CN106961311A - Cognition wireless network cooperative spectrum detection method based on sensing node degree of belief - Google Patents

Abstract

The present invention relates to a kind of cognition wireless network cooperative spectrum detection method based on sensing node degree of belief, the degree of belief thresholding appropriate by setting, the higher sensing node of degree of belief is selected to participate in cooperation, global frequency spectrum detection decision statistics reasonable in design and decision threshold carry out frequency spectrum detection judgement, eliminate the influence that low safe node and failure or malicious node detect performance to collaboration frequency spectrum, network overhead is reduced, network lifecycle is extended.It is exactly specifically that the local frequency spectrum detecting result each obtained is issued fusion center by each sensing node, fusion center calculates the degree of belief of corresponding sensing node according to the verification and measurement ratio and false alarm rate of each sensing node, the high node of degree of belief is selected to participate in cooperation, refuse low degree of belief node and failure or malicious node participates in collaboration frequency spectrum detection, suitable historical information length and weighted factor are selected, reduces the sporadic wrong influence to frequency spectrum detection during sensing node local detection.

Description

Cognition wireless network cooperative spectrum detection method based on sensing node degree of belief

Technical field

The present invention relates to the frequency spectrum perception in cognition radio communication network and detection technique, more particularly to one kind The collaboration frequency spectrum detection technique based on sensing node degree of belief under environment of cognitive radio network.

Background technology

With the continuous growth of wireless data service, available radio spectrum resources become more and more rare.On the other hand, Current radio spectrum resources utilization rate is very unbalanced, there is a large amount of availability of frequency spectrums very low mandate frequency range.Cognition wireless Electric (Cognitive Radio, CR) is Intellisense spectrum environment, efficiently using one of technological means of wireless frequency spectrum, is caused The extensive concern of people.

In cognition wireless network, in order to avoid the interference to master (mandate) user, cognitive user needs the frequency to surrounding Spectrum environment continuously accurately and quickly perceive.Therefore, quickly and accurately frequency spectrum perception technology is to realize cognition wireless network Basis.Because there is the biography complicated and changeable such as hidden terminal, channel multi-path, shadow fading in most of cognitive wireless network systems Characteristic is broadcast, the frequency spectrum detection of single node is difficult to ensure that the accurate of frequency spectrum detection, and collaboration frequency spectrum detection can effectively improve frequency spectrum The performance of detection.But during collaborative spectrum sensing, the reliability of each sensing node frequency spectrum detecting result is different 's.If the relatively low node of some reliabilities participates in the perception data fusion of fusion center, often not but not in raising fusion The frequency spectrum perception performance of the heart, can deteriorate frequency spectrum perception performance on the contrary, decline the perception degree of accuracy, while can also cause Internet resources Waste.

The content of the invention

It is an object of the invention to overcome above-mentioned the deficiencies in the prior art, commenting for sensing node frequency spectrum detection reliability is solved Valency problem, designs a kind of cooperative spectrum detection method based on cognition network sensing node degree of belief.

In order to achieve the above object, a kind of cognition wireless network collaboration frequency spectrum inspection based on sensing node degree of belief of the present invention Survey method, the cognition wireless network includes a primary user, N number of cognitive user and a frequency spectrum detection fusion center, the N Individual cognitive user forms N number of frequency spectrum detection sensing node, and the cooperative spectrum detection method comprises the following steps：

Step 1, i-th of sensing node received signal in frequency spectrum detection are x_i(t) hypothesis testing is carried out, this is obtained Ground testing result d_i, and by local detection result d_iSend to fusion center, i=1,2 ..., N；

Step 2, fusion center calculate the verification and measurement ratio P of each sensing node after -1 frequency spectrum detection of kth_diAnd false-alarm (k-1) Rate P_fi(k-1)；Wherein, verification and measurement ratio P_di(k-1) definition is：In preceding k-1 frequency spectrum detection, fusion center judges primary user Signal is present and i-th of sensing node also judges the probability that primary user's signal is present, and the verification and measurement ratio of first run frequency spectrum detection is 1； False alarm rate P_fi(k-1) definition is：In preceding k-1 frequency spectrum detection, fusion center judges that primary user's signal is not present but i-th Sensing node judges the probability that primary user's signal is present, and the false alarm rate of first run frequency spectrum detection is 0；

Step 3, the degree of belief for calculating sensing node, the degree of belief of i-th of sensing node is during kth time detection

Wherein, R_i(k) degree of belief of i-th of sensing node when for kth time frequency spectrum detection, α and β are that node is locally examined respectively The verification and measurement ratio and false alarm rate weighted factor of survey, alpha+beta=1, i=1,2 ..., N；

Step 4, fusion center are by the average value or back-end crop average value of all sensing node degree of beliefs during kth time frequency spectrum detection It is used as the degree of belief thresholding of sensing node selection during kth time frequency spectrum detection；

Step 5, selection degree of belief are more than or equal to the cooperation of the sensing node participation fusion center of the degree of belief thresholding Frequency spectrum detection；

Step 6, the local detection result average value for calculating participation collaboration frequency spectrum detection senses node M participates in the number of the sensing node of fusion center collaboration frequency spectrum detection when being kth time frequency spectrum detection；

Step 7, the global frequency spectrum detection decision statistics Hr (k) for calculating fusion center,

Wherein, hsl is the historical information length of default sensing node, ρ is weighted factor, 0<ρ<1；

If the global statistics Hr (k) of step 8, fusion center is more than local detection result average value Gr (k), in fusion The heart judgement primary user be present, and otherwise fusion center judgement primary user is not present.

The present invention also has following feature：

1st, it is s (t) that the primary user authorizes the primary user's signal transmitted in frequency spectrum at it, and i-th of sensing node is in frequency spectrum Received signal is x during detection_i(t), the channel gain of the sensing node is h_i(t), the additive white Gaussian noise of channel is n_i (t), wherein i=1 ... N.

2nd, the degree of belief thresholding that sensing node is selected during kth time frequency spectrum detection

3rd, the optimal value that α optimal value is 0.6, β is 0.4.

4th, hsl optimum length is 15.Default sensing node historical information length hsl is too short, there is sensing node even Influence of the hair property mistake to frequency spectrum detection；The historical information length of default sensing node is long, and computational complexity is improved, typically 10<hsl<20。

5th, ρ optimal value is 0.65.

If the 6, fusion center judgement primary user is present, the frequency spectrum of detection section is busy in network, and cognitive user is unusable The frequency range carries out service communication；If fusion center judgement primary user be not present, detect that the frequency spectrum of section is idle in network, cognition is used Family can use the frequency range to carry out service communication.

The inventive method is the degree of belief thresholding appropriate by setting, choosing during cognition wireless network collaboration frequency spectrum is detected Select the higher sensing node of degree of belief and participate in cooperation, global frequency spectrum detection decision statistics reasonable in design and decision threshold are carried out Frequency spectrum detection is adjudicated, and eliminates the influence that low safe node and failure or malicious node detect performance to collaboration frequency spectrum, is reduced Network overhead, extends network lifecycle.It is exactly specifically each sensing node by the local frequency each obtained Spectrum testing result issues fusion center, and fusion center calculates corresponding perceive according to the verification and measurement ratio and false alarm rate of each sensing node and saved The degree of belief of point, the high node of selection degree of belief participates in cooperation, refuses low degree of belief node and failure or malicious node is participated in Collaboration frequency spectrum is detected, selects suitable historical information length hsl and weighted factor ρ, during reduction sensing node local detection Influence of the sporadic mistake to frequency spectrum detection.

The invention has the advantages that：

(1) by setting sensing node degree of belief and its thresholding, select high degree of belief node to participate in cooperation, reduce participation The sensing node number of cooperation, has saved network overhead, eliminates low safe node and failure or malicious node to cooperation The influence of frequency spectrum detection performance, improves detection performance；

(2) updated by the dynamic of sensing node degree of belief, reduce sporadic mistake during sensing node local detection Influence to frequency spectrum detection, improves the accuracy of global frequency spectrum detection；

(3) by introducing sensing node historical information decay weighted factor, make the result of global decision more accurate, improve The accuracy of collaboration frequency spectrum detection.

Brief description of the drawings

The present invention is further illustrated below in conjunction with the accompanying drawings.

Fig. 1 is cognitive radio networks system schematic.

Fig. 2 is embodiment of the present invention frequency spectrum detecting method flow chart.

Embodiment

The present invention will be further described with specific embodiment below in conjunction with the accompanying drawings.

Such as Fig. 1, include the cognitive wireless of at least one primary user, N number of cognitive user and a fusion center at one In network system, cognitive user is detected to surrounding spectrum environment, and testing result has two kinds：1st, the frequency spectrum of section is detected in network Busy, primary user is using this section of frequency spectrum；2nd, detect that the frequency spectrum of section is idle in network, primary user is not using this section of frequency spectrum.

Sensing node i (can essentially use any single node frequency spectrum detecting method, such as using energy detection algorithm Matched filter detection, cyclo-stationary detection, characteristic value detection etc.) to the signal x that receives_i(t) it whether there is primary user in Signal s (t) carries out hypothesis testing, obtains local detection result d_i.If primary user signal s (t) is present, testing result d_i= 1；If primary user signal s (t) is not present, testing result d_i=0.

Whether each sensing node sends local detection result to fusion center, deposited by fusion center judgement detection section frequency spectrum In primary user signal s (t).Fusion center is to basic step such as Fig. 2 with the presence or absence of primary user's progress hypothesis testing, detailed process It is as follows：

First, fusion center, which is calculated, characterizes sensing node frequency spectrum detection accuracy after (k-1) secondary (last time) frequency spectrum detection Verification and measurement ratio P_diAnd false alarm rate P (k-1)_fi(k-1).Verification and measurement ratio P_di(k-1) definition is：In preceding k-1 frequency spectrum detection, melt Conjunction center judges that primary user's signal deposits (d_FC=1) and i-th of sensing node also judge primary user's signal exist (d_i=1) Probability, i.e. p_di(k)=p { d_i=1 | d_FC=1 }, the verification and measurement ratio of first run frequency spectrum detection is 1；False alarm rate P_fi(k-1) definition is： In preceding k-1 frequency spectrum detection, fusion center judges that (d is not present in primary user's signal_FC=0) but i-th sensing node judges master There is (d in subscriber signal s (t)_i=probability 1), i.e. p_fi(k)=p { d_i=1 | d_FC=0 }, the false alarm rate of first run frequency spectrum detection is 0。

Then, sensing node i is calculated according to the weighted factor and β of given node local detection verification and measurement ratio and false alarm rate Degree of belief R_i(k)=α P_di(k-1)+β(1-P_fi(k-1)).In this example, α=0.6, β=0.4.In first run frequency spectrum detection, The degree of belief of each sensing node is 1, since second of frequency spectrum detection, is often detected once, and the degree of belief of sensing node updates Once.

Secondly, using the average value of all sensing node degree of beliefs during kth time detection as kth detect when sensing node choosing The degree of belief thresholding selectedSelect degree of belief to be more than or equal to the sensing node of the thresholding to participate in fusion The collaboration frequency spectrum detection of the heart, calculates the average value for the local detection result for participating in collaboration frequency spectrum detection node Wherein, m=1,2 ..., M, M participate in when being kth time detection the detection of fusion center collaboration frequency spectrum sensing node number.

Again, the historical information length hsl and weighted factor ρ of sensing node, 0 are given<ρ<1, calculate fusion center Global frequency spectrum detection decision statistics.In this example, hsl=15, ρ=0.65.

Finally, fusion center is made global frequency spectrum detection according to global frequency spectrum detection decision statistics and decision threshold and sentenced Certainly.If the global statistics Hr (k) of fusion center is more than decision threshold Gr (k), fusion center judgement primary user be present, it is assumed that H₁Set up, the frequency spectrum of detection section is busy in network, cognitive user cannot use the frequency range to carry out service communication；Otherwise in merging The heart judgement primary user be not present, it is assumed that H₀Set up, the frequency spectrum of detection section is idle in network, and cognitive user can use the frequency range to enter Row service communication.

In addition to the implementation, the present invention can also have other embodiment.All use equivalent or equivalent transformation shape Into technical scheme, all fall within the protection domain of application claims.

Claims (7)

1. the cognition wireless network cooperative spectrum detection method based on sensing node degree of belief, the cognition wireless network includes one Individual primary user, N number of cognitive user and a frequency spectrum detection fusion center, N number of cognitive user form N number of frequency spectrum detection and perceived Node, the cooperative spectrum detection method comprises the following steps：

Step 1, i-th of sensing node received signal in frequency spectrum detection are x_i(t) hypothesis testing is carried out, is locally examined Survey result d_i, and by local detection result d_iSend to fusion center, i=1,2 ..., N；

Step 2, fusion center calculate the verification and measurement ratio P of each sensing node after -1 frequency spectrum detection of kth_diAnd false alarm rate P (k-1)_fi (k-1)；Wherein, verification and measurement ratio P_di(k-1) definition is：In preceding k-1 frequency spectrum detection, fusion center judges that primary user's signal is deposited And i-th of sensing node also judges the probability that primary user's signal is present, the verification and measurement ratio of first run frequency spectrum detection is 1；False alarm rate P_fi(k-1) definition is：In preceding k-1 frequency spectrum detection, fusion center judges that primary user's signal is not present but i-th perceives section Point judges the probability that primary user's signal is present, and the false alarm rate of first run frequency spectrum detection is 0；

Step 3, the degree of belief for calculating sensing node, the degree of belief of i-th of sensing node is during kth time detection

$R_i\left(k\right)=\left\{\begin{array}{cc}1,& k=1\\ {\mathrm{\&alpha;P}}_{di}(k-1)+\mathrm{\&beta;}(1-P_{fi}(k-1\left)\right),& k>1\end{array}\right.$

Wherein, R_i(k) degree of belief of i-th of sensing node when for kth time frequency spectrum detection, α and β are node local detection respectively Verification and measurement ratio and false alarm rate weighted factor, alpha+beta=1, i=1,2 ..., N；

Step 4, fusion center using the average value or back-end crop average value of all sensing node degree of beliefs during kth time frequency spectrum detection as The degree of belief thresholding of sensing node selection during kth time frequency spectrum detection；

Step 5, selection degree of belief are more than or equal to the collaboration frequency spectrum of the sensing node participation fusion center of the degree of belief thresholding Detection；

Step 6, the local detection result average value for calculating participation collaboration frequency spectrum detection senses nodeM=1, 2 ..., M, M participate in when being kth time frequency spectrum detection the detection of fusion center collaboration frequency spectrum sensing node number；

Step 7, the global frequency spectrum detection decision statistics Hr (k) for calculating fusion center,

$Hr\left(k\right)=\left\{\begin{array}{cc}\frac{Gr\left(1\right){\mathrm{\&rho;}}^{k-1}+Gr\left(2\right){\mathrm{\&rho;}}^{k-2}+...+Gr\left(k\right){\mathrm{\&rho;}}^0}{{\mathrm{\&rho;}}^{k-1}+{\mathrm{\&rho;}}^{k-2}+...+{\mathrm{\&rho;}}^0}& k<hsl\\ \frac{Gr\left(1\right){\mathrm{\&rho;}}^{hsl-1}+Gr\left(2\right){\mathrm{\&rho;}}^{hsl-2}+...+Gr\left(hsl\right){\mathrm{\&rho;}}^0}{{\mathrm{\&rho;}}^{hsl-1}+{\mathrm{\&rho;}}^{hsl-2}+...+{\mathrm{\&rho;}}^0}& k\&GreaterEqual;hsl\end{array}\right.$

Wherein, hsl is the historical information length of default sensing node, and ρ is weighted factor, 0<ρ<1；

If the global statistics Hr (k) of step 8, fusion center is more than local detection result average value Gr (k), fusion center is sentenced Certainly primary user is present, and otherwise fusion center judgement primary user is not present.

2. the cognition wireless network cooperative spectrum detection method based on sensing node degree of belief according to claim 1, it is special Levy and be：It is s (t) that the primary user authorizes the primary user's signal transmitted in frequency spectrum at it, and i-th of sensing node is in frequency spectrum detection When received signal be x_i(t), the channel gain of the sensing node is h_i(t), the additive white Gaussian noise of channel is n_i(t), Wherein i=1 ... N.

3. the cognition wireless network cooperative spectrum detection method based on sensing node degree of belief according to claim 1, it is special Levy and be：The degree of belief thresholding of sensing node selection during kth time frequency spectrum detection

4. the cognition wireless network cooperative spectrum detection method based on sensing node degree of belief according to claim 1, it is special Levy and be：, the optimal value that α optimal value is 0.6, β be 0.4.

5. the cognition wireless network cooperative spectrum detection method based on sensing node degree of belief according to claim 1, it is special Levy and be：Hsl optimal value is 15.

6. the cognition wireless network cooperative spectrum detection method based on sensing node degree of belief according to claim 1, it is special Levy and be：ρ optimal value is 0.65.

7. the cognition wireless network cooperative spectrum detection method based on sensing node degree of belief according to claim 1, it is special Levy and be：If fusion center judgement primary user is present, the frequency spectrum of detection section is busy in network, cognitive user it is unusable this frequently Duan Jinhang service communications；If fusion center judgement primary user be not present, detect that the frequency spectrum of section is idle in network, cognitive user can To carry out service communication using the frequency range.