural network are implemented on the DSP TMS320C30 by Texas Instruments [11]
Summary
The Artificial Neural Networks (ANNs) are more efficient than the conventional algorithms and represent an interesting tool for advanced research and applications both in measurement and signal processing [1, 2]. In particular the ANNs promise to be more ef ficient in recognising and distinguishing complex vectors according to their ability to generalise and to form some internal representations of the sup plied input signal [3, 4] These abilities make them very useful for the demodulation of the Gaussian Minimum Shift Keying (GMSK) signals, used in GSM telecom munications. The LVQ neural network shows the greatest benefit with regard to the char acteristics of simplicity and performance [9] This demodulator does not require recovery of the carrier phase and frequency. The prototype of the neuralbased GMSK de modulator has been realised by implementing the LVQ neural network on a Digital Signal Processor (DSP) This prototype permits to test the demodu lator performance according to the international recommendations. The test results, according to the international recommendations, are given in order to evaluate the performance of this neural demodu lator versus the Signal to Noise Ratio (SNR)
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