CN101848044A - Low power consumption time domain and frequency domain double threshold combined energy detection algorithm - Google Patents

Abstract

The invention provides a lower power consumption time domain and frequency domain double threshold combined energy detection algorithm, comprising the following main steps of: A. carrying out time domain detection on a signal to be detected; B. judging that a main user does not exist if the main user is not detected; and otherwise, carrying out time domain detection; and C. judging that the main user does not exist if the main user is not detected; and otherwise, judging that the main user exists. Since a time domain energy detection method has lower power consumption but low detection accuracy; and a frequency domain energy detection method has higher detection accuracy but higher power consumption. By combining the time domain energy detection with the frequency domain energy detection according to a specific program, the invention exerts respective advantages, has lower power consumption than any (time domain or frequency domain) energy detection method when used independently and the method is simple and effective.

Description

Low power consumption time domain and frequency domain double threshold combined energy detection algorithm

Technical field

The invention belongs to communication technical field, relate to the Ubiquitous Network communication, particularly cognitive radio networks that have from the perception ability.Specifically, because the frequency spectrum detection technology is a kind of key technology of cognitive radio, cognitive user also will detect, monitor the operating position of frequency spectrum in real time except the communication service that will carry self.Therefore, the power consumption of cognitive user will be bigger problem, also is the bottleneck of restriction cognitive radio technology practical application.So under the condition that does not influence service behaviour, reduce the power loss of frequency spectrum detection and monitoring, research low-power consumption frequency spectrum share strategy just becomes and has the Research Significance of reality.The present invention is in conjunction with these two kinds of energy detection algorithms of time-domain and frequency-domain, and this algorithm can be under the condition that guarantees certain accuracy of detection, and is all lower than the energy detection method power consumption of any time domain of independent use or frequency domain.

Background technology

At present, the frequency spectrum detecting method of cognitive radio networks mainly still is an energy measuring, and energy detection method is divided into time domain again and detects and two kinds of methods of frequency domain detection.

Two kinds of detection methods all have pluses and minuses separately.Detect for time domain, " Energy Detectionof Unknown Deterministic Signals " done detailed introduction at document.The time domain energy detector is made up of band pass filter, analog to digital converter, square algorithm module, COMPREHENSIVE CALCULATING module and threshold judgement module.Outer signals r (t) at first is the band pass filter of W by bandwidth, picked up signal r _W(t), by analog to digital converter analog signal is become digital signal y (n) then, y (n) enters the COMPREHENSIVE CALCULATING module by the square algorithm module N dimension sampled point is averaged, and obtains the sample value of target frequency bands W

Will

Compare with predefined decision threshold, judge whether to exist main subscriber signal.For frequency domain detection, mainly form by analog to digital converter, fast Fourier transform module, square algorithm module, COMPREHENSIVE CALCULATING module and threshold judgement module.Outer signals r (t) at first becomes digital signal y (n) by analog to digital converter with analog signal, y (n) is converted to frequency-region signal by the FFT conversion with time-domain signal, then frequency-region signal is imported square algorithm module picked up signal energy, by the COMPREHENSIVE CALCULATING module frequency-region signal is averaged, obtain the sample value V of target frequency bands, V and predefined decision threshold are compared, judge whether to exist main subscriber signal.

Above two kinds of detection methods, the time area detecting method have and realize that module is simple, low in energy consumption, the advantage that the processing time is short, but the measuring accuracy of this method is on the low side; The frequency domain detection method has improved measuring accuracy owing to having introduced the FFT processing module, but this module has consumed the power consumption of whole energy detection module 70%-80%, thereby has increased system power consumption.In view of the pluses and minuses separately of above two kinds of methods, people often can't accept or reject in the two again, and the present invention is in conjunction with these two kinds of detection methods, are making full use of a kind of method that addresses this problem that proposes on the basis of advantage separately.

Summary of the invention

The purpose of this invention is to provide a kind of energy measuring efficiently of cognitive radio networks that is applicable to improves one's methods.A large amount of measurement Research according to FCC (FCC) and American National radio net research experiment bed (NRNRT) prove that the probability of main user's existence is generally 10 ^-2Magnitude is so can think that it is small probability event that main user exists this true.When only using the time domain energy detection algorithm to detect main user for this reason, if judged result is for existing main user, mean that then small probability event has taken place, then for the generation of further confirming this small probability event is not because the low accuracy of time domain measurement algorithm causes, so we carry out the generation that the frequency domain energy measuring is further determined small probability event again.

In conjunction with above-mentioned thought, concrete steps of the present invention are as follows:

First step: carry out time domain to measured signal and detect.

Time domain detection module real time execution carries out energy measuring to the signal in the cognitive environment on time domain.It is low that this module has complexity with respect to the frequency domain detection module, the advantage that power consumption is little, but accuracy of detection is lower.

Second step:, judge that main user does not exist if do not detect main user; Otherwise, carry out frequency domain detection.

We know explanation by the front, and the probability that main user exists is very low, so we judge that the non-existent possibility of main user is bigger.In other words, the possibility that we proceed frequency domain detection is lower, so the frequency domain detection module is not a real-time working.Like this, just under the situation that guarantees certain precision, reduced the power consumption that detects.Here, more directly perceived in order to make analysis result, we have introduced the participation factor alpha of frequency domain detection module, and have provided its definite method, make the detection probability of system and rate of false alarm comprise α.

Third step:, judge that main user does not exist if frequency domain does not detect main user; Otherwise, judge that main user exists

Because it is small probability event that main user exists, therefore when time domain detects main user and exists,,, thereby the result of determination of this step has been arranged just we have carried out frequency domain detection in order to guarantee the precision that detects.

Description of drawings

Fig. 1 is time domain energy detection model figure.

Fig. 2 is a frequency domain energy measuring illustraton of model.

Fig. 3 is a dual threshold low-power consumption energy detection algorithm flow chart

Fig. 4 is that time domain, frequency domain and dual threshold detect the power consumption curve chart

Fig. 5 is for participating in coefficient curve α and time domain threshold value Γ _TDGraph of relation

Embodiment

The invention will be further described below in conjunction with the implementation process of algorithm, but this implementation process should not be construed as limitation of the present invention.

The dual threshold energy detection system is made of jointly time domain and frequency domain two parts.Whole concept is low-power consumption and the simple advantage of realization of utilizing time domain to detect, carry out Preliminary screening to main with signal earlier, think that main subscriber signal exists if time domain detects, utilize the frequency domain detection module to confirm again, so promptly saved system power dissipation and guaranteed certain detection probability again.

In this invention, we have at first obtained whole detection probability and the detection probability of false alarm probability and time domain module and frequency domain module and the relation of false alarm probability of detection system.Introduced the participation coefficient of frequency domain module at us after, expressed the functional relation that participates between coefficient and dual threshold energy detection system whole detection probability and the whole misinformation probability again.

The concrete implementation of algorithm is as follows:

1, carrying out time domain to measured signal detects.

The time domain energy detection model as shown in Figure 1, the time domain energy detector is made up of band pass filter (BPF), analog to digital converter (A/D), square algorithm module (Square-Law), COMPREHENSIVE CALCULATING module and threshold judgement module (Threshold).Outer signals r (t) at first is the band pass filter of W by bandwidth, picked up signal r _W(t), by analog to digital converter analog signal is become digital signal y (n) then, y (n) enters the COMPREHENSIVE CALCULATING module by the square algorithm module N dimension sampled point is averaged, and obtains the sample value V of target frequency bands W, with V and predefined decision threshold Γ _TDRelatively, judge whether to exist main subscriber signal.

Time domain energy detects based on a simple fact, and the energy when promptly the energy of signal plus noise is necessarily greater than the noise individualism is shown below:

E{(s(t)+n(t)) ²}＝E{s ²(t)}+E{n ²(t)}＞E{n ²(t)}

Suppose that noise n (t) is the white Gaussian noise of zero-mean, its double-side band power spectral density is N ₀, noise bandwidth is W; Signal s (t) is uncorrelated with noise, i.e. E{s (t) n (t) }=0.

Time domain is got statistic:

${\hat{V}}_{\mathrm{TD}}=\frac{1}{{\mathrm{<figure-callout\; id="0"\; label="N"\; filenames="HSA00000122387100022.png"\; state="\{\{state\}\}">N</figure-callout>}}_0}{\&Integral;}_0^{\mathrm{\&tau;}}{y}^2\left(t\right)\mathrm{dt}$

Be true statistic V behind the integration _TDA linear function.Be continuously T, bandwidth is that the signal of W can be represented by 2TW sampled point.

When only considering noise signal n (t),

Be the quadratic sum of the Gaussian random variable of 2TW zero-mean, unit variance, promptly

It is the stochastic variable of card side, the obedience center distribution of 2TW the degree of freedom; When only considering main subscriber signal s (t),

For the degree of freedom is the non-central card side distribution of 2TW the degree of freedom, non-central coefficient lambda is

$\mathrm{\&lambda;}=\frac{1}{{\mathrm{<figure-callout\; id="0"\; label="N"\; filenames="HSA00000122387100022.png"\; state="\{\{state\}\}">N</figure-callout>}}_0}{\&Integral;}_0^T{s}^2\left(t\right)\mathrm{dt}=\frac{{\mathrm{<figure-callout\; id="0"\; label="E\; s\; N"\; filenames="HSA00000122387100022.png"\; state="\{\{state\}\}">E</figure-callout>}}_s}{N_{}}$

Determine statistics distribution under the various situations by above step, just can obtain the detection probability and the misinformation probability of energy detection method,

$P_{\mathrm{fa}}^{\mathrm{TD}}=P\{{\hat{V}}_{\mathrm{TD}}>{\mathrm{\&Gamma;}}_{\mathrm{TD}}|H_0\}=P\{{\mathrm{\&chi;}}_{2\mathrm{<figure-callout\; id="2"\; label="TW"\; filenames="HSA00000122387100012.png"\; state="\{\{state\}\}">TW</figure-callout>}}^2>{\mathrm{\&Gamma;}}_{\mathrm{TD}}\}$

$P_d^{\mathrm{TD}}=P\{{\hat{V}}_{\mathrm{TD}}>{\mathrm{\&Gamma;}}_{\mathrm{TD}}|H_1\}=P\{{\mathrm{\&chi;}}_{2\mathrm{TW}}^{\&prime;2}>{\mathrm{\&Gamma;}}_{\mathrm{TD}}\}$

H in the formula ₀Represent that there is not H in main user ₁Expression is main with existing;

Under big long-pending (TW＞125) condition of time wide bandwidth, counting after the sampling is a lot of and separate, can get according to central-limit theorem, and the distribution of sampled point is similar to Normal Distribution.So under the idle situation of main user, statistic

Quadratic sum by 2TW independent Gaussian stochastic variable is formed, through deriving

Obey N (2TW, Gaussian Profile 4TW);

Under the condition that main user exists,

Obey the Gaussian Profile of N (2TW+ λ, 4TW+ λ).Suppose to judge that the energy threshold that main subscriber signal exists is Γ _TD, then false alarm probability is:

$P_{\mathrm{fa}}^{\mathrm{TD}}=Q\left(\frac{{\mathrm{\&Gamma;}}_{\mathrm{TD}}-{\mathrm{\&mu;}}_0}{{\mathrm{\&sigma;}}_0}\right)$

Detection probability is:

$P_d^{\mathrm{TD}}=Q\left(\frac{{\mathrm{\&Gamma;}}_{\mathrm{TD}}-{\mathrm{\&mu;}}_1}{{\mathrm{\&sigma;}}_1}\right)$

In the formula

$Q\left(x\right)=\frac{1}{\sqrt{2\mathrm{\&pi;}}}{\&Integral;}_x^{\&infin;}{e}^{{-\mathrm{<figure-callout\; id="2"\; label="y"\; filenames="HSA00000122387100012.png"\; state="\{\{state\}\}">y</figure-callout>}}^2/2}\mathrm{dy}$

2, if do not detect main user, judge that main user does not exist; Otherwise, carry out frequency domain detection.

If do not detect main user, judge that main user does not exist.Then start the frequency domain energy measuring when detecting main user, frequency domain energy measuring model as shown in Figure 2, it mainly is made up of analog to digital converter (A/D), fast Fourier transform module, square algorithm module (Square-Law), COMPREHENSIVE CALCULATING module and threshold judgement module (Threshold).Outer signals r (t) at first becomes digital signal y (n) by analog to digital converter with analog signal, y (n) is converted to frequency-region signal by the FFT conversion with time-domain signal, then frequency-region signal is imported square algorithm module picked up signal energy, by the COMPREHENSIVE CALCULATING module frequency-region signal is averaged, obtain the sample value of target frequency bands

Will

With predefined decision threshold Γ _FDRelatively, judge whether to exist main subscriber signal.When in the channel during physical presence master subscriber signal, time domain detection module (TD) is to receiving to such an extent that signal is analyzed and handled and and time domain decision threshold Γ _TDHave relatively Probability Detection to signal, have Probability fail to report, exist if adjudicate main subscriber signal, then start the frequency domain detection module and participate in detecting.

Detect with time domain and to compare, the advantage of frequency domain detection is to be to detect behind the frequency domain detection signal by spatial transform, has strengthened its application flexibility, thereby has improved detection accuracy; Shortcoming is to have introduced the FFT module, has consumed the power consumption of whole energy detection module 70%-80%.The The whole analytical process and the time domain of frequency domain energy measuring are basic identical, no longer describe in detail here.

3, if frequency domain does not detect main user, judge that main user does not exist; Otherwise, judge that main user exists

Because it is small probability event that main user exists, therefore when time domain detects main user and exists,,, thereby the result of determination of this step has been arranged just we have carried out frequency domain detection in order to guarantee the precision that detects.

Dual threshold energy detection system illustraton of model as shown in Figure 3.Among the figure

Be illustrated under the condition of main user's existence, the time domain module is with Γ _TDFor judging whether main user exists the detection probability under the situation of threshold value,

For judging the non-existent probability of main user; Among the figure

Be illustrated under the condition of main user's existence, frequency domain module is with Γ _FDFor judging whether main user exists the detection probability under the situation of threshold value,

For judging the non-existent probability of main user; Among the figure

Be illustrated under the non-existent condition of main user, the time domain module is with Γ _TDFor judging whether main user exists the false alarm probability under the situation of threshold value,

For judging the non-existent probability of main user; Among the figure

Be illustrated under the non-existent condition of main user, frequency domain module is with Γ _FDFor judging whether main user exists the false alarm probability under the situation of threshold value, For judging the non-existent probability of main user; Here establish

Be dual threshold energy detection system whole detection probability,

Be the whole false alarm probability of dual threshold energy detection system.

According to the flow process that Fig. 3 represents, the whole detection probability and the false alarm probability that can obtain the dual threshold energy detection system are as follows:

$P_d^{\mathrm{OA}}=P_{\mathrm{\&lambda;}}{P}_d^{\mathrm{TD}}{P}_d^{\mathrm{FD}}+(1-P_{\mathrm{\&lambda;}})(1-P_{\mathrm{fa}}^{\mathrm{FD}}{P}_{\mathrm{fa}}^{\mathrm{TD}})$

$P_{\mathrm{fa}}^{\mathrm{OA}}=P_{\mathrm{fa}}^{\mathrm{TD}}{P}_{\mathrm{fa}}^{\mathrm{FD}}$

P wherein _λThe probability of physical presence master user subscriber signal in the expression channel, (1-P _λ) actual dereliction user's probability in the expression channel.A large amount of measurement Research proof P according to FCC (FCC) and American National radio net research experiment bed (NRNRT) _λGenerally 10 ^-2Magnitude.

When big, under the long-pending condition of wide bandwidth when (TW＞125), have:

$P_d^{\mathrm{OA}}=P_{\mathrm{\&lambda;}}Q\left(\frac{{\mathrm{\&Gamma;}}_{\mathrm{TD}}-{\mathrm{\&mu;}}_1}{{\mathrm{\&sigma;}}_1}\right)Q\left(\frac{{\mathrm{\&Gamma;}}_{\mathrm{FD}}-{\mathrm{\&mu;}}_1}{{\mathrm{\&sigma;}}_1}\right)+(1-P_{\mathrm{\&lambda;}})(1-Q\left(\frac{{\mathrm{\&Gamma;}}_{\mathrm{TD}}-{\mathrm{\&mu;}}_0}{{\mathrm{\&sigma;}}_0}\right)Q\left(\frac{{\mathrm{\&Gamma;}}_{\mathrm{FD}}-{\mathrm{\&mu;}}_0}{{\mathrm{\&sigma;}}_0}\right))$

$P_{\mathrm{fa}}^{\mathrm{OA}}=Q\left(\frac{{\mathrm{\&Gamma;}}_{\mathrm{TD}}-{\mathrm{\&mu;}}_0}{{\mathrm{\&sigma;}}_0}\right)Q\left(\frac{{\mathrm{\&Gamma;}}_{\mathrm{FD}}-{\mathrm{\&mu;}}_0}{{\mathrm{\&sigma;}}_0}\right)$

According to the system works flow process, the time domain detection module is in running order always, has only when the time domain testing result to surpass Γ _TDThe time, start the frequency domain detection module and detect.So overall power E of dual threshold energy measuring model _OAProportional with the probability of use of frequency domain detection module.Here the probability of use that defines the frequency domain detection module is the participation factor alpha of frequency domain detection module.E is rule of thumb generally arranged _FD=2E _TD, E wherein _TDExpression frequency domain energy detection module overall power, E _TDExpression time domain energy detection module overall power.So the size of α has directly determined the overall power of dual threshold energy detection system.According to the physical significance of α, it is as follows that we can write out its mathematic(al) representation:

$\mathrm{\&alpha;}={\mathrm{AP}}_d^{\mathrm{TD}}+(1-A)P_{\mathrm{fa}}^{\mathrm{TD}}$

The meaning of α is to think when the time domain detection module to have detected authorized user, just starts the frequency domain detection module and participates in detecting.And the detection performance of time domain detection module and threshold value Γ _TDDirect relation is arranged, so have:

$\mathrm{\&alpha;}=\mathrm{AP}\{{\mathrm{\&chi;}}_{2\mathrm{TW}}^{\&prime;2}>{\mathrm{\&Gamma;}}_{\mathrm{TD}}\}+(1-A)P\{{\mathrm{\&chi;}}_{2\mathrm{<figure-callout\; id="2"\; label="TW"\; filenames="HSA00000122387100012.png"\; state="\{\{state\}\}">TW</figure-callout>}}^2>{\mathrm{\&Gamma;}}_{\mathrm{TD}}\}$

When big under the long-pending condition of wide bandwidth when (TW＞125):

$\mathrm{\&alpha;}=\mathrm{AQ}\left(\frac{{\mathrm{\&Gamma;}}_{\mathrm{TD}}-{\mathrm{\&mu;}}_1}{{\mathrm{\&sigma;}}_1}\right)+(1-A)Q\left(\frac{{\mathrm{\&Gamma;}}_{\mathrm{TD}}-{\mathrm{\&mu;}}_0}{{\mathrm{\&sigma;}}_0}\right)$

The functional relation that has the above-mentioned derivation of equation can participate between factor alpha and dual threshold energy detection system whole detection probability and the whole misinformation probability is as follows:

$P_d^{\mathrm{OA}}=P_{\mathrm{\&lambda;}}{P}_d^{\mathrm{TD}}{P}_d^{\mathrm{FD}}+(1-P_{\mathrm{\&lambda;}})(1-P_{\mathrm{fa}}^{\mathrm{FD}}\frac{\mathrm{\&alpha;}-{\mathrm{AP}}_d^{\mathrm{TD}}}{1-A})$

$P_{\mathrm{fa}}^{\mathrm{OA}}=\frac{\mathrm{\&alpha;}-{\mathrm{AP}}_d^{\mathrm{TD}}}{1-A}{P}_{\mathrm{fa}}^{\mathrm{FD}}$

According to the simulation result of Fig. 4, the overall power E of system when we only use frequency domain or time domain detection module as can be seen _OAThe rising with threshold value Γ does not raise, and is fixed value and E _FD≈ 2E _TDThis mainly is because time domain and frequency domain detection power consumption master use with detection system hardware device power consumption relevant devices power consumption in case determine that the entire system power consumption just remains unchanged, and and Γ _TDAnd Γ _FDBe provided with irrelevant.Dual threshold energy detection system power consumption is then along with time domain threshold value Γ _TDThe rising power consumption reduce, this is because Γ _TDThe dual threshold energy detection system all can start the frequency domain detection system ninety-nine times out of a hundred when equating with noise power, so system power dissipation is approximately equal to frequency domain and time domain detection system power consumption sum.Along with threshold value Γ _TDThe probability of rising frequency domain detection system start-up more and more lower, so dual threshold energy detection system power consumption is worked as Γ more and more near time domain detection system power consumption _TDEqual background noise and main subscriber signal sum time-frequency domain detection system starts hardly, so dual threshold energy detection system power consumption equates with time domain detection system power consumption.

As can be seen from Figure 5 as territory threshold value Γ _TDThe system parameters factor alpha is approximately 1 when low, and it is higher to mean that the frequency domain detection module starts probability, along with Γ _TDRaise, the probability that the time domain detection module detects main user's appearance is more and more lower, and then the probability of frequency domain module startup is also more and more lower, is close to 0 so system participates in coefficient.

The content that is not described in detail in this specification belongs to this area professional and technical personnel's known prior art.

The above only is preferable enforcement of the present invention, and is in order to restriction the present invention, within the spirit and principles in the present invention not all, any modification of being done, is equal to replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (5)

1. low power consumption time domain and frequency domain double threshold combined energy detection algorithm, a kind of time domain energy that is used for Ubiquitous Network detects a kind of frequency spectrum detecting method that combines with the frequency domain energy measuring.Under the situation that guarantees certain precision, all lower than the energy detection method power consumption of any time domain of independent use or frequency domain.

The step of this algorithm is as follows:

A) carrying out time domain to measured signal detects;

B) if do not detect main user, judge that main user does not exist; Otherwise, carry out frequency domain detection;

C) if do not detect main user, judge that main user does not exist; Otherwise main user exists.

2. method according to claim 1, it is characterized in that,, require the time domain detection module to detect in real time for the low-power consumption of guaranteeing to satisfy under certain required precision detects, and the frequency domain detection module is carried out the non real-time detection, promptly just works when the time domain detection module judges that main user exists.

3. method according to claim 1 is characterized in that, this detection method combines time domain neatly and detects and two kinds of frequency spectrum detecting methods of frequency domain detection.

4. method according to claim 1 is characterized in that, for ease of analyzing the power consumption situation of this detection method, has introduced the notion of the participation factor alpha of frequency domain detection module, i.e. the probability of use of frequency domain detection module.Along with the increase of α, the power consumption of system is also increasing.Provided definite method of α at last.

5. method according to claim 1 is characterized in that, has comprised the participation factor alpha in the detection probability of system and the rate of false alarm.

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