Reserch On Recognition And Cognition Technology Of Typical Communication Signals And Biomedical Signals

Cognitive radio technology is the most important modern wireless communication technology, can effectively carry out spectrum management, intelligent understanding of the entire communication process. In the field of biomedicine, cognitive radio also has a very important application value, can be allocated to the medical application of spectrum scarcity caused by interference problems. Cognitive radio technology is divided into hardware and software. The software part mainly has the cognition modulation recognition technology, the spectrum sensing technology and so on. In order to improve the efficiency of wireless resources, this paper studied the commonly used communication signal and biomedical signal recognition and cognitive technology. These technologies can help reduce the interference in wireless communication and telemedicine communication, contribute to the spectrum management of the signal and improve the spectrum utilization rate.Signal modulation and recognition technology in environmental monitoring and cognitive radio has a wide range of applications .The research of the modulation recognition and cognitive technology of communication signals have been carried out for many years. At present,the existing algorithms are divided into two kinds: one is based on the maximum likelihood discrimination algorithm, the algorithm is more complicated, the other algorithm is based on feature recognition, this algorithm is easy to implement. If the appropriate feature parameters are selected, it will have a good recognition effect.This paper focuses on the research of cognitive radio and biomedical signal recognition based on fractal. The aim of this paper is to obtain a modulation recognition algorithm with robust performance, low complexity and high recognition efficiency. In this paper, the existing algorithms are summarized, and the characteristics of each algorithm and their shortcomings are pointed out. On the basis of the above, the fractal theory is used to insure the noise, and some algorithms are innovated and the results are as follows:1. A modulation recognition algorithm of wireless communication signal based on harmonic mean fractal box dimension is proposed. Firstly,the received signal is preprocessed by Hilbert transform, then its box dimensions and kurtosis harmonic parameters are extracted, and the two parameters are averaged and averaged to form the characteristic parameter of harmonic mean fractal box dimension. And uses the decision tree theory to classify and recognize. The simulation results show that the proposed algorithm can achieve a recognition rate of over 80% for wireless signals with a signal to noise ratio (SNR) of -5dB under the 5GHz unlicensed frequency bands of WiFi, LTE-U, Bluetooth and ZigBee. Which is higher than the recognition rate of traditional algorithms, and has lower complexity, and is easy to be applied in engineering.2. An algorithm of signal modulation type recognition based on high order cumulant and normalized kurtosis is proposed. In the traditional signal classification and recognition, the signal high-order cumulant feature has good anti-noise performance, is widely used in signal modulation type identification. However, the higher-order cumulants of 2ASK and BPSK are equal to the higher-order cumulants of MFSKs such as 2FSK and 4FSK. Therefore, only the high-order cumulant is not enough to identify the signal. To solve this problem, the normalized kurtosis of the signal is extracted, the high-order cumulant and normalized kurtosis of the signal constitute the joint characteristic parameters, and identify the communication signal by using the cascade neural network classifier. The simulation results show that the proposed algorithm has low computational complexity and good anti - noise performance. For 2ASK, BPSK, 4ASK, 4PSK, 2FSK, 4FSK, 16QAM signals, In the signal to noise ratio of not less than 5dB, the test sample of not less than 100 conditions, the correct recognition rate of 87% or more.3. In the recognition of biomedical signal, the feature extraction and recognition of ECG are mainly studied. For the recognition of normal ECG and abnormal ECG signal, a new algorithm is proposed. Firstly, the cyclic frequency spectrum of ECG signal is obtained, and the alpha cycle frequency of ECG signal is obtained, then the fractal feature extraction is performed. ECG signals from the famous MIT-BIH database, verified by Matlab simulation experiments, in ECG signal recognition, the fractal intercept is more recognition than the fractal dimension.