1、1Research on Modulation Recognition Algorithm of Digital Communication Signal Based on Wavelet DenoisAbstract: The paper researches a recognition algorithm of modulation signal and modulation modes. The modulation modes to be recognized include 2ASK, 2FSK, 2PSK, 4ASK, 4FSK and 4PSK modulation. There
2、 are two methods recognizing modulation modes of digital signal, method based on decision theory and pattern-recognition method based on feature extraction. The method based on decision theory is not suitable for recognition with multiple modulation modes. The core of pattern recognition based on fe
3、ature extraction is selection of feature parameters. So the paper uses the feature parameters with simple calculation, easy to be implemented and high recognition rate as the core. The extraction of feature parameters is based on instant feature of modulation signal after Hilbert transformation. Key
4、 words: modulation recognition, instant feature, feature parameter, wavelet denoising 1 Introduction With rapid development of modern communication technique, 2analogue modulation is out of date. Digital modulation has the characteristics of it has various forms, is easy to be controlled, encrypted
5、and integrated, so it is widely applied. Therefore, the paper researches recognition method of digital modulation mode. There are many modulation methods of digital signal, but most of them are in the stage of software simulation, and are infrequent for hardware implementation. In recent years, with
6、 the development of information processing technique, digital signal processing technique has developed to be a mainstream technique. Digital signal processor has the characteristics of rapid operation, programmability and flexible interface, which makes it play more and more important role in the d
7、evelopment and application of electronic products. And using chip to implement digital signal processing system is the development trend. So it has important significance for research on modulation signal recognition, especially it has wide application prospect for research on implementation of modu
8、lation mode recognition. 2 Digital Signal Modulation and Simulation In actual communication, most channels cant directly transfer baseband signal, so the transmission is completed by carriers. The waveform parameters of the carriers are 3controlled by baseband signal, and the parameters change with
9、the change of signals, which is carrier modulation. Theoretically, the waveform of carriers can be random. In practice, sinusoidal signal has the advantages of simple, easy to be generated and accepted, so it is generally selected as carrier in most communication systems. The common digital modulati
10、on modes include, FSK and PSK, based on which many other forms can be derived. The principles of the digital modulation modes are as follows. 2.1ASK signal ASKis the earliest digital modulation mode, and has been replaced by FSK and PSK.But it is the basis of researching other digital modulation mod
11、es. (1) 2ASK Digital signal consists of 0 and 1. If there are digital signals in which the probability of 0 is P, the probability of 1 is 1-P, and 2ASKsignal can be represented by the following formula. In formula 1, g(t) is the single rectangular pulse that the pulse width is Ts, fc is carrier freq
12、uency, and the value of an must meet the following relationship. 2.2 FSK signal 4FSK is the modulation method using different carrier frequency to represent features of baseband signal. It is the earliest modulation mode which is applied widely. (1) 2FSK 2FSK uses two carrier frequencies, f1 and f2
13、to represent digital signals, 0 and 1. If f1 is used to carry information of symbol 0, and f2 is sued to carry information of symbol 1, 2FSK signal can be represented by the following formula. (2) MFSK MFSKmodulation is the extension of 2FSK. The carrier frequency of MFSK has M values, MFSKcan be re
14、presented by the following formula. 2.4 Simulation of digital modulation signal Before simulating digital modulation signal, suitable sampling frequency should be selected firstly. From Nyquist sampling theorem, we can see that sampling frequency fs should meet(is the highest carrier frequency of).
15、In order to reflect the detailed information of digital modulation signal, the value of sampling frequencyshould be. After analyzing the principles of digital modulation signal and selecting carrier frequency, we can simulate the digital modulation signals, which can help us directly know and analyz
16、e 5the features of modulation signal. In order to be easy for observation, the parameter setting is as follows, and.And the number of code elements is 10. 3.Digital Modulation Signal Denoising Based on Wavelet Transform 3.1 Wavelet transform Wavelet transform is the branch of applied mathematics whi
17、ch was developed in the late 1980s. And several scientists perfected the theory of wavelet transform. It is I. Daubechies and S. Malla that introduced the theory into engineering application, especially in digital processing field. The following is an introduction on concept of wavelet transform. Th
18、e expression of the signal to be analyzed is x(t), and the signal square can be multiplied, . After the function (t) of basic wavelet displaces , it and x(t) are inner products under different scales a.And the wavelet transform of signal x(t) is 3.2 Multi-resolution analysis (1) Definition of multi-
19、resolution analysis The extension function of wavelet transform actually describes the range of the observed signals, which can be called resolution. So wavelet transform can be considered to be 6multi-resolution analysis of signal. And the definition is as follows. (3) Decomposition and Reconstruct
20、ion of frequency space The basic idea of multi-resolution analysis is to start from space V0 and decompose V0 into high-frequency part and low-frequency part, .And V1 continues to be decomposed. After J-level decomposition, we can get, and we can get the decomposition coefficient under each subspace
21、. And the step-by-step decomposition figure is as follows. In the above formula,(k) andare noisy signal, pure signal and wavelet coefficient of noise. Threshold denoising sets a threshold which is achieved from lots of experiments. And the coefficients which are less than the threshold are considere
22、d to be noisy signal, and that are greater than the threshold are considered to be pure signal. Hard threshold sets the part of wavelet coefficients which are less than the threshold to be 0, and retains the coefficients that are greater than the threshold. The hard threshold process can be represen
23、ted by the following formula. In the above formula,is wavelet coefficient, andis wavelet coefficient after threshold process, and is the threshold. Hard threshold directly sets the wavelet coefficients which 7are less than the threshold to be 0, and the coefficients which are greater than the thresh
24、old reduce the threshold. And soft threshold process can be expressed by the following formula. 3.5 Thresholds in the paper There are four common thresholds, and the paper uses Rigrsuret threshold. The calculation method of the threshold is as follows. (1)After taking absolute values, each element i
25、nof the noisy signals are ordered from small to large. Then, each element is squared, which can get the new sequence. (2)If the threshold is the square root of the k element in the sequence, the threshold produces risk, and the risk is N is the length of signals. (3) of the minimum risk value is fig
26、ured out, and Rigrsure threshold is 4 Analysis on Recognition Results Before and After Denoising After analyzing five feature parameters and researching denoising algorithm, the recognition algorithm needs to be simulated. The recognition parameters are set as follows. The carrier frequency is 20kHz
27、, sampling frequency is 160kHz, 8sampling number is 512, and the number of code elements is 16, which can show the values of baseband signals. The signal-noise ratio is 5dB, 10dB and 15dB, and the noise is additive white Gaussian noise. Each signal in the experiment is simulated for 100 times indepe
28、ndently. The experiment is simulated in MATLAB. The recognition results of recognition algorithm and receiving denoising process are as follows. Table 1 and Table 2 is the recognition results after denoising when SNR=5. We can see that the recognition rate of PSK and FSK signal after denoising impro
29、ves evidently, especially the recognition rate of FSK signal, which can reach 93% at least. So it makes denoising system useful in low signal-to-noise ratio. The following is the recognition results. Table 5 and Table 6 is the recognition results before and after denoising when SNR=15. We can see th
30、at the recognition rate of each signal when signal-to-noise ratio is 15dB is improved a lot. And it is evident the accurate times after denoising is improved. From the simulation results, we can see that the main problem is that the differentiation on 2PSK signal and 4PSK signal is not as good as ot
31、her signals. When signal-to-noise is 9low, adding denoising system is conductive to improve the probability of correct recognition of the system. 5 Summary For the problems and solutions of digital signal modulation recognition theory in implementation, the paper designs feature parameters which are
32、 not dependent on fixed amplitudes, and some problems in software implementation including FFT transform, real amplitude after FFT transform and algorithm transplant of analytic algorithm. It is a challenging and significant research to implement digital signal modulation mode recognition according
33、to the theory. There are many scholars for theoretical research on modulation mode recognition. They use new methods and new mathematical models, and achieve great achievements. But the methods need complicated calculation and tedious classification process, which makes the methods limited in hardwa
34、re implementation. For the objective of hardware implementation, the paper researches a simple modulation recognition algorithm with small calculation amount and high resolution rate. Reference 1F.F.Liedtke.Computer simulation of an automatic classification procedure for digitally modulated communic
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