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Modulation Recognition Scheme Using Mixed Model

Author: YaoXuQing
Tutor: WangHuaKui
School: Taiyuan University of Technology
Course: Communication and Information System
Keywords: Modulation Recognition Higher order cumulants Mixed modulation Multi - classification neural network
CLC: TN761
Type: Master's thesis
Year: 2011
Downloads: 75
Quote: 1
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Abstract


With the development of communication technology, digital communication plays an increasingly important role in the daily life and work. The signal modulation is crucial in the field of communications technology, is the basic link for signal transmission, transmission and reception. Identification of the type of modulation current research topic has become the communications field, which signal automatic modulation recognition is become the hot research topic in the field of modulation recognition. Identification of digital modulation types used in digital confirmation, interference of discern, radio listener, electronic warfare, and software radio field. Whether military or civilian aspects of communication signal modulation recognition has important applications. In the investigative aspects of military signal, the signal recognition applications is extremely broad signal diagnostics signal listener and signal to identify the scope of its application. In the civil context, communication No. modulation recognition is mainly used in the management of the radio spectrum. Accompanied by the rise of the software radio technology, automatic signal modulation recognition technology is becoming more and more important. Firstly, the basic theory of signal modulation recognition described and simulation, followed by the article on the basis of theoretical knowledge in order cumulants, also do a more detailed description and simulation the signal eigenvalue extraction details. Finally, we propose two signal modulation recognition algorithm: the known SNR case, the use of automatic modulation recognition algorithm for multi-classification neural network and automatic modulation recognition algorithm in the case of unknown signal-to-noise ratio, the hybrid neural network. As can be seen by comparison of two methods of noise on the signal modulation recognition, joined the order cumulants characteristic parameters, reduced noise interference, signal recognition and signal recognition rate. From the experimental results can be seen that the use of mixed modulation identification method, greatly improved digital signal modulation recognition rate. This work: 1. Described several common digital signal modulation principle, which added signal of quadrature amplitude modulation (MQAM), and several common digital signal when the frequency characteristic analysis simulation. 2 order cumulants basic theoretical knowledge and feature extraction. First expounded signal order higher moments and higher order cumulants. Secondly, the the instantaneous characteristic parameters of the signal is introduced, the main analysis of the signal's amplitude, frequency and phase. Signal transient characteristics and simulation analysis. 3. Mainly on digital signal feature extraction algorithm. Then, according to the different ways of each of the modulation signal proposed for the the seven characteristic value of the signal automatic modulation recognition. Note signal feature extraction followed by a detailed exposition. 4. Research mainly on the use of neural networks for signal modulation recognition algorithm. First introduces the basics of the neural network, which focuses on the basic concepts and basics of the BP neural network. Improved BP neural network. Finally, based on improved BP neural network, the proposed multi-classification neural network and the hybrid neural network are two modes. The use of hybrid neural network to identify research and experimental simulation of digitally modulated signals in the case of multi-classification neural network and the unknown signal-to-noise ratio to use in the case of a known signal-to-noise ratio, respectively. Through the comparison of these two methods, on the one hand, to prove the impact of noise on the signal modulation recognition. The other hand, show that in the case of unknown signal-to-noise ratio, by adding higher order cumulants can effectively improve the recognition rate of the signal. The experiment proved that this method has a high recognition rate, the feasibility good.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Basic electronic circuits > Modulation technique and the modulator,demodulator and the demodulator > Modulation technique and the modulator
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