CN104280611A - High-frequency signal spectrum sensing method based on stochastic resonance systems - Google Patents

Abstract

The invention discloses a high-frequency signal spectrum sensing method based on stochastic resonance systems. According to the method, a dimension conversion method is used, the stochastic resonance systems are made to be suitable for detecting high-frequency main user signals, the two or more stochastic resonance systems are cascaded, and through the property that the stochastic resonance systems are cascaded, output signals of the inferior stochastic resonance systems are smoother that output signals of the superior stochastic resonance systems, influences of noise high-frequency glitches on detection results are reduced, and the probability and the success rate of main user signal detection are increased.

Description

A kind of high-frequency signal frequency spectrum sensing method based on stochastic resonance system

Technical field

The high-frequency signal frequency spectrum sensing method of a kind of cascade stochastic resonance system based on change of scale that the present invention proposes, is applicable to the detection probability and the success ratio that improve primary user's signal, belongs to cognition wireless electrical domain.

Background technology

Recent years, input science is in ceaselessly progress and development, and input refers to the extraction be in feeble signal under strong neighbourhood noise, detection.At present, for input, domestic and international each researcher is proposed oneself method, such as noise filtering, signal amplifies and optimum matching detects etc.But the detection for feeble signal also has signal to be in detection under strong noise environment, and traditional signal detecting method is just often felt simply helpless.At present, correlative study shows to use nonlinear theory and technology more efficiently can process the test problems of feeble signal, particularly Stochastic Resonance Theory (stochastic resonance, SR) theoretical and chaos (chaos) theory, the detection for input especially feeble signal provides new method.Stochastic Resonance Theory describes a kind of nonlinear principle, namely when a comparatively faint signal is surrounded compared with very noisy, if by a nonlinear system, and feeble signal, neighbourhood noise and nonlinear system three are when reaching certain matching relationship, the energy of neighbourhood noise can to the energy trasfer of feeble signal, in other words, the energy dropoff of neighbourhood noise while the energy of feeble signal strengthens, this just can improve the signal to noise ratio (S/N ratio) of mixed signal.

When Stochastic Resonance Theory is used for Detection of Weak Signals, process and the common ground of other signal detecting methods signal to noise ratio (S/N ratio) that to be final goals be all in order to improve mixed signal of sampling, be convenient to detect feeble signal.And maximum difference is Stochastic Resonance Theory utilizes noise, and noise energy is shifted, and on the contrary, traditional signal detecting method major part is the mode by attenuating noise or noise isolation.The Stochastic Resonance Theory Land use systems different to noise is that the test problems of feeble signal provides new thinking.

But, according to linear response theory and adiabatic approximation theory, traditional stochastic resonance system is only applicable to the detection of low-frequency cycle primary user signal, although there is adaptive algorithm, stochastic resonance system is made to be applicable to the environment of different noise intensity, but once the frequency of primary user's signal becomes higher-frequency (frequency is greater than 1Hz), Stochastic Resonance Theory is just no longer applicable to the detection of signal.This just needs the method introducing change of scale, makes traditional stochastic resonance system be applicable to the detection of high frequency primary user signal.But, traditional scale transformation method can reduce the sampling step length of cognitive user, this just has higher requirement to sampling precision and sampling rate, and meanwhile, the less sampling period means that frequency spectrum perception more easily can be subject to the impact of stochastic resonance system output signal high-frequency noises burr.

Summary of the invention

Technical scheme: based on stochastic resonance system high-frequency signal frequency spectrum sensing method detect high frequency primary user's signal time key step as follows:

1). cognitive user carries out perception to surrounding environment, and the mixed signal sampled is sent to fusion center;

2). fusion center is estimated primary user's signal frequency that may exist in mixed signal;

3). according to the flow process of change of scale, mixed signal is amplified, and the parameter of stochastic resonance system at different levels is adjusted;

4). sampling mixed signal is sent to the stochastic resonance system of cascade by fusion center, processes;

5). fast fourier transform is carried out to the output of second level stochastic resonance system, and uses traditional frequency spectrum perception algorithm to detect primary user's signal.

Beneficial effect: the present invention compared with prior art, has the following advantages:

Be not suitable for the problem detecting high-frequency signal for traditional stochastic resonance system, first the method utilizes change of scale, makes stochastic resonance system be applicable to high frequency master and detects, decrease detecting period simultaneously, improve perception efficiency.Then be further processed for stochastic resonance system output signal, high frequency component signal reduced further, makes output signal " smooth " more, improve the detection probability to primary user's signal and success ratio.

Accompanying drawing explanation

Fig. 1 n level of the present invention cascade stochastic resonance system model.

Fig. 2 high-frequency signal frequency spectrum sensing method flow process based on accidental resonance of the present invention.

Specific embodiments

1. change of scale

The method of mathematics mesoscale conversion is incorporated in Stochastic Resonance Theory, makes traditional stochastic resonance system be applicable to the detection and treatment of actual wireless communication environment medium-high frequency primary user signal.

If be applied to by Stochastic Resonance Theory in frequency spectrum perception, can represent with s (t)=Acos ω t the weak periodical primary user signal treating perception, wherein A represents the amplitude of primary user's signal, and ω represents the frequency of primary user's signal.Γ (t) represents that around cognitive user, average is 0, noise intensity is the white Gaussian noise of D, and x (t) represents the sampling mixed signal after stochastic resonance system process, and a, b are two variable elements of stochastic resonance system.

Corresponding stochastic resonance system model is as follows:

$\left\{\begin{array}{c}\frac{\mathrm{dx}}{\mathrm{dt}}=\mathrm{ax}-{\mathrm{bx}}^3+A\cos \mathrm{\&omega;t}+\mathrm{\&Gamma;}\left(t\right)\\ <\mathrm{\&Gamma;}\left(t\right)>=<\mathrm{\&Gamma;}\left(t\right),\mathrm{\&Gamma;}\left(0\right)>=2\mathrm{D\&delta;}\left(t\right)\end{array}\right.---\left(1\right)$

In above formula, δ (t) is unit impulse function, if a>0 and b>0, can the mode of replacing variable be used to be normalized conversion above-mentioned stochastic resonance system model, that is:

$z=x\sqrt{\frac{b}{a}}---\left(2\right)$

τ＝at (3)

Formula (2) and formula (3) are substituted into formula (1) can obtain:

$a\sqrt{\frac{a}{b}}\frac{\mathrm{dz}}{\mathrm{d\&tau;}}=a\sqrt{\frac{a}{b}}z-a\sqrt{\frac{a}{b}}{z}^3+A\cos \left(\frac{\mathrm{\&omega;}}{a}\mathrm{\&tau;}\right)+\mathrm{\&Gamma;}\left(\frac{\mathrm{\&tau;}}{a}\right)---\left(4\right)$

Wherein, noise meet:

$<\mathrm{\&Gamma;}\left(\frac{\mathrm{\&tau;}}{a}\right),\mathrm{\&Gamma;}\left(0\right)>=2\mathrm{Da\&delta;}\left(t\right)---\left(5\right)$

Therefore, derivation can obtain:

$\mathrm{\&Gamma;}\left(\frac{\mathrm{\&tau;}}{a}\right)=\sqrt{2\mathrm{Da}}\mathrm{\&xi;}\left(\mathrm{\&tau;}\right)---\left(6\right)$

Wherein, < ξ (τ) >=0, < ξ (τ), ξ (0) >=δ (τ), namely ξ (τ) represents that average is 0, and variance is the white Gaussian noise of 1.

Formula (6) is substituted into formula (4) can obtain:

$a\sqrt{\frac{a}{b}}\frac{\mathrm{dz}}{\mathrm{d\&tau;}}=a\sqrt{\frac{a}{b}}z-a\sqrt{\frac{a}{b}}{z}^3+A\cos \left(\frac{\mathrm{\&omega;}}{a}\mathrm{\&tau;}\right)+\sqrt{2\mathrm{Da}}\mathrm{\&xi;}\left(\mathrm{\&tau;}\right)---\left(7\right)$

By above formula abbreviation, can obtain:

$\frac{\mathrm{dz}}{\mathrm{d\&tau;}}=z-z^3+\sqrt{\frac{b}{a^3}}A\cos \left(\frac{\mathrm{\&omega;}}{a}\mathrm{\&tau;}\right)+\sqrt{\frac{2\mathrm{Db}}{a^2}}\mathrm{\&xi;}\left(\mathrm{\&tau;}\right)---\left(8\right)$

Contrast (8) and formula (1), can discoverable type (8) be the normalized form of formula (1), these two formulas be of equal value in form.Meanwhile, in formula (8), the frequency of primary user's signal is ω/a, compared with before converting with normalization, and the 1/a before having become.This explanation, if want to utilize stochastic resonance system to detect primary user's signal of high frequency, can according to the principle of the conversion of normalization above, in the mode of change of scale, proportionally adjust, amplify parameter a and the b of original stochastic resonance system, make high frequency primary user signal normalization become the signal of low frequency, Stochastic Resonance Theory so just can be made to be applicable to the detection of high-frequency signal, adjust as follows:

First, the reference foundation of one group of best stochastic resonance system parameter as change of scale is chosen.Each parameter is as follows: primary user's signal amplitude A ₀, frequency f ₀; White Gaussian noise expects E=0, variance stochastic resonance system parameter a ₀, b ₀; Stochastic resonance system sampling step length h ₀.

Suppose to treat that perception high frequency primary user signal frequency is f, the mixing sampled signal treating perception carries out fast fourier transform, uses following formula to estimate the frequency f of primary user's signal that may exist in mixed signal:

$f=\frac{1}{L}[\frac{r\left|G_{\max +r}\right|}{\left|G_{\max }\right|+\left|G_{\max +r}\right|}+k_{\max }]---\left(9\right)$

In above formula, L represents cognitive user total sampling time, and kmax represents sampling mixed signal fast fourier transform maximum amplitude spectrum position, and Gmax represents the amplitude of amplitude spectrum in kmax position.Wherein, | Gmax+1| >=| during Gmax-1|, r=1, otherwise r=-1.

After estimating the primary user signal frequency f and may exist, by following parameter a, b and sampling step length h method of adjustment as follows:

$a=\frac{f}{f_0}{a}_0---\left(10\right)$

$b=\frac{f}{f_0}{b}_0---\left(11\right)$

$h=\frac{f_0}{f}{h}_0---\left(12\right)$

When carrying out matlab emulation, because sampling is discrete, the noise variance σ therefore in environment ²relevant with the noise intensity size of reality and sampling step length h two factors:

${\mathrm{\&sigma;}}^2=\frac{2D}{h}---\left(13\right)$

Be D before change of scale ₀, σ ₀; After conversion is D, σ; Noise intensity D=2D after change of scale ₀b/a ², in addition, because the frequency of primary user's signal reduces after normalization, therefore sampling step length h also will amplify accordingly, becomes original a doubly, so, through change of scale, the variances sigma of Gaussian noise ²=4D ₀b/ (a ³h), after change of scale with change of scale before the ratio of variance be:

$\frac{{\mathrm{\&sigma;}}^2}{{\mathrm{\&sigma;}}_0^2}=\frac{2b}{a^3}---\left(14\right)$

Can push away Normalized Scale conversion is equivalent to and becomes original by primary user's signal amplitude times, and the variance of neighbourhood noise becomes original 2b/a ³doubly:

$A=\sqrt{\frac{2b}{a^3}}{A}_0---\left(15\right)$

${\mathrm{\&sigma;}}^2=\frac{2b}{a^3}{{\mathrm{\&sigma;}}_0}^2---\left(16\right)$

According to the analysis of change of scale principle and derivation, the high frequency primary user signal of perception is treated in the inverse transformation utilizing Normalized Scale to convert and stochastic resonance system parameter carries out parameter adjustment, makes stochastic resonance system be applicable to the detection of high frequency primary user signal.

2. cascade stochastic resonance system model

The stochastic resonance system of n level cascade can regard the series connection of n traditional stochastic resonance system as, and the output of upper level stochastic resonance system, as the output of next stage stochastic resonance system, can be expressed as Fig. 1.

The every one-level of cascade stochastic resonance system is all optimizing again upper level, the accidental resonance characteristic of performance at different levels is identical, and in the signal processing applications of reality, two-stage cascade stochastic resonance system structure is relatively simple, effect of optimization is comparatively obvious, therefore applies the most general.Therefore, the present invention starts with from the simplest two-level concatenation stochastic resonance system, is studied the characteristic of cascade stochastic resonance system and feasibility.Two-level concatenation stochastic resonance system model can represent with formula below:

$\left\{\begin{array}{c}\frac{{\mathrm{dx}}_1}{\mathrm{dt}}={\mathrm{ax}}_1-{\mathrm{bx}}_1^3+s\left(t\right)+\mathrm{\&Gamma;}\left(t\right)\\ \frac{{\mathrm{dx}}_2}{\mathrm{dt}}={\mathrm{ax}}_2-{\mathrm{bx}}_2^3+{\mathrm{kx}}_1\end{array}\right.---\left(17\right)$

Wherein, s (t)=Acos ω t represents the weak periodical primary user signal treating perception; Γ (t) represents that in cognitive user surrounding environment, average is 0, variance is σ ²white Gaussian noise, and d represents noise intensity, and represent that average is 0, variance is the white Gaussian noise of 1; x ₁with x ₂represent the sampling mixed signal (x after the first order and second level stochastic resonance system process respectively ₁input signal as second level stochastic resonance system); A, b are two variable elements of stochastic resonance system, and k is the enlargement factor of cascade system.

Cascade stochastic resonance system is formed by two identical traditional stochastic resonance system cascades, the method coefficient of intermediate amplifier is set to K=1 usually, if the sampling mixed signal of input is very faint, when second level stochastic resonance system exports and is still difficult to detect primary user's signal, K can be set to larger numerical value.

By above to the analysis of stochastic resonance system principle, can know that the mixing sampled signal of cognitive user is after the process that have passed through first order stochastic resonance system, no matter can stochastic resonance system there is best accidental resonance, and the FFT amplitude spectrum of the treated mixed signal that stochastic resonance system exports is made up of two parts: one is the S relevant with cycle primary user signal to be detected ₁(f), S ₁f () is that two is Ss relevant with Gaussian noise with primary user's signal frequency in input mixed signal with frequently ₂(f), and S ₂f () meets Lorentz distribution:

$S_1\left(f\right)=\frac{2a^2{u}^d\exp ({-u}^2/2D)/\left(\mathrm{\&pi;}{D}^2\right)}{{\left(2\mathrm{\&pi;}{f}_0\right)}^2+(2u^2\exp ({-u}^2/2D)/{\mathrm{\&pi;}}^2)}\mathrm{\&delta;}(f_0-f)---\left(18\right)$

$S_2\left(f\right)=[1-\frac{a^2{u}^3\exp ({-u}^2/2D)/\left({\mathrm{\&pi;D}}^2\right)}{{\left(2\mathrm{\&pi;}{f}_0\right)}^2+({2u}^2\exp ({-u}^2/2D)/{\mathrm{\&pi;}}^2)}]---\left(19\right)$

Wherein, u is relevant with two parameter a, b of stochastic resonance system, and meets u=a/b.F ₀for the frequency of primary user's signal in cognitive user sampled signal.D is environmental noise level.

Lorentz distribution represents that the FFT amplitude spectrum concentration of energy of output signal is in the lower region of frequency, and it is less at the energy of high-frequency region, this is because Stochastic Resonance Phenomenon not only can make noise energy shift to primary user's signal energy, also can make noise high frequency energy in mixed signal that transfer to a certain degree occurs to low frequency energy direction simultaneously, this transfer can make original Gauss's flat noise be converted into " look " noise, namely compose structure and there occurs change, be no longer uniformly distributed.

By by two traditional stochastic resonance system cascades, the energy of the high-frequency signal in first order stochastic resonance system can being outputed signal shifts to low frequency signal further, high fdrequency component is made also to be that " burr " greatly reduces, this just can make output signal become more smooth, further reduction frequency glitches is on the impact of testing result, and make the comparatively intensive low frequency region of noise energy become narrower, improve the detection probability to primary user's signal.

Claims (3)

1., based on a high-frequency signal frequency spectrum sensing method for stochastic resonance system, it is characterized in that comprising the following steps when detecting high frequency primary user's signal:

Step 1: cognitive user carries out perception to surrounding environment, is sent to fusion center by the mixed signal sampled;

Step 2: fusion center is estimated primary user's signal frequency that may exist in mixed signal;

Step 3: mixed signal is amplified according to the flow process of change of scale, and the parameter of stochastic resonance system at different levels is adjusted;

Step 4: sampling mixed signal is sent to the stochastic resonance system of cascade by fusion center, processes;

Step 5: fast fourier transform is carried out to the output of second level stochastic resonance system, and use traditional frequency spectrum sensing method to detect primary user's signal.

2. a kind of high-frequency signal frequency spectrum sensing method based on stochastic resonance system according to claim 1, is characterized in that the change of scale in step 3 is: first choose one group of best stochastic resonance system parameter as chi

The reference foundation of degree conversion; The mixing sampled signal treating perception again carries out fast fourier transform, estimates the frequency f of primary user's signal that may exist in mixed signal; Adjustment parameter a, b and sampling step length h; Push away Normalized Scale conversion is equivalent to and becomes original by primary user's signal amplitude times, and the variance of neighbourhood noise becomes original 2b/a ³doubly.

3. a kind of high-frequency signal frequency spectrum sensing method based on stochastic resonance system according to claim 1 and 2, it is characterized in that the stochastic resonance system of the cascade in step 3 is: the stochastic resonance system of multi-stage cascade, can see the series connection of n traditional stochastic resonance system as, the output of upper level stochastic resonance system is as the output of next stage stochastic resonance system.