CN103684635A - Method and system used by secondary user for detecting cognitive radio frequency spectrum - Google Patents

Abstract

The invention provides a method and system used by a secondary user for detecting a cognitive radio frequency spectrum. The method includes the step 101 of calculating the difference values of actual power spectra and an average power spectrum of data received by the secondary user in a certain period of time according to the time-dependent difference of the frequency-hopping signal power spectra so as to counteract the frequency-fixed interference power spectra and reserve the frequency-hopping signal power spectra, and the step 102 of conducting Fourier inversion on the reserved frequency-hopping signal power spectra to obtain an auto-correlation function, and then detecting the frequency band with unexpected frequency-fixed interference through a time domain auto-correlation algorithm. Due to the fact that in the prior art, the condition of the unexpected frequency-fixed interference is not taken into consideration according to the time domain auto-correlation algorithm, in other words, only a single signal of overlapped Gaussian white noise is taken into consideration, the detection accuracy is very low. According to the method used by the secondary user for detecting the cognitive radio frequency spectrum, influences of the unexpected frequency-fixed interference on the detection result are taken into consideration according to the improved time domain auto-correlation detection algorithm, and therefore the detection accuracy is made to be higher.

Description

A kind of secondary user's detects the method and system of cognitive radio frequency spectrum

Technical field

The present invention relates to cognitive radio frequency spectrum perception field, be specifically related to the method and system that a kind of secondary user's detects cognitive radio frequency spectrum.

Background technology

Along with the develop rapidly of wireless communication technology, it is more and more nervous that frequency spectrum resource becomes.Especially along with the development of wireless lan (wlan) technology, wireless personal area network (WPAN) technology, increasing people passes through these technology accessing Internet wirelessly.These network technologies are used unauthorized frequency range (UFB) work mostly.Due to the fast development of WLAN, WPAN radio communication service, the unauthorized frequency range that these networks are worked is gradually saturated.And other communication service (as visual broadcast service etc.) needs communication network that certain protection is provided, and makes them avoid the interference of other communication services.For good protection is provided, frequency management department specific assigned specific mandate frequency range (LFB) for specific communication service, use.Compare with authorizing frequency range, the frequency spectrum resource of unauthorized frequency range will lack a lot (most frequency spectrum resource is all used to do and authorizes frequency range to use).And the utilance of a considerable amount of mandate frequency spectrum resources is very low.So just there is such fact: the frequency spectrum resource of some part traffic carrying capacity relatively less but carrying on it is very large, and the frequency spectrum resource utilization rate that other has been authorized is very low.Therefore, can draw such conclusion: the frequency spectrum resource allocation method based on current, the utilance that has quite a few frequency spectrum resource is very low.

In order to solve the problem of frequency spectrum resource scarcity, basic ideas are exactly to improve the utilance of existing frequency spectrum as far as possible.For this reason, people have proposed the concept of cognitive radio.The basic point of departure of cognitive radio is exactly: in order to improve the availability of frequency spectrum, the Wireless Telecom Equipment (being secondary user's) with cognitive function can be operated in the frequency range of having authorized according to the mode of certain " wait for an opportunity (Opportunistic Way) ".Certainly, this must be based upon and authorize frequency range useless or only have communication service seldom movable in the situation that.This frequency spectrum resource that can be utilized occurring in spatial domain, time domain and frequency domain is called as " frequency spectrum cavity-pocket ".The ability that the core concept of cognitive radio makes Wireless Telecom Equipment have discovery " frequency spectrum cavity-pocket " and rationally utilize exactly.

When unauthorized communication user (being secondary user's) is used the frequency spectrum resource of having authorized by the mode of " using ", must guarantee that its communication can not have influence on other communication of authorized user (being primary user).Accomplish this point, unauthorized user must be used found " frequency spectrum cavity-pocket " according to certain rule.

In prior art, when primary user uses frequency-hopping mode when communication, the inferior user of cognitive radio uses time domain Autocorrelation Detection method to carry out frequency spectrum detection conventionally, if at this moment primary user's Frequency Hopping Signal is gone here and there determining frequency interference and can cause detecting unsuccessfully into burst.And for this technical problem, do not have relevant resolution policy at present.

Summary of the invention

The object of the invention is to, for overcoming the problems referred to above, thereby provide a kind of secondary user's to detect the method and system of cognitive radio frequency spectrum.

For achieving the above object, the invention provides a kind of method that secondary user's detects cognitive radio frequency spectrum, described method comprises:

Step 101), according to the time dependent otherness of Frequency Hopping Signal power spectrum, calculate secondary user's and within certain period, receive the actual power spectrum of data and the difference of average power spectra, thereby offset the fixed spectrum of interference power frequently and reservation Frequency Hopping Signal power spectrum;

Step 102) the Frequency Hopping Signal power spectrum retaining is carried out to inverse Fourier transform and obtain auto-correlation function, then use time domain auto-correlation algorithm to detect and contain frequency range when burst is fixed to be disturbed frequently.

Above-mentioned steps 101) further comprise:

Step 101-1) non-stationary signal that receives within certain period of intercepting secondary user's, and the non-stationary signal of intercepting is that length is the discrete data sequence of N, is specifically expressed as:

$x_N\left(n\right)=\underset{i=1}{\overset{a}{\mathrm{\Σ}}}{S}_{\mathrm{Fi}}\left(n\right)+\underset{j=1}{\overset{b}{\mathrm{\Σ}}}{S}_{\mathrm{Hj}}\left(n\right)+n\left(n\right)$

Wherein, S _fi(n) represent a fixed interference frequently, i=1,2 ..., a; S _hj(n) represent b Frequency Hopping Signal, j=1,2 ..., b; N (n) represents white Gaussian noise; Function x _n(n) span of parameter n is: n=1, and 2 ..., N;

Step 101-2) non-stationary signal that is N by above-mentioned length is divided into L section stationary signal, and the length of each section of stationary signal is M, each section x _mKrepresent;

Step 101-3) adopt following formula to calculate each section of stationary signal x _mKpower spectrum P _{xMK (n ')}(ω):

$P_{\mathrm{xMK}\left(n^{\′}\right)}\left(\mathrm{\ω}\right)=\frac{1}{M}{\left|{\mathrm{\Σ}}_{n^{\′}=0}^{M-1}{x}_{\mathrm{MK}}\left(n^{\′}\right)e^{-\mathrm{j\ω}{n}^{\′}}\right|}^2;$

Wherein, the span of n ' is: n '=0, and 1,2 ..., (M-1); K=1,2,3 ..., L;

The mean value of the power spectrum of all each section of stationary signals that step 101-3) calculate according to following formula:

${\stackrel{\‾}{P}}_{x_{N\left(n\right)}}\left(\mathrm{\ω}\right)=\frac{1}{L}\underset{K=1}{\overset{L}{\mathrm{\Σ}}}{P}_{\mathrm{xMK}\left(n^{\′}\right)}\left(\mathrm{\ω}\right)=\frac{1}{\mathrm{ML}}\underset{K=1}{\overset{L}{\mathrm{\Σ}}}{\left|\underset{n^{\′}=0}{\overset{M-1}{\mathrm{\Σ}}}{x}_{\mathrm{MK}}\left(n^{\′}\right)e^{-\mathrm{j\ω}{n}^{\′}}\right|}^2$

Step 101-4) according to following formula, to step 101-1) whole section of non-stationary signal of secondary user's intercepting carry out Fourier transform, and then obtain the power spectrum of institute's intercept signal;

$P_{\mathrm{xN}\left(n\right)}\left(\mathrm{\ω}\right)=\frac{1}{N}{\left|\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}{x}_N\left(n\right)e^{-\mathrm{j\ωn}}\right|}^2$

Step 101-5) mean value calculation of the power spectrum of the power spectrum based on intercept signal and all each section of stationary signals offsets power spectrum, obtains retaining Frequency Hopping Signal power spectrum, and specific formula for calculation is as follows:

$P_{\mathrm{SUB}}\left(\mathrm{\ω}\right)=P_{\mathrm{xN}\left(n\right)}\left(\mathrm{\ω}\right)-{\stackrel{\‾}{P}x}_{N\left(n\right)}\left(\mathrm{\ω}\right).$

Above-mentioned steps 102) further comprise:

Step 102 ?1) to retaining Frequency Hopping Signal power spectrum P _sUB(ω) carry out inverse Fourier transform and obtain auto-correlation function R _x1(τ), concrete transformation for mula is as follows:

$R_{X1}\left(\mathrm{\τ}\right)={F^{-1}}_{\mathrm{\τ}}\left[P_{\mathrm{SUB}}\left(\mathrm{\ω}\right)\right]={\∫}_{-\∞}^{+\∞}{P}_{\mathrm{SUB}}\left(\mathrm{\ω}\right)e^{\mathrm{j\ω\τ}}\mathrm{d\ω}$

Wherein, auto-correlation function R _x1(τ) be to determine frequency to disturb the auto-correlation function after suppressing;

Step 102-2) based on determining frequency, disturb the auto-correlation function after suppressing, adopt time domain auto-correlation strategy to realize the detection of frequency spectrum cavity-pocket.

Above-mentioned steps 102-2) further comprise:

Step 102-2-1) according to the auto-correlation function obtaining, be calculated as follows two characteristic quantity E ₁and E ₂;

${\mathrm{<figure-callout\; id="1"\; label="E"\; filenames="HDA0000432075360000012.png,HDA0000432075360000021.png"\; state="\{\{state\}\}">E</figure-callout>}}_1=\frac{1}{T_H}{\&Integral;}_0^{T_H}\left|R_{X1}\left(\mathrm{\&tau;}\right)\right|\mathrm{d\&tau;}$

${\mathrm{<figure-callout\; id="2"\; label="E"\; filenames="HDA0000432075360000012.png,HDA0000432075360000021.png"\; state="\{\{state\}\}">E</figure-callout>}}_2=\frac{1}{T-T_H}{\&Integral;}_{T_H}^T\left|R_{X1}\left(\mathrm{\&tau;}\right)\right|\mathrm{d\&tau;}$

Step 102-2-2) calculate the ratio ρ of above-mentioned two characteristic quantities;

Step 102-2-3) threshold value of the ρ obtaining and setting is big or small, when ρ is greater than threshold value, there is Frequency Hopping Signal, otherwise there is no Frequency Hopping Signal.

Such scheme is by estimating ρ ₁expectation and variance yields, and determine suitable threshold value, described ρ in conjunction with probability density distribution situation and the false alarm probability of normally distributed random variable ₁characteristic quantity ratio when only having noise without Frequency Hopping Signal.

In addition, the invention provides the system that a kind of secondary user's detects cognitive radio frequency spectrum, described system comprises:

Frequency Hopping Signal power spectrum extraction module, be used for according to the time dependent otherness of Frequency Hopping Signal power spectrum, calculate secondary user's and within certain period, receive the actual power spectrum of data and the difference of average power spectra, and then balance out and determine frequency interference power spectrum and retain Frequency Hopping Signal power spectrum;

Time domain auto-correlation function obtains and detection module, for the Frequency Hopping Signal power spectrum retaining is got to inverse Fourier transform, obtains auto-correlation function, and then uses the detection of time domain auto-correlation algorithm to contain frequency range when burst is fixed to be disturbed frequently.

Above-mentioned Frequency Hopping Signal power spectrum extraction module further comprises:

Intercepting submodule, the non-stationary signal receiving within certain period for intercepting secondary user's, and intercepting non-stationary signal be that length is the discrete data sequence of N, be specifically expressed as:

$x_N\left(n\right)=\underset{i=1}{\overset{a}{\mathrm{\&Sigma;}}}{S}_{\mathrm{Fi}}\left(n\right)+\underset{j=1}{\overset{b}{\mathrm{\&Sigma;}}}{S}_{\mathrm{Hj}}\left(n\right)+n\left(n\right)$

Wherein, S _fi(n) represent a fixed interference frequently, i=1,2 ..., a; S _hj(n) represent b Frequency Hopping Signal, j=1,2 ..., b; N (n) represents white Gaussian noise; Function x _n(n) span of parameter n is: n=1, and 2 ..., N;

Segmentation submodule, for being N by length, non-stationary signal is divided into L section stationary signal, and the length of each section of stationary signal is M, each section x _mKrepresent;

Each section of stationary signal spectra calculation submodule, for adopting following formula to calculate each section of stationary signal x _mKpower spectrum P _{xMK (m ')}(ω):

$P_{\mathrm{xMK}\left(n^{\&prime;}\right)}\left(\mathrm{\&omega;}\right)=\frac{1}{M}{\left|{\mathrm{\&Sigma;}}_{n^{\&prime;}=0}^{M-1}{x}_{\mathrm{MK}}\left(n^{\&prime;}\right)e^{-\mathrm{j\&omega;}{n}^{\&prime;}}\right|}^2;$

Wherein, the span of n ' is: n '=0, and 1,2 ..., (M-1); K=1,2,3 ..., L;

Average power is obtained submodule, the mean value of the power spectrum of all each section of stationary signals that calculate for the following formula of basis:

${\stackrel{\&OverBar;}{P}}_{x_{N\left(n\right)}}\left(\mathrm{\&omega;}\right)\frac{1}{L}\underset{K=1}{\overset{L}{\mathrm{\&Sigma;}}}{P}_{\mathrm{xMK}\left(n^{\&prime;}\right)}\left(\mathrm{\&omega;}\right)=\frac{1}{\mathrm{ML}}\underset{K=1}{\overset{L}{\mathrm{\&Sigma;}}}{\left|\underset{n^{\&prime;}=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{x}_{\mathrm{MK}}\left(n^{\&prime;}\right)e^{-\mathrm{j\&omega;}{n}^{\&prime;}}\right|}^2$

Intercept signal power spectrum obtains submodule, for according to following formula, to step 101-1) whole section of non-stationary signal of secondary user's intercepting carry out Fourier transform, and then obtain the power spectrum of institute's intercept signal;

$P_{\mathrm{xN}\left(n\right)}\left(\mathrm{\&omega;}\right)=\frac{1}{N}{\left|\underset{n=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{x}_N\left(n\right)e^{-\mathrm{j\&omega;n}}\right|}^2$

Process submodule, for the mean value calculation of the power spectrum of the power spectrum based on intercept signal and all each section of stationary signals, offset power spectrum, obtain retaining Frequency Hopping Signal power spectrum, specific formula for calculation is as follows:

$P_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)=P_{\mathrm{xN}\left(n\right)}\left(\mathrm{\&omega;}\right)-{\stackrel{\&OverBar;}{P}x}_{N\left(n\right)}\left(\mathrm{\&omega;}\right).$

Above-mentioned time domain auto-correlation function obtains and detection module further comprises:

Auto-correlation function obtains submodule, for to retaining Frequency Hopping Signal power spectrum P _sUB(ω) carry out inverse Fourier transform and obtain auto-correlation function R _x1(τ), concrete transformation for mula is as follows:

$R_{X1}\left(\mathrm{\&tau;}\right)={F^{-1}}_{\mathrm{\&tau;}}\left[P_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)\right]={\&Integral;}_{-\&infin;}^{+\&infin;}{P}_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)e^{\mathrm{j\&omega;\&tau;}}\mathrm{d\&omega;}$

Wherein, auto-correlation function R _x1(τ) be to determine frequency to disturb the auto-correlation function after suppressing;

Detection sub-module, for disturbing the auto-correlation function after suppressing based on determining frequency, adopts time domain auto-correlation strategy to realize the detection of frequency spectrum cavity-pocket.

In a word, for the problems referred to above, the present invention is fully analyzing on the basis of time domain correlation method, utilizes power spectrum opposition method to improve time domain Autocorrelation Detection method, curbs and determines frequency interference and then improve frequency spectrum detection performance.

Compared with prior art, technical advantage of the present invention is:

Due to the time domain auto-correlation algorithm of prior art, do not consider to happen suddenly and determine the situation that frequency disturbs, only considered the single signal of stack white Gaussian noise, thereby accuracy of detection is very low.And improved time domain Autocorrelation Detection method has been considered the impact of the fixed interference frequently of burst on testing result in the present invention, thereby make accuracy of detection higher.

Accompanying drawing explanation

Fig. 1 is the flow chart that secondary user's provided by the invention detects the method for cognitive radio frequency spectrum;

Fig. 2 is auto-correlation coefficient and the time delay when receiving signal and only containing frequency hopping and noise;

Fig. 3 receives signal to contain frequency hopping, fixed auto-correlation coefficient and time delay frequently, during noise;

Fig. 4 is the actual power spectrum of data intercept section;

Fig. 5 is that use power spectrum opposition method suppresses the fixed power spectrum afterwards that frequently disturbs;

Fig. 6 carries out inverse Fourier transform and asks auto-correlation function suppressing to determine power spectrum after frequency disturbs;

Performance comparison diagram before and after Fig. 7 surely frequently disturbs and suppresses.

Embodiment

Below in conjunction with drawings and Examples, the method for the invention is elaborated.

The present invention adopts power spectrum opposition method to improve time domain auto-correlation algorithm, suppresses the fixed interference frequently of burst.Power spectrum opposition method, according to the time dependent otherness of Frequency Hopping Signal power spectrum, by carrying out subtracting each other of actual power spectrum and average power spectra to receiving data, balances out and determines frequency interference power spectrum, retains Frequency Hopping Signal power spectrum.According to dimension, receive khintchine's theorem, the power spectrum retaining is got to inverse Fourier transform and obtain auto-correlation function, and then use time domain auto-correlation algorithm to go to detect to contain frequency range when burst is fixed to be disturbed frequently.Concrete principle is explained as follows:

The present invention based on the improvement principle of time domain Autocorrelation Detection method be:

Suppose to receive signal and contain Frequency Hopping Signal, noise and determine frequency while disturbing, can be expressed as

x(t)=S _H(t)+S _F(t)+n(t)(1)

Wherein, x (t) represents that time user receives signal.S _h(t) represent that hop period is T _hfrequency Hopping Signal, sampling time T is much larger than hop period (but be less than frequency hop sequences cycle), thereby guarantees the information that signal that sampling obtains comprises a plurality of frequency hopping frequencies.S _f(t) represent the burst interference frequently surely that whole detection frequency range all exists.N (t) represents that average is 0, and single-side belt power spectral density is

the logical white Gaussian noise of band.The auto-correlation computation that receives signal as shown in the formula:

$\begin{array}{c}{R}_X\left(\mathrm{\&tau;}\right)=E\left[x\left(t\right)x(t+\mathrm{\&tau;})\right]\\ =R_{{\mathrm{SS}}_{\mathrm{HH}}}\left(\mathrm{\&tau;}\right)+R_{{\mathrm{SS}}_{\mathrm{FF}}}\left(\mathrm{\&tau;}\right)+R_{\mathrm{NN}}\left(\mathrm{\&tau;}\right)+R_{S_H{S}_F}\left(\mathrm{\&tau;}\right)+R_{S_F{S}_H}\left(\mathrm{\&tau;}\right)+R_{S_{H}N}\left(\mathrm{\&tau;}\right)\\ +R_{N{S}_H}\left(\mathrm{\&tau;}\right)+R_{S_{F}N}\left(\mathrm{\&tau;}\right)+R_{N{S}_F}\left(\mathrm{\&tau;}\right)\end{array}---\left(2\right)$

Wherein

the Frequency Hopping Signal auto-correlation that represents primary user's transmission,

represent the fixed auto-correlation of frequently disturbing,

represent Frequency Hopping Signal, fixed cross-correlation between interference frequently.Because the cross-correlation between noise and frequency hopping and fixed frequency can neglect to fall to disregarding.Formula (2) is expressed as:

$R_X\left(\mathrm{\&tau;}\right)=R_{S{S}_{\mathrm{HH}}}\left(\mathrm{\&tau;}\right)+R_{S{S}_{\mathrm{FF}}}\left(\mathrm{\&tau;}\right)+R_{\mathrm{NN}}\left(\mathrm{\&tau;}\right)+R_{S_H{S}_F}\left(\mathrm{\&tau;}\right)+R_{S_F{S}_H}\left(\mathrm{\&tau;}\right)---\left(3\right)$

Because white Gaussian noise is incoherent in time domain, so receive the auto-correlation and the fixed auto-correlation of frequently disturbing that the auto-correlation of signal depends on frequency hopping letter.Frequency Hopping Signal auto-correlation

at τ < T _htime, Frequency Hopping Signal is correlated with in a jump space, and it is worth non-zero.Because the frequency of Frequency Hopping Signal is not identical within adjacent several hop periods, therefore at τ > T _htime, when frequency hopping number of times is more,

and therefore fixed frequently interference have very strong autocorrelation in whole detection frequency range, the fixed autocorrelation value of frequently disturbing

when primary user is only contained Frequency Hopping Signal and white Gaussian noise, according to the auto-correlation that receives signal, whether only within the scope of a hop cycle time delay, there is a larger peak value, and comparatively mild and close to 0 this character within the scope of other time delays, can realize the detection to Frequency Hopping Signal existence.Now the relation of normalized autocorrelation coefficient and time delay τ as shown in Figure 2, and when primary user is contained Frequency Hopping Signal, fixed interference frequently and white Gaussian noise, owing to determining frequency interference, in whole detection frequency range, have very strong autocorrelation, the relation of its normalized autocorrelation coefficient and time delay τ as shown in Figure 3.

When receiving signal without Frequency Hopping Signal, contain noise and fixed interference frequently, formula (3) can be expressed as

$R_X\left(\mathrm{\&tau;}\right)=R_{S{S}_{\mathrm{FF}}}\left(\mathrm{\&tau;}\right)+\mathrm{PR}\left(\mathrm{\&tau;}\right)|{\mathrm{\&sigma;}}^2=\mathrm{1,0}<\mathrm{\&tau;}<T---\left(4\right)$

When reception signal comprises Frequency Hopping Signal, contain Frequency Hopping Signal, noise and fixed interference frequently, formula (3) can be expressed as

$P_X\left(\mathrm{\&tau;}\right)=\left\{\begin{array}{cc}{R}_{{\mathrm{SS}}_{\mathrm{HH}}}\left(\mathrm{\&tau;}\right)+R_{{\mathrm{SS}}_{\mathrm{FF}}}\left(\mathrm{\&tau;}\right)+R_{S_H{S}_F}\left(\mathrm{\&tau;}\right)+R_{S_F{S}_H}\left(\mathrm{\&tau;}\right)+\mathrm{PR}\left(\mathrm{\&tau;}\right){|}_{{\mathrm{\&sigma;}}^2=1},& 0<\mathrm{\&tau;}<T_H\\ R_{{\mathrm{SS}}_{\mathrm{FF}}}\left(\mathrm{\&tau;}\right)+R_{S_H{S}_F}\left(\mathrm{\&tau;}\right)+R_{S_F{S}_H}\left(\mathrm{\&tau;}\right)+\mathrm{PR}\left(\mathrm{\&tau;}\right){|}_{{\mathrm{\&sigma;}}^2=1,}& T_H<\mathrm{\&tau;}<T\end{array}\right.---\left(5\right)$

Wherein P represents noise power, R (τ) | σ ²=1 represents the auto-correlation of the white Gaussian noise that power is unit value.By formula (4), (5) are known, even if detect in frequency range, contain Frequency Hopping Signal, and because the frequency of determining on each frequency disturbs and self also has good autocorrelation, so which type of value no matter time delay τ get, and the autocorrelation value of reception signal can not be tending towards 0 yet.

In addition, according to the auto-correlation function R of detection signal _x(τ) ratio of calculated characteristics amount and characteristic quantity is as follows:

${\mathrm{<figure-callout\; id="1"\; label="E"\; filenames="HDA0000432075360000012.png,HDA0000432075360000021.png"\; state="\{\{state\}\}">E</figure-callout>}}_1=\frac{1}{T_H}{\&Integral;}_0^{T_H}\left|R_X\left(\mathrm{\&tau;}\right)\right|\mathrm{d\&tau;};{\mathrm{<figure-callout\; id="2"\; label="E"\; filenames="HDA0000432075360000012.png,HDA0000432075360000021.png"\; state="\{\{state\}\}">E</figure-callout>}}_2=\frac{1}{T-T_H}{\&Integral;}_{T_H}^T\left|R_X\left(\mathrm{\&tau;}\right)\right|\mathrm{d\&tau;}---\left(6\right)$

The ratio ρ of characteristic quantity during without Frequency Hopping Signal ₁be expressed as

${\mathrm{\&rho;}}_1=\frac{E_1}{{\mathrm{<figure-callout\; id="2"\; label="E"\; filenames="HDA0000432075360000012.png,HDA0000432075360000021.png"\; state="\{\{state\}\}">E</figure-callout>}}_2}=\frac{(T-T_H){\&Integral;}_0^{T_H}|R_{{\mathrm{SS}}_{\mathrm{FF}}}\mathrm{\&tau;}+\mathrm{PR}\left(\mathrm{\&tau;}\right){|}_{{\mathrm{\&sigma;}}^2=1}|\mathrm{d\&tau;}}{T_H{\&Integral;}_{T_H}^T|R_{S{S}_{\mathrm{FF}}}\left(\mathrm{\&tau;}\right)+\mathrm{PR}\left(\mathrm{\&tau;}\right){|}_{{\mathrm{\&sigma;}}^2=1}|\mathrm{d\&tau;}}---\left(7\right)$

The ratio ρ of characteristic quantity while having Frequency Hopping Signal ₂be expressed as

${\mathrm{\&rho;}}_2=\frac{E_1}{{\mathrm{<figure-callout\; id="2"\; label="E"\; filenames="HDA0000432075360000012.png,HDA0000432075360000021.png"\; state="\{\{state\}\}">E</figure-callout>}}_2}=\frac{(T-T_H){\&Integral;}_0^{T_H}|R_{{\mathrm{SS}}_{\mathrm{HH}}}\left(\mathrm{\&tau;}\right)+R_{{\mathrm{SS}}_{\mathrm{FF}}}\left(\mathrm{\&tau;}\right)+R_{S_H{S}_F}\left(\mathrm{\&tau;}\right)+R_{S_F{S}_H}\left(\mathrm{\&tau;}\right)+\mathrm{PR}\left(\mathrm{\&tau;}\right){|}_{{\mathrm{\&sigma;}}^2=1}|\mathrm{d\&tau;}}{T_H{\&Integral;}_{T_H}^T|R_{S{S}_{\mathrm{FF}}}\left(\mathrm{\&tau;}\right)+R_{S_H{S}_F}\left(\mathrm{\&tau;}\right)+R_{S_F{S}_F}\left(\mathrm{\&tau;}\right)+\mathrm{PR}\left(\mathrm{\&tau;}\right){|}_{{\mathrm{\&sigma;}}^2=1}|\mathrm{d\&tau;}}---\left(8\right)$

In formula (7), owing to determining the existence of frequency interference, can cause false alarm probability to increase, can make like this time user lose the chance of more use frequency spectrum cavity-pockets, the availability of frequency spectrum is reduced.Formula (8), disturbs existence owing to determining frequency, and its average there will not be an obvious increment.

To sum up, improvement time domain Autocorrelation Detection method provided by the invention, first carries out actual power spectrum and average power spectrum subtraction to cognition wireless electrical receive signal.Balance out and determine frequency interference, retain Frequency Hopping Signal.Secondly by the information of these reservations, carry out time domain Autocorrelation Detection, judgement has or not frequency spectrum cavity-pocket.

Embodiment

Suppose to detect frequency range and mainly by Frequency Hopping Signal, noise and fixed interference frequently, formed, because Frequency Hopping Signal belongs to non-stationary signal.

First, one section of non-stationary signal is divided into several sections of stationary signals.

Suppose that non-stationary signal is that length is the discrete series of N, is expressed as:

$x_N\left(n\right)=\underset{i=1}{\overset{a}{\mathrm{\&Sigma;}}}{S}_{\mathrm{Fi}}\left(n\right)+\underset{j=1}{\overset{b}{\mathrm{\&Sigma;}}}{S}_{\mathrm{Hj}}\left(n\right)+n\left(n\right)---\left(9\right)$

Wherein, S _fi(n) represent a fixed interference frequently, i=1,2 ..., a; S _hj(n) represent b Frequency Hopping Signal, j=1,2 ..., b; N (n) represents white Gaussian noise.Function x _n(n) span of parameter n is: n=1, and 2 ..., N;

The non-stationary signal x that is N by above-mentioned length _n(n) be divided into L section stationary signal, and the length of each section of stationary signal is M, each section x _mx represents; Respectively to each section of stationary signal x _mKget FFT (Fast Fourier Transform) and ask power spectrum power spectrum P _{xMK (n ')}(ω):

$P_{\mathrm{xMK}\left(n^{\&prime;}\right)}\left(\mathrm{\&omega;}\right)=\frac{1}{M}{\left|{\mathrm{\&Sigma;}}_{n^{\&prime;}=0}^{M-1}{x}_{\mathrm{MK}}\left(n^{\&prime;}\right)e^{-\mathrm{j\&omega;}{n}^{\&prime;}}\right|}^2---\left(10\right);$

Wherein, the span of n ' is: n '=0,1,2 ..., (M-1); K=1,2,3 ..., L;

Then, the corresponding addition of the power spectrum of every segment data is averaged, can obtains the average power spectra of this data segment, specific formula for calculation is as follows:

${\stackrel{\&OverBar;}{P}}_{x_{N\left(n\right)}}\left(\mathrm{\&omega;}\right)=\frac{1}{L}\underset{K=1}{\overset{L}{\mathrm{\&Sigma;}}}{P}_{\mathrm{xMK}\left(n^{\&prime;}\right)}\left(\mathrm{\&omega;}\right)=\frac{1}{\mathrm{ML}}\underset{K=1}{\overset{L}{\mathrm{\&Sigma;}}}{\left|\underset{n^{\&prime;}=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{x}_{\mathrm{MK}}\left(n^{\&prime;}\right)e^{-\mathrm{j\&omega;}{n}^{\&prime;}}\right|}^2---\left(11\right)$

Then, whole segment data is made to FFT, calculate the power spectrum P of institute's intercept signal _{xN (n)}(ω), the following also reference diagram (4) of computing formula:

$P_{\mathrm{xN}\left(n\right)}\left(\mathrm{\&omega;}\right)=\frac{1}{N}{\left|\underset{n=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{x}_N\left(n\right)e^{-\mathrm{j\&omega;n}}\right|}^2---\left(12\right)$

Again then, adopt following formula to calculate and offset power spectrum P _sUB(ω), simultaneously as figure (5) as shown in, 1,2 two frequency that the corresponding occurrence frequency of difference collides wherein.Compare with corresponding frequency in Fig. 4, after power spectrum offsets, the frequency hopping power spectrum of two frequencies reduces to some extent, but little on the impact of detection performance, and does not have the frequency of determining of frequency collision to disturb suppressed.

$P_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)=P_{\mathrm{xN}\left(n\right)}\left(\mathrm{\&omega;}\right)-{\stackrel{\&OverBar;}{P}x}_{N\left(n\right)}\left(\mathrm{\&omega;}\right)---\left(13\right)$

In a word, by computing above, can balance out most fixed frequent spectrum information, retain most frequency hopping spectrum informations.

Finally, according to Wei Na-khintchine's theorem, auto-correlation function and power spectrum are a pair of Fourier transform.To P _sUB(ω) carry out inverse Fourier transform and obtain auto-correlation function R _x1(τ), computing formula following also as shown in Figure 6 simultaneously.

$P_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)=F_{\mathrm{\&tau;}}\left[R_{X1}\left(\mathrm{\&tau;}\right)\right]={\&Integral;}_{-\&infin;}^{+\&infin;}{R}_{X1}\left(\mathrm{\&tau;}\right)e^{-\mathrm{j\&omega;\&tau;}}\mathrm{d\&omega;}---\left(14\right)$

$R_{X1}\left(\mathrm{\&tau;}\right)={F^{-1}}_{\mathrm{\&tau;}}\left[P_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)\right]={\&Integral;}_{-\&infin;}^{+\&infin;}{P}_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)e^{\mathrm{j\&omega;\&tau;}}\mathrm{d\&omega;}---\left(15\right)$

Wherein, R in above formula _x1(τ) represent the fixed auto-correlation after suppressing of frequently disturbing, through type (15) obtains determining frequency and disturbs the auto-correlation after suppressing, and recycles the detection that conventional time domain autocorrelation method is realized frequency spectrum cavity-pocket.

At the same P of false alarm probability _fin=0.05 situation, existing time domain Autocorrelation Detection method and improved time domain Autocorrelation Detection method are carried out to Performance Ratio, as shown in Figure 7.

It should be noted last that, above embodiment is only unrestricted in order to technical scheme of the present invention to be described.Although the present invention is had been described in detail with reference to embodiment, those of ordinary skill in the art is to be understood that, technical scheme of the present invention is modified or is equal to replacement, do not depart from the spirit and scope of technical solution of the present invention, it all should be encompassed in the middle of claim scope of the present invention.

Claims (8)

1. secondary user's detects a method for cognitive radio frequency spectrum, and described method comprises:

Step 101), according to the time dependent otherness of Frequency Hopping Signal power spectrum, calculate secondary user's and within certain period, receive the actual power spectrum of data and the difference of average power spectra, thereby offset the fixed spectrum of interference power frequently and reservation Frequency Hopping Signal power spectrum;

Step 102) the Frequency Hopping Signal power spectrum retaining is carried out to inverse Fourier transform and obtain auto-correlation function, then use time domain auto-correlation algorithm to detect and contain frequency range when burst is fixed to be disturbed frequently.

2. secondary user's according to claim 1 detects the method for cognitive radio frequency spectrum, it is characterized in that described step 101) further comprise:

Step 101-1) non-stationary signal that receives within certain period of intercepting secondary user's, and the non-stationary signal of intercepting is that length is the discrete data sequence of N, is specifically expressed as:

$x_N\left(n\right)=\underset{i=1}{\overset{a}{\mathrm{\&Sigma;}}}{S}_{\mathrm{Fi}}\left(n\right)+\underset{j=1}{\overset{b}{\mathrm{\&Sigma;}}}{S}_{\mathrm{Hj}}\left(n\right)+n\left(n\right)$

Wherein, S _fi(n) represent a fixed interference frequently, i=1,2 ..., a; S _hj(n) represent b Frequency Hopping Signal, j=1,2 ..., b; N (n) represents white Gaussian noise; Function x _n(n) span of parameter n is: n=1, and 2 ..., N;

Step 101-2) non-stationary signal that is N by above-mentioned length is divided into L section stationary signal, and the length of each section of stationary signal is M, each section x _mKrepresent;

Step 101-3) adopt following formula to calculate each section of stationary signal x _mKpower spectrum P _{xMK (n ')}(ω):

$P_{\mathrm{xMK}\left(n^{\&prime;}\right)}\left(\mathrm{\&omega;}\right)=\frac{1}{M}{\left|{\mathrm{\&Sigma;}}_{n^{\&prime;}=0}^{M-1}{x}_{\mathrm{MK}}\left(n^{\&prime;}\right)e^{-\mathrm{j\&omega;}{n}^{\&prime;}}\right|}^2;$

Wherein, the span of n ' is: n '=0, and 1,2 ..., (M-1); K=1,2,3 ..., L;

The mean value of the power spectrum of all each section of stationary signals that step 101-3) calculate according to following formula:

${\stackrel{\&OverBar;}{P}}_{x_{N\left(n\right)}}\left(\mathrm{\&omega;}\right)=\frac{1}{L}\underset{K=1}{\overset{L}{\mathrm{\&Sigma;}}}{P}_{\mathrm{xMK}\left(n^{\&prime;}\right)}\left(\mathrm{\&omega;}\right)=\frac{1}{\mathrm{ML}}\underset{K=1}{\overset{L}{\mathrm{\&Sigma;}}}{\left|\underset{n^{\&prime;}=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{x}_{\mathrm{MK}}\left(n^{\&prime;}\right)e^{-\mathrm{j\&omega;}{n}^{\&prime;}}\right|}^2$

Step 101-4) according to following formula, to step 101-1) whole section of non-stationary signal of secondary user's intercepting carry out Fourier transform, and then obtain the power spectrum of institute's intercept signal;

$P_{\mathrm{xN}\left(n\right)}\left(\mathrm{\&omega;}\right)=\frac{1}{N}{\left|\underset{n=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{x}_N\left(n\right)e^{-\mathrm{j\&omega;n}}\right|}^2$

Step 101-5) mean value calculation of the power spectrum of the power spectrum based on intercept signal and all each section of stationary signals offsets power spectrum, obtains retaining Frequency Hopping Signal power spectrum, and specific formula for calculation is as follows:

$P_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)=P_{\mathrm{xN}\left(n\right)}\left(\mathrm{\&omega;}\right)-{\stackrel{\&OverBar;}{P}x}_{N\left(n\right)}\left(\mathrm{\&omega;}\right).$

3. secondary user's according to claim 1 detects the method for cognitive radio frequency spectrum, it is characterized in that described step 102) further comprise:

Step 102 ?1) to retaining Frequency Hopping Signal power spectrum P _sUB(ω) carry out inverse Fourier transform and obtain auto-correlation function P _x1(τ), concrete transformation for mula is as follows:

$R_{X1}\left(\mathrm{\&tau;}\right)={F^{-1}}_{\mathrm{\&tau;}}\left[P_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)\right]={\&Integral;}_{-\&infin;}^{+\&infin;}{P}_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)e^{\mathrm{j\&omega;\&tau;}}\mathrm{d\&omega;}$

Wherein, auto-correlation function R _x1(τ) be to determine frequency to disturb the auto-correlation function after suppressing;

Step 102-2) based on determining frequency, disturb the auto-correlation function after suppressing, adopt time domain auto-correlation strategy to realize the detection of frequency spectrum cavity-pocket.

4. secondary user's according to claim 3 detects the method for cognitive radio frequency spectrum, it is characterized in that described step 102-2) further comprise:

Step 102-2-1) according to the auto-correlation function obtaining, be calculated as follows two characteristic quantity E ₁and E ₂;

$E_1=\frac{1}{T_H}{\&Integral;}_0^{T_H}\left|R_{X1}\left(\mathrm{\&tau;}\right)\right|\mathrm{d\&tau;}$

$E_2=\frac{1}{T-T_H}{\&Integral;}_{T_H}^T\left|R_{X1}\left(\mathrm{\&tau;}\right)\right|\mathrm{d\&tau;}$

Step 102-2-2) calculate the ratio ρ of above-mentioned two characteristic quantities;

Step 102-2-3) threshold value of the ρ obtaining and setting is big or small, when ρ is greater than threshold value, there is Frequency Hopping Signal, otherwise there is no Frequency Hopping Signal.

5. secondary user's according to claim 4 detects the method for cognitive radio frequency spectrum, it is characterized in that, by estimating ρ ₁expectation and variance yields, and determine suitable threshold value, described ρ in conjunction with probability density distribution situation and the false alarm probability of normally distributed random variable ₁characteristic quantity ratio when only having noise without Frequency Hopping Signal.

6. secondary user's detects a system for cognitive radio frequency spectrum, it is characterized in that, described system comprises:

Frequency Hopping Signal power spectrum extraction module, be used for according to the time dependent otherness of Frequency Hopping Signal power spectrum, calculate secondary user's and within certain period, receive the actual power spectrum of data and the difference of average power spectra, and then balance out and determine frequency interference power spectrum and retain Frequency Hopping Signal power spectrum;

Time domain auto-correlation function obtains and detection module, for the Frequency Hopping Signal power spectrum retaining is got to inverse Fourier transform, obtains auto-correlation function, and then uses the detection of time domain auto-correlation algorithm to contain frequency range when burst is fixed to be disturbed frequently.

7. secondary user's according to claim 6 detects the system of cognitive radio frequency spectrum, it is characterized in that, described Frequency Hopping Signal power spectrum extraction module further comprises:

Intercepting submodule, the non-stationary signal receiving within certain period for intercepting secondary user's, and intercepting non-stationary signal be that length is the discrete data sequence of N, be specifically expressed as:

$x_N\left(n\right)=\underset{i=1}{\overset{a}{\mathrm{\&Sigma;}}}{S}_{\mathrm{Fi}}\left(n\right)+\underset{j=1}{\overset{b}{\mathrm{\&Sigma;}}}{S}_{\mathrm{Hj}}\left(n\right)+n\left(n\right)$

Wherein, S _fi(n) represent a fixed interference frequently, i=1,2 ..., a; S _hj(n) represent b Frequency Hopping Signal, j=1,2 ..., b; N (n) represents white Gaussian noise; The span of the parameter n of function xN (n) is: n=1, and 2 ..., N;

Segmentation submodule, for being N by length, non-stationary signal is divided into L section stationary signal, and the length of each section of stationary signal is M, each section x _mKrepresent;

Each section of stationary signal spectra calculation submodule, for adopting following formula to calculate each section of stationary signal x _mKpower spectrum P _{xMK (n ')}(ω):

$P_{\mathrm{xMK}\left(n^{\&prime;}\right)}\left(\mathrm{\&omega;}\right)=\frac{1}{M}{\left|{\mathrm{\&Sigma;}}_{n^{\&prime;}=0}^{M-1}{x}_{\mathrm{MK}}\left(n^{\&prime;}\right)e^{-\mathrm{j\&omega;}{n}^{\&prime;}}\right|}^2;$

Wherein, the span of n ' is: n '=0, and 1,2 ..., (M-1); K=1,2,3 ..., L;

Average power is obtained submodule, the mean value of the power spectrum of all each section of stationary signals that calculate for the following formula of basis:

${\stackrel{\&OverBar;}{P}}_{x_{N\left(n\right)}}\left(\mathrm{\&omega;}\right)\frac{1}{L}\underset{K=1}{\overset{L}{\mathrm{\&Sigma;}}}{P}_{\mathrm{xMK}\left(n^{\&prime;}\right)}\left(\mathrm{\&omega;}\right)=\frac{1}{\mathrm{ML}}\underset{K=1}{\overset{L}{\mathrm{\&Sigma;}}}{\left|\underset{n^{\&prime;}=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{x}_{\mathrm{MK}}\left(n^{\&prime;}\right)e^{-\mathrm{j\&omega;}{n}^{\&prime;}}\right|}^2$

Intercept signal power spectrum obtains submodule, for according to following formula, to step 101-1) whole section of non-stationary signal of secondary user's intercepting carry out Fourier transform, and then obtain the power spectrum of institute's intercept signal;

$P_{\mathrm{xN}\left(n\right)}\left(\mathrm{\&omega;}\right)=\frac{1}{N}{\left|\underset{n=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{x}_N\left(n\right)e^{-\mathrm{j\&omega;n}}\right|}^2$

Process submodule, for the mean value calculation of the power spectrum of the power spectrum based on intercept signal and all each section of stationary signals, offset power spectrum, obtain retaining Frequency Hopping Signal power spectrum, specific formula for calculation is as follows:

$P_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)=P_{\mathrm{xN}\left(n\right)}\left(\mathrm{\&omega;}\right)-{\stackrel{\&OverBar;}{P}x}_{N\left(n\right)}\left(\mathrm{\&omega;}\right).$

8. secondary user's according to claim 6 detects the system of cognitive radio frequency spectrum, it is characterized in that, described time domain auto-correlation function obtains and detection module further comprises:

Auto-correlation function obtains submodule, for to retaining Frequency Hopping Signal power spectrum P _sUB(ω) carry out Fourier's inversion

Get auto-correlation function R in return _x1(τ), concrete transformation for mula is as follows:

$R_{X1}\left(\mathrm{\&tau;}\right)={F^{-1}}_{\mathrm{\&tau;}}\left[P_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)\right]={\&Integral;}_{-\&infin;}^{+\&infin;}{P}_{\mathrm{SUB}}\left(\mathrm{\&omega;}\right)e^{\mathrm{j\&omega;\&tau;}}\mathrm{d\&omega;}$

Wherein, auto-correlation function R _x1(τ) be to determine frequency to disturb the auto-correlation function after suppressing;

Detection sub-module, for disturbing the auto-correlation function after suppressing based on determining frequency, adopts time domain auto-correlation strategy to realize the detection of frequency spectrum cavity-pocket.

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