Parameter estimation Neural Networks

Parameter Estimation, Modulation Recognition and Demodulation of Communication Signals on Base Band
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During the improving modern communication techniques and communication systems, more and more requirements that connecting the systems will not interfere with the regular ones appear in the fields of point-to-multipoint networks, radio management and surveillance, malfunction detection in communication systems, and in the fields of communication reconnaissance and counterwork. This form of unauthorized access is often called patterns of non-cooperative communications. Those kinds of unauthorized wireless access consist of the following steps:parameter estimation, modulation recognition, parameter adjustment and blind demodulation, information analysis and information deceit. The first two are the basic and key steps. In order to demodulate signals blindly, the modulation type and communication parameters are required in advance, that is modulation recognition and parameter estimation is necessary.In order to recognize and demodulate the received signal correctly, we need to estimate symbol rate, carrier frequency offset and timing error in advance, sometimes as well as bandwidth, SNR and power. Estimating and compensating carrier frequency offset are to achieve carrier frequency synchronization. Estimating symbol rate and correcting timing error are used to synchronize symbol.The fundamental task of modulation recognition of communication signals is to recognize signals’ modulation type and other parameters for further analysis under complicated environment and noise interference. The early modulation recognition methods are operated artificially with the aid of kinds of instruments. It takes very long time for a skilled operator to recognize limited types of signals. Nevertheless, automatic modulation recognition can overcome the shortages of artificial recognition. It is more robust to estimation error, noise and fading effect. To recognize modulation type of signals involves the process of detection, estimation, feature extraction and classification. The research into automatic modulation recognition is to find a simple, fast and reliable method with high accuracy rate, which can distinguish large amounts of types under complicated communication background.Transform input data is needed to get the most effective identifying feature for valid classification. The extraction and selection of feature are very important, which determine the structure and performance of recognizer. So far, the methods of feature extraction include time domain technology, frequency domain technology, wavelet transform technology, high-order spectrum analysis technology, constellation shape technology and so on. Classification is to classify signal into its category according to the observed value of feature extracted. It is an important research issue to select a reasonable decision rule and structure. There are two main classifiers named neural network and binary tree.Our project focuses on the basic issues of parameter estimation and automatic modulation recognition in uncooperative correspondence based on software radio. In addition, demodulation are also taken into research. The objects are quadrature modulated communication signals on baseband. The research contents mainly consist of symbol rate estimation, carrier frequency offset estimation, modulation recognition and blind demodulation.

Source: Telecom Paper

Kluwer Academic Competitively Inhibited Neural Networks for Adaptive Parameter Estimation (The Kluwer International Series in Engineering and Computer Science)
Book (Kluwer Academic)

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