GMSK neural network based demodulator

A. Aiello, D. Grimaldi, S. Rapuano
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引用次数: 14

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.
基于GMSK神经网络的解调器
利用人工神经网络的模式识别特性,实现了GSM通信中高斯最小移位键控信号的真实解调。该解调器采用学习向量量化(LVQ)神经网络。它提供了更高的解调效率和更低的噪声敏感性。为了解决输入信号同步的问题,组织了预处理阶段。通过在TMS320C30数字信号处理器上实现预处理相位和LVQ神经网络,实现了解调样机。该解调器已根据欧洲电信标准协会的建议进行了测试。
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