CN102546059A - Non-supervision clustering-based distributed cooperative spectrum sensing method for cognitive self-organizing network - Google Patents

Abstract

A non-supervision clustering-based distributed cooperative spectrum sensing method for a cognitive self-organizing network relates to the field of cognitive radio in wireless communication technology. Aiming at solving the problems of difficult distributed cooperation of the cognitive self-organizing network and large overhead of whole-network cooperation, the method adopts the latest achievements of a non-supervision clustering theory and a co-recognition theory to achieve fully-distributed, steady and reliable distributed cooperative spectrum sensing under the condition of simplifying the overhead of network sensing; and users with potential optimal detection performance spontaneously gather only via information interaction between neighbors, further the users carry out cooperation spectrum sensing by utilizing an average co-recognition protocol, and a sensing result is broadcast to the whole network users. The method does not require local users to receive apriori information of noise-signal ratio, and does not need any central controllers, thereby greatly lowering sensing overhead and acquiring detection performances similar to optimal soft combination solution.

Description

In the cognitive self-organizing network based on the distributed cooperation frequency spectrum sensing method that does not have the supervision cluster

Technical field

The present invention relates to the cognition wireless electrical domain in the wireless communication technology, specifically is that a kind of application does not have the new method that the theoretical latest developments with the common recognition theory of supervision cluster realize distributed cooperation frequency spectrum perception in the cognitive self-organizing network.

Background technology

At present,, the demand of radio spectrum resources also is exponential increase, makes that frequency spectrum resource " scarcity " problem in the following radio communication becomes increasingly conspicuous along with the quick growth of radio communication service kind.Cognitive radio technology utilizes the idle frequency range of authorized user with the mode of " waiting for an opportunity to insert " under the condition that guarantees authorized user service quality; Improved the service efficiency of frequency spectrum greatly; Be the effective ways that solve " frequency spectrum is deficient " problem, have important and practical meanings and wide application prospect.The frequency spectrum perception technology is used to effectively detect the operating state of current authorized user, to seek spectrum opportunities and the interference of avoiding authorized user or main user (primary user is called for short PU).Therefore, effectively the frequency spectrum perception technology is the prerequisite and the basis of cognition wireless network operate as normal.

Because single cognitive user or secondary user's (secondary user; Abbreviation SU) frequency spectrum perception performance very easily receives the influence of factors such as shadow effect in the wireless channel, multipath fading, hidden terminal and exposed terminal and worsens; The method that people have proposed many SU cooperation frequency spectrum perception (cooperative spectrum sensing is called for short CSS) overcomes these problems.

From whether there being the angle of fusion center, present CSS method mainly comprises following two types:

Center type CSS: in center type CSS, each SU at first carries out local frequency spectrum perception, then sensing results is uploaded to fusion center, and fusion center is made the judgement whether spectrum opportunities exists after the sensing results of each SU is carried out data fusion.Current, gradually ripe about the research of center type CSS, the advantage of this method is to realize easily the optimization with the whole network perceptual performance of obtaining of the whole network information; The deficiency of this method is too to rely on infrastructure such as fusion center, and because of the single point failure method that makes lost efficacy, network extensibility and robustness are relatively poor easily.

Distributed C S: in Distributed C S, each SU at first carries out local frequency spectrum perception, then each SU only and carry out information interaction, fusion between the neighbours, through the limited number of time iteration, finally each SU independently makes the judgement whether spectrum opportunities exists.This Distributed C S method does not rely on infrastructure such as fusion center, and the robustness and the extensibility of network are better.Given this advantage does not have the center, adaptive cognitive self-organizing network causes academia and the extensive interest of industrial quarters gradually, also begins to receive research staff's close attention gradually about the design of Distributed C S method in recent years.

Present Distributed C S method is only considered the scene that network size is less, and supposes that all users participate in cooperation.Yet, in the cognitive self-organizing network of being considered the number of SU more for a long time, all SU participate in cooperating the huge perception expense that will bring; Simultaneously, consider factors such as path loss, multipath fading and shadow effect, also can there be significant difference in the detecting reliability that is in the SU of different spatial.Therefore, how to excavate and utilize these difference effectively, under the brief condition of network aware expense, realize sane, frequency spectrum perception is a problem with important significance for theories and practical value reliably.

Not having supervision cluster (Unsupervised clustering) theory is the strong instrument that addresses the above problem.Its thought derives from the observation analysis to biocenose intelligence phenomenon at first, wild goose swarming row for example, bee colony gathering honey etc.In recent years; Do not have supervision cluster theory and obtained using widely (list of references: Pedro A F, Alfonso C, Georgios B G in fields such as distributed control and decision-making, multiple agent cooperation and sensor network distribution type parameter Estimation; " Distributed clustering using wireless sensor networks; " IEEE J Sel Topics Signal Process, 2011,5 (4): 707-724).Up-to-date theoretical progress as pattern recognition and artificial intelligence field; Not having the theoretical core concept of supervision cluster is: " no tutor's self-study ", and promptly each user obtains the observed quantity to environment at first separately in the network, based on there not being supervision cluster agreement; Each user and neighbours carry out information interaction; Through iteration repeatedly, under the situation of no center control telegon, the user that performance is close can spontaneously gather together.

Common recognition (Consensus) theory is the key technology that realizes that distributed data merges between the user.Its basic principle is: each user has different environment observed quantity separately at first, and based on the common recognition agreement, each user and neighbours carry out information interaction; Through iteration repeatedly, under the situation of no center control telegon, form common recognition or consistency understanding (list of references: R.Olfati-Saber between the end user to the environmental observation amount; J.Fax; And R.Murray, " Consensus and cooperation in networked multi-agent systems, " Proc IEEE; 2007,95 (1): 215-233).

Summary of the invention

The objective of the invention is problem to distributed cooperation in the cognitive self-organizing network is difficult, the whole network cooperation expense is big; Integrated application does not have the latest developments of supervising cluster theory and common recognition theory, is implemented in sane, reliable distributed cooperation frequency spectrum perception under the brief condition of perception expense.

Technical scheme of the present invention is:

In a kind of cognitive self-organizing network based on the distributed cooperation frequency spectrum sensing method that does not have the supervision cluster; Under the situation of no control centre; At first confirm to have the cognitive user S set BS of optimal perceived performance through unsupervised clustering; Detect based on the cooperation frequency spectrum between a plurality of cognitive user SUs in the theoretical SBS of realization of common recognition then, obtain the probability that corresponding frequency spectrum takies, utilize broadcast mechanism that testing result is informed outer a plurality of cognitive user SUs to SBS at last.

The present invention specifically may further comprise the steps:

Step 1. parameter initialization:

Each SU at first obtains self observed quantity to environment, and random initializtion self is to the local type barycenter and the local Lagrange multiplier of SBS class and non-SBS class then, and on this basis, each SU initialization belongs to coefficient for local type;

The weighted average of the environmental observation amount of all cognitive user in described " type barycenter " type of being meant;

The middle transition variable that described " Lagrange multiplier " is clustering algorithm does not have concrete physical significance;

Described " type ownership coefficient " is to characterize it to belong to the possibility of SBS class and non-SBS class;

Step 2. is confirmed SBS based on unsupervised clustering:

Each SU at first with mutual local type of barycenter of a hop neighbor SUs, based on this interactive information, each SU upgrades local type barycenter, local Lagrange multiplier and local type of ownership coefficient successively, this process iteration is carried out, and satisfies until stopping criterion for iteration;

After algorithm iteration stops, the local type barycenter of all SUs will be tending towards identical in the network, promptly reach " all SU class barycenter common recognitions ", but the local type of ownership coefficient that each SU obtains has nothing in common with each other.Based on this, each SU realizes not having the supervision cluster according to local type of ownership coefficient type of the carrying out ownership judgement of self;

Step 3. realizes distributed cooperation frequency spectrum detection in the SBS class based on the common recognition theory:

Each SU in the SBS class at first carries out local energy perception, and mutual local Perspective of Energy measured value then and between the neighbours SUs carries out data fusion based on the common recognition agreement, and through iteration repeatedly, all SUs in the final SBS class reach the common recognition to the spectrum energy measured value;

Based on the Perspective of Energy measured value of common recognition, each SU carries out this locality judgement, and to obtain frequency spectrum state-detection result be the frequency spectrum free time or take;

Step 4. broadcasting testing result:

The neighbours SU that each SU of SBS class is outer with testing result type of being broadcast to realizes that the whole network SU reaches common understanding to sensing results.

Unsupervised clustering of the present invention may further comprise the steps:

Step 1. parameter initialization:

1.1 based on historical perception information { E _i(m) | m=1 ..., M}, each SUi ∈ in the network 1 ..., N} at first obtains self observed quantity to spectrum environment:

$O_i=\frac{1}{M}\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{E}_i\left(m\right)$

E wherein _iDetected energy value when (m) being the m time perception, M are the number of times that adds up; When frequency spectrum is idle, E _i(m) only comprise noise energy; When frequency spectrum takies, E _i(m) be the energy that not only comprises noise, also comprise the energy of PU signal.

1.2 each SUi ∈ { 1 in the network; ...; N} random initializtion

k ∈ { 1; 2} and k ∈ { 1; 2};

k ∈ { 1 wherein; 2} be respectively SUi ∈ 1 ..., the SBS class that N} is local and the initial barycenter of non-SBS class; The weighted average of the observed quantity of all cognitive user in described barycenter type of being meant;

k ∈ 1,2} be respectively SUi ∈ 1 ...; The middle transition variable that the SBS class that N} is local and the initial Lagrange multiplier described " Lagrange multiplier " of non-SBS class are clustering algorithms does not have concrete physical significance;

1.3 each cognitive user SUi ∈ { 1 in the network; ..., local type of ownership coefficient

of N} initialization

$a_i^0\left(k\right)=\frac{{\left|\right|O_i-c_i^0\left(k\right)\left|\right|}^{-\frac{2}{p-1}}}{{\mathrm{\Σ}}_{k=1}^K{\left|\right|O_i-c_i^0\left(k\right)\left|\right|}^{-\frac{2}{p-1}}},\∀i\∈\{1,...,N\},\∀k\∈\left\{\mathrm{1,2}\right\}$

Wherein, p＞1.Here

is more near 1, and then the possibility of user SUi adding type k is big more; Otherwise;

more near 0, then the possibility of user SUi adding type k is more little;

Step 2. is not confirmed SBS based on there being the supervision cluster:

Each SU at first with mutual local type of barycenter of a hop neighbor SUs, based on this interactive information, each SU upgrades local type barycenter, local Lagrange multiplier and local type of ownership coefficient successively, this process iteration is carried out, and satisfies until stopping criterion for iteration; After iteration stopped, each SU obtained local type of ownership coefficient and local type barycenter (annotate: behind iteration convergence, all SUs are identical to of a sort local type barycenter in the network, promptly reach " type barycenter common recognition ", but local type of ownership coefficient have nothing in common with each other); On this basis, each SU realizes not having the supervision cluster according to local type of ownership coefficient type of the carrying out ownership judgement that obtains;

Concrete through carrying out following distributed iterative method realization: based on t=0; 1; 2 ... local type of each SUi of barycenter

that inferior iteration obtains, i ∈ { 1; ..., N} carries out iteration the t+1 time:

2.1 each SUi ∈ 1 ..., N} is with this locality type barycenter

Be broadcast to a hop neighbor user

Here S _iBe meant the set of the hop neighbor of SUi, d _IjDistance between expression cognitive user SUi and the one of which hop neighbor user SUj, d _ComRepresent between two SU can proper communication ultimate range;

2.2 each SUi ∈ 1 ..., N} upgrades local type barycenter, obtains:

$c_i^{t+1}\left(k\right)={\left(a_i^{t+1}\left(k\right)2\mathrm{\η}\right|S_i\left|\right)}^{-1}\{a_i^{t+1}\left(k\right)O_i-2{\mathrm{\λ}}_i^t\left(k\right)+\mathrm{\η}\underset{j\∈S_i}{\mathrm{\Σ}}[c_i^t\left(k\right)+c_J^t\left(k\right)\left]\right\},\∀i\∈\{1,...,N\},k\∈\left\{\mathrm{1,2}\right\}$

Wherein, η＞0; | S _i| the number of element among the hop neighbor S set i of expression SUi; Being a local Lagrange multiplier that dynamically updates, is the middle transition variable of clustering algorithm, does not have concrete physical significance, and its update rule is according to following step 2.4.

2.3 each SUi ∈ 1 ..., N} upgrades local type of ownership coefficient, obtains:

$a_i^{t+1}\left(k\right)=\frac{{\left|\right|O_i-c_i^{t+1}\left(k\right)\left|\right|}^{-\frac{2}{p-1}}}{{\mathrm{\Σ}}_{k=1}^K{\left|\right|O_i-c_i^{t+1}\left(k\right)\left|\right|}^{-\frac{2}{p-1}}},\∀i\∈\{1,...,N\},\∀k\∈\left\{\mathrm{1,2}\right\}$

Wherein, p＞1; Often get p=2 in the reality.Here

is more near 1, and then the possibility of user SUi adding type k is big more; Otherwise;

more near 0, then the possibility of user SUi adding type k is more little;

2.4 each SUi ∈ 1 ..., N} upgrades local Lagrange multiplier, obtains:

${\mathrm{\λ}}_i^{t+1}\left(k\right)={\mathrm{\λ}}_i^t\left(k\right)+\frac{\mathrm{\η}}{2}\underset{j\∈S_i}{\mathrm{\Σ}}[c_j^{t+1}\left(k\right)-c_i^{t+1}\left(k\right)],\∀i\∈\{1,...,N\},k\∈\left\{\mathrm{1,2}\right\}$

Step 2.1-2.4 iteration is carried out, through iteration repeatedly, if condition

With Satisfy simultaneously, then iteration stops; Wherein, ε _a, ε _cAnd ε _λBe and approach 0 positive number, often get ε in the reality _a=ε _c=ε _λ∈ [10 ^-6, 10 ^-3], value is more little, restrains slowly more, and convergence precision is high more; If end condition does not satisfy, then rebound step 2.1 satisfies as if condition, and then iteration stops; (annotate: what said end condition showed is key parameters

With

Relative increment with the increase of iterations t significant change takes place no longer.)

After 2.5 iteration stops, each SUi, i ∈ 1 ..., N} carries out this locality type ownership judgement according to following rule:

Each SUi, i ∈ 1 ..., N} confirms to get into SBS class or non-SBS class;

The SUi class that gets into k=1 then as

; And

be changed to 1,

is changed to 0;

Otherwise the SUi class that gets into k=2 then as

; And

be changed to 0,

is changed to 1;

Through the class ownership judgement of step 2.5, all SU that belong to the SBS class spontaneously flock together, and constitute SBS user's collection:

Wherein,

Expression barycenter bigger class, i.e. SBS class,

Expression SUi belongs to SBS class k ^◇, S _SBSRepresent that all belong to SBS class k ^◇The set of SU;

Step 3. realizes distributed cooperation frequency spectrum detection in the SBS class based on the common recognition theory:

The SUs self-organizing of optimal perceived performance ground forms on the basis of SBS class in step 2, and each SU in the SBS class at first carries out local energy perception, mutual local Perspective of Energy measured value then and between the neighbours SUs; Carry out data fusion based on the common recognition agreement; Through iteration repeatedly, all SUs in the final SBS class reach the common recognition to the spectrum energy measured value, based on this common recognition; Each SU carries out this locality judgement, and it is idle or take to obtain frequency spectrum state-detection result and be frequency spectrum:

Concrete through carrying out following distributed iterative method realization: based on t=0; 1; 2 ... this locality each SUi of common recognition variable

that inferior iteration obtains, i ∈ { 1; ..., N} carries out iteration the t+1 time:

3.1 initialization: the SBS class is an optimal perceived performance cognitive user S set _SBSEach interior SUi ∈ S _SBSCarry out local energy measuring, obtain Perspective of Energy measured value E _iAnd its initial local common recognition variable is made as

3.2 each SUi ∈ S _SBSWith the one of which hop neighbor variable of knowing together alternately Be each SUi ∈ S _SBSWith its common recognition variate-value

Be broadcast to a hop neighbor user

Receive common recognition variable simultaneously from a hop neighbor cognitive user SU

3.3 each SUi ∈ S _SBSCarry out information fusion according to following common recognition agreement:

$x_i^{t+1}=x_i^t+s_x\underset{j\∈S_i}{\mathrm{\Σ}}(x_j^t-x_i^t)$

S wherein _xThe＞0th, iteration step length is got usually

If condition Satisfy, then iteration stops, the whole network asymptotic reaching of on average knowing together, and consensus value is asymptotic does

ε wherein _xBe to approach 0 positive number, often get ε in the reality _a=ε _c=ε _λ∈ [10 ^-6, 10 ^-3], value is more little, restrains slowly more, and convergence precision is high more; If condition does not satisfy, then rebound step 3.2 satisfies as if condition, and then iteration stops; (annotate: what said end condition showed is key parameters Relative increment with the increase of iterations t significant change takes place no longer.)

In case 3.4 iteration stops, each SU obtains final common recognition variate-value x ^*, carry out following local judgement,

Wherein λ is the decision threshold of frequency spectrum detection, and λ detects performance working point (P corresponding to one _Fa, P _d), P _FaRefer to false alarm probability, promptly actual spectrum is idle, and court verdict is the probability that frequency spectrum takies; P _dBe detection probability, promptly actual spectrum takies the probability that court verdict also takies for frequency spectrum originally;

Step 4. broadcasting testing result, work below accomplishing:

Each SUi ∈ S in the SBS class _SBSThe neighbours SU that testing result d type of being broadcast to is outer (being the user who belongs to non-SBS class among the neighbours of SUi), thus make the user of non-SBS class upgrade cognition for frequency spectrum free time/seizure condition, realize that the whole network SU reaches common understanding to sensing results.

In the step 1 of the present invention, when frequency spectrum is idle, E _i(m) only comprise noise energy; When frequency spectrum takies, E _i(m) be the energy that not only comprises noise, also comprise the energy of PU signal.

P=2 of the present invention.

Beneficial effect of the present invention:

1, network operation is full distributed.Suggest plans, without any need for central coordinator (like the base station, access point, bunch first-class), all information interactions only carry out between neighbours.Therefore, suggest plans and possess that robustness is strong, network scalability good and advantage such as network overhead is little.

The complexity of 2, being suggested plans is very low.On the one hand, suggest plans in each SU need not carry out the estimation of himself received signal to noise ratio, do not need the prior information of PU position yet; On the other hand, suggest plans and need not spend extra time overhead and obtain the required observed quantity of cluster because this observed quantity utilization is historical detection information, this information can be learnt to obtain through the mode of off-line.

3, suggest plans greatly reduces network overhead when obtaining higher detecting reliability.Emulation shows that the present invention suggests plans and can obtain and the close detection performance of optimum soft information Merge Scenarios, but institute suggests plans and only need part SU to participate in cooperation, and the information interaction amount reduces greatly, and while distributed iterative algorithm the convergence speed is obviously accelerated.

Description of drawings

The cognitive radio system frame assumption diagram of Fig. 1 for being designed among the present invention.

Fig. 2 is a method flow diagram of the present invention.

Fig. 3 is the sketch map as a result of instantiation artificial network model among the present invention and cluster scheme.

Fig. 4 is the comparison sketch map of suggest plans among the present invention with the receiver operating characteristic curves of traditional scheme.

Embodiment

Below in conjunction with accompanying drawing and embodiment the present invention is further described.

As shown in Figure 1.A kind of cognitive radio system frame structure that the present invention designed.This frame structure is made up of four essential parts: cluster period, perception period, radio slot and transfer of data period.The cluster period is realized the distributed node selection, and the SU that promptly has optimum detection performance spontaneously assembles formation SBS class; The perception period is realized the distributed cooperation frequency spectrum detection between the SU in the SBS class; In the radio slot, the neighbours SU that the SU in the SBS class is outer with sensing results type of being broadcast to; Transfer of data is in the period, and is idle if sensing results is a frequency spectrum, then carries out transfer of data, if frequency spectrum is taken by PU, then mourns in silence and waits for the arrival of next frame.Make T _fThe total length of a basic frame of expression, we define a basic frame and are made up of perception period, radio slot and transmission period.Notice that the cluster period is every at a distance from N _f=T _c/ T _fIndividual basic frame activates once, wherein N _fRelevant with the network topology change frequency that the SU mobility causes.

As shown in Figure 2.The flow chart of method of the present invention.

1. parameter initialization:

1.1 based on historical perception information { E _i(m) | m=1 ..., M}, each SUi ∈ in the network 1 ..., N} at first obtains self observed quantity to spectrum environment:

$O_i=\frac{1}{M}\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{E}_i\left(m\right)$

E wherein _iDetected energy value when (m) being the m time perception, M are the number of times that adds up; M=100 in following embodiment; When frequency spectrum is idle, E _i(m) only comprise noise energy; When frequency spectrum takies, E _i(m) be the energy that not only comprises noise, also comprise the energy of PU signal.

1.2 Network each SUi ∈ {1, ..., N} random initialization

k ∈ {1,2} and k ∈ {1,2}, where

k ∈ {1,2} are SUi ∈ {1,. .., N} the local and non-SBS SBS initial class centroid, the centroid is the class of all the observations cognitive user weighted average,

k ∈ {1,2} are SUi ∈ {1 , ..., N} the local and non-SBS SBS type described in the initial Lagrange multipliers of the "Lagrange multiplier" is the intermediate clustering algorithm, with no specific physical significance; next described embodiment

1.3 each cognitive user SUi ∈ { 1 in the network; ..., local type of ownership coefficient

of N} initialization

$a_i^0\left(k\right)=\frac{{\left|\right|O_i-c_i^0\left(k\right)\left|\right|}^{-\frac{2}{p-1}}}{{\mathrm{\Σ}}_{k=1}^K{\left|\right|O_i-c_i^0\left(k\right)\left|\right|}^{-\frac{2}{p-1}}},\∀i\∈\{1,...,N\},\∀k\∈\left\{\mathrm{1,2}\right\}$

Wherein, p=2 is often got in p＞1 in the reality.Here

is more near 1, and then the possibility of user SUi adding type k is big more; Otherwise;

more near 0, then the possibility of user SUi adding type k is more little;

2. do not confirm SBS based on there being the supervision cluster:

Each SU at first with mutual local type of barycenter of a hop neighbor SUs, based on this interactive information, each SU upgrades local type barycenter, local Lagrange multiplier and local type of ownership coefficient successively, this process iteration is carried out, and satisfies until stopping criterion for iteration; After iteration stopped, each SU obtained local type of ownership coefficient and local type barycenter (annotate: behind iteration convergence, all SUs are identical to of a sort local type barycenter in the network, promptly reach " type barycenter common recognition ", but local type of ownership coefficient have nothing in common with each other); On this basis, each SU realizes not having the supervision cluster according to local type of ownership coefficient type of the carrying out ownership judgement that obtains;

Concrete through carrying out following distributed iterative method realization: based on t=0; 1; 2 ... local type of each SUi of barycenter

that inferior iteration obtains, i ∈ { 1; ..., N} carries out iteration the t+1 time:

2.1 each SUi ∈ 1 ..., N} is with this locality type barycenter

Be broadcast to a hop neighbor user

Here S _iBe meant the set of the hop neighbor of SUi, d _IjDistance between expression cognitive user SUi and the one of which hop neighbor user SUj, d _ComRepresent between two SU can proper communication ultimate range;

2.2 each SUi ∈ 1 ..., N} upgrades local type barycenter, obtains:

$c_i^{t+1}\left(k\right)={\left(a_i^{t+1}\left(k\right)2\mathrm{\η}\right|S_i\left|\right)}^{-1}\{a_i^{t+1}\left(k\right)O_i-2{\mathrm{\λ}}_i^t\left(k\right)+\mathrm{\η}\underset{j\∈S_i}{\mathrm{\Σ}}[c_i^t\left(k\right)+c_J^t\left(k\right)\left]\right\},\∀i\∈\{1,...,N\},k\∈\left\{\mathrm{1,2}\right\}$

Wherein, η＞0; | S _i| the hop neighbor S set of expression SUi _iThe number of middle element;

Being a local Lagrange multiplier that dynamically updates, is the middle transition variable of clustering algorithm, does not have concrete physical significance, and its update rule is according to following step 2.4.

2.3 each SUi ∈ 1 ..., N} upgrades local type of ownership coefficient, obtains:

$a_i^{t+1}\left(k\right)=\frac{{\left|\right|O_i-c_i^{t+1}\left(k\right)\left|\right|}^{-\frac{2}{p-1}}}{{\mathrm{\Σ}}_{k=1}^K{\left|\right|O_i-c_i^{t+1}\left(k\right)\left|\right|}^{-\frac{2}{p-1}}},\∀i\∈\{1,...,N\},\∀k\∈\left\{\mathrm{1,2}\right\}$

Wherein, p＞1; Often get p=2 in the reality.Here

is more near 1, and then the possibility of user SUi adding type k is big more; Otherwise;

more near 0, then the possibility of user SUi adding type k is more little;

2.4 each SUi ∈ 1 ..., N} upgrades local Lagrange multiplier, obtains:

${\mathrm{\λ}}_i^{t+1}\left(k\right)={\mathrm{\λ}}_i^t\left(k\right)+\frac{\mathrm{\η}}{2}\underset{j\∈S_i}{\mathrm{\Σ}}[c_j^{t+1}\left(k\right)-c_i^{t+1}\left(k\right)],\∀i\∈\{1,...,N\},k\∈\left\{\mathrm{1,2}\right\}$

Step 2.1-2.4 iteration is carried out, through iteration repeatedly, if condition

With

Satisfy simultaneously, then iteration stops; Wherein, ε _a, ε _cAnd ε _λBe and approach 0 positive number, often get ε in the reality _a=ε _c=ε _λ∈ [10 ^-6, 10 ^-3], value is more little, restrains slowly more, and convergence precision is high more; ε among the following embodiment _a=ε _c=ε _λ=10 ^-4If end condition does not satisfy, then rebound step 2.1; (annotate: what said end condition showed is key parameters

With

Relative increment with the increase of iterations t significant change takes place no longer.)

After 2.5 iteration stops, each SUi, i ∈ 1 ..., N} carries out this locality type ownership judgement according to following rule:

Each SUi, i ∈ 1 ..., N} confirms to get into SBS class or non-SBS class;

The SUi class that gets into k=1 then as

; And

be changed to 1,

is changed to 0;

Otherwise the SUi class that gets into k=2 then as

; And

be changed to 0, is changed to 1;

Through the class ownership judgement of step 2.5, all SU that belong to the SBS class spontaneously flock together, and constitute SBS user's collection:

Wherein,

Expression barycenter bigger class, i.e. SBS class, Expression SUi belongs to SBS class k ^◇, S _SBSRepresent that all belong to SBS class k ^◇The set of SU;

3. based on distributed cooperation frequency spectrum detection in the theoretical realization of the common recognition SBS class:

The SUs self-organizing of optimal perceived performance ground forms on the basis of SBS class in step 2, and each SU in the SBS class at first carries out local energy perception, mutual local Perspective of Energy measured value then and between the neighbours SUs; Carry out data fusion based on the common recognition agreement; Through iteration repeatedly, all SUs in the final SBS class reach the common recognition to the spectrum energy measured value, based on this common recognition; Each SU carries out this locality judgement, and it is idle or take to obtain frequency spectrum state-detection result and be frequency spectrum:

Concrete through carrying out following distributed iterative method realization: based on t=0; 1; 2 ... this locality each SUi of common recognition variable

that inferior iteration obtains, i ∈ { 1; ..., N} carries out iteration the t+1 time:

3.1 initialization: the SBS class is an optimal perceived performance cognitive user S set _SBSEach interior SUi ∈ S _SBSCarry out local energy measuring, obtain Perspective of Energy measured value E _iAnd its initial local common recognition variable is made as

3.2 each SUi ∈ S _SBSWith the one of which hop neighbor variable of knowing together alternately

Be each SUi ∈ S _SBSWith its common recognition variate-value

Be broadcast to a hop neighbor user

Receive common recognition variable simultaneously from a hop neighbor cognitive user SU

3.3 each SUi ∈ S _SBSCarry out information fusion according to following common recognition agreement:

$x_i^{t+1}=x_i^t+s_x\underset{j\∈S_i}{\mathrm{\Σ}}(x_j^t-x_i^t)$

S wherein _xThe＞0th, iteration step length is got usually

If condition

Satisfy, then iteration stops, the whole network asymptotic reaching of on average knowing together, and consensus value is asymptotic does

ε wherein _xBe to approach 0 positive number, often get ε in the reality _a=ε _c=ε _λ∈ [10 ^-6, 10 ^-3], value is more little, restrains slowly more, and convergence precision is high more; ε in following embodiment _x=10 ^-4If condition does not satisfy, then rebound step 3.2; (annotate: what said end condition showed is key parameters

Relative increment with the increase of iterations t significant change takes place no longer.)

In case 3.4 iteration stops, each SU obtains final common recognition variate-value x ^*, carry out following local judgement,

Wherein λ is the decision threshold of frequency spectrum detection, and λ detects performance working point (P corresponding to one _Fa, P _d), P _FaRefer to false alarm probability, promptly actual spectrum is idle, and court verdict is the probability that frequency spectrum takies; P _dBe detection probability, promptly actual spectrum takies the probability that court verdict also takies for frequency spectrum originally;

4. broadcasting testing result, work below accomplishing:

Each SUi ∈ S in the SBS class _SBSThe neighbours SU that testing result d type of being broadcast to is outer (being the user who belongs to non-SBS class among the neighbours of SUi), thus make the user of non-SBS class upgrade cognition for frequency spectrum free time/seizure condition, realize that the whole network SU reaches common understanding to sensing results.

Embodiment: a specific embodiment of the present invention is described as follows, and Matlab software is adopted in system emulation, and parameter setting does not influence generality.Following embodiment is that whether the free time is basic references object with a certain channel that detects the VHF/UHF frequency range, mainly confirms through the mode of energy measuring whether the PU signal is arranged on this channel.What be worth to stress is that this invention is suggested plans and also is suitable for the detection of signal on other frequency ranges.

N in the present embodiment _fBe taken as 100.Perceived bandwidth W is taken as 10MHz, and detecting period is 100 μ s.Noise power spectral density is N ₀=-174dBm, receiver noise figure are 11dB.The transmitting power of PU is made as 100mW.The path loss factor is 4, and the shadow fading standard deviation is 5.5dB, and the average of multipath fading is 1.

Shown in Fig. 3 (a), in this embodiment, we consider the square area of a 10km * 10km, and 1 PU (representing with triangle among the figure) is positioned at the center, and its coordinate is (5000,5000).36 SUs (representing with empty circles among the figure) are evenly distributed in the square area, and its coordinate is respectively:

Table 1: all SU coordinates

The SU numbering	Abscissa (m)	Ordinate (m)	The SU numbering	Abscissa (m)	Ordinate (m)
						1	2924.1	1009.503	19	2730.718	6109.955
2	4426.882	750.4901	20	4187.477	5916.674
						3	5883.987	776.6619	21	6232.208	5484.891
4	7575.978	1152.288	22	7744.184	5874.95
						5	9108.442	461.2307	23	9248.488	5779.396
6	1146.945	2742.655	24	995.6626	7222.06
						7	2241.05	2232.496	25	2604.708	7501.983
8	3833.959	2498.827	26	3906.556	7210.746
						9	5830.178	2213.956	27	5750.613	7742.598
10	7576.491	2847.238	28	7933.851	7650.508
						11	9284.362	2893.584	29	9491.021	7472.757
12	618.506	3950.469	30	1149.068	9110.859
						13	2569.737	4421.017	31	2223.543	9420.612
14	4216.669	4323.365	32	4060.52	9430.696
						15	6224.181	3982.686	33	5827.415	9308.894
16	7364.333	3776.017	34	7217.564	9498.024
						17	9151.755	4694.266	35	8961.066	9191.028
18	1051.947	5897.68	36	724.6895	1252.52

Wireless channel model (list of references: A.Goldsmith according to classics; Wireless Communications, Cambridge University Press, 2005.); Take all factors into consideration path loss, shadow fading and multipath fading parameter, can obtain following average signal-to-noise ratio:

Table 2: all SU receive average signal-to-noise ratio

The SU numbering	Average signal-to-noise ratio (dB)	The SU numbering	Average signal-to-noise ratio (dB)	The SU numbering	Average signal-to-noise ratio (dB)
						1	0.321903	13	22.15143	25	5.084683
2	6.54046	14	4.167672	26	19.49637
						3	3.433937	15	9.957042	27	-2.02053
4	0.043135	16	9.332119	28	-2.23853
						5	-9.59268	17	4.209668	29	-26.3339
6	-1.50782	18	4.860982	30	-10.4904

7	7.729074	19	19.50539	31	-3.7888
						8	4.584223	20	21.19825	32	1.16455
9	3.691429	21	17.19486	33	4.973656
						10	8.024405	22	18.884	34	-16.0538
11	-0.60406	23	-2.49563	35	2.711777
						12	14.32138	24	11.93699	36	-10.8365

Shown in (1), through M=100 historical energy measuring information { E of accumulation _i(m) | m=1 ..., M}, each SUi in the network, i ∈ 1 ..., N} obtains classification observed quantity O _iBe respectively:

Table 3: the classification observed quantity of all SU

The SU numbering	Classification observed quantity (dB)	The SU numbering	Classification observed quantity (dB)	The SU numbering	Classification observed quantity (dB)
						1	-89.9874	13	-89.6800	25	-89.9840
2	-89.9834	14	-89.9849	26	-89.8152
						3	-89.9855	15	-89.9682	27	-89.990
4	-89.9867	16	-89.9713	28	-89.9919
						5	-89.991	17	-89.9863	29	-89.9915
6	-89.9878	18	-89.9856	30	-89.9891
						7	-89.9386	19	-89.816	31	-89.9878
8	-89.9831	20	-89.7393	32	-89.9884
						9	-89.9842	21	-89.8895	33	-89.9828
10	-89.9769	22	-89.8411	34	-89.9883
						11	-89.9889	23	-89.9877	35	-89.986
12	-89.9184	24	-89.9298	36	-89.9905

With table 3 data as input; Fig. 3 (b) has provided the result of the present invention's put forward based on the distributed cluster scheme of common recognition; 7 SUs (representing with solid circles among the figure) self-organizing ground forms the SBS class among the figure, and its coordinate, classification observed quantity and average signal-to-noise ratio are as shown in table 4.Through contrast table 2, table 3 and table 4, we see: the classification observed quantity of the SUs in the SBS class is greater than the outer SUs of class, and corresponding average signal-to-noise ratio also has identical rule.Therefore, the classification observed quantity of SUs has reflected its average signal-to-noise ratio level well.

Table 4: the SU coordinate and the average signal-to-noise ratio that obtain after the cluster that the present invention suggests plans

The SU numbering	Abscissa (m)	Ordinate (m)	Classification observed quantity (dB)	Average signal-to-noise ratio (dB)
					12	618.5059621	3950.469241	-89.9184	14.32138339
13	2569.736697	4421.016728	-89.6800	22.15143138
					19	2730.718266	6109.954564	-89.816	19.50539124
20	4187.476877	5916.673989	-89.7393	21.19825018
					21	6232.208407	5484.890822	-89.8895	17.19485813
22	7744.184084	5874.949831	-89.8411	18.8839977
					26	3906.555554	7210.745656	-89.8152	19.49636712

As comparing; We have provided based on distance in Fig. 3 (c) distributed cluster scheme (is that the nearest SU of distance P U spontaneously gathers together; Notice that each SU need possess stationkeeping ability in this scheme) the result; Wherein 7 nearest SUs of distance P U form the SBS class, and its coordinate and average signal-to-noise ratio are following:.

Table 5: based on apart from the SU coordinate and the average signal-to-noise ratio that obtain after the cluster

The SU numbering	Abscissa (m)	Ordinate (m)	Average signal-to-noise ratio (dB)
				13	2569.736697	4421.016728	22.15143138
14	4216.668528	4323.365253	4.167672237
				15	6224.180544	3982.685919	9.957042449
19	2730.718266	6109.954564	19.50539124
				20	4187.476877	5916.673989	21.19825018
21	6232.208407	5484.890822	17.19485813
				26	3906.555554	7210.745656	19.49636712

The difference of Fig. 3 (b) and Fig. 3 (c) comes from: only consider large scale path loss or distance to detecting Effect on Performance based on the cluster scheme of distance, and institute suggests plans and taken all factors into consideration the influence that path loss, shadow fading and multipath Rayleigh decline.Simultaneously, contrast table 3 can find out that with table 4 except public SUs (13,19,20,21,26), the average signal-to-noise ratio of the SU that the cluster of suggesting plans obtains is higher than the average signal-to-noise ratio that obtains based on distance.

The detection performance that has compared different schemes among Fig. 4.Wherein, transverse axis is represented false alarm probability (mistaken verdict is the probability of " frequency spectrum takies " under the situation of " frequency spectrum is idle "), and the longitudinal axis is represented detection probability (correct judgement is the probability of " frequency spectrum takies " under the situation of " frequency spectrum takies ").We can see among the figure, and under the situation of given false alarm probability, the detection performance of being suggested plans obviously is superior to traditional equal gain combining (Equal Gain Combination; EGC) scheme (list of references: S.P. Herath and N.Raj atheva, " Analysis of equal gain combining in energy detection for cognitive radio over Nakagami channels, " in Proc.IEEE GLOBECOM; Nov.2008.) with based on the cluster scheme (list of references: Amy C.Malady and Claudio R.C.M.da Silva of distance; " Clustering methods for distributed spectrum sensing in cognitive radio systems, " in Proc.IEEE GLOBECOM, Nov.2008.); Suggest plans simultaneously and obtained and the soft merging of optimum linearity (Optimal Soft Combination; OSC) scheme (list of references: J.Ma, G. Zhao, and G. Li; " Soft combination and detection for cooperative spectrum sensing in cognitive radio networks; " IEEE Transactions on Wireless Communications, vol.7, no.11; Pp.4502-4507, Nov.2008.) close performance.Notice that than the OSC scheme, the advantage of being suggested plans is: each SU need not carry out the estimation of himself instantaneous received signal to noise ratio in the network, and institute suggests plans simultaneously only needs part SU to participate in cooperation, and the information interaction amount reduces greatly.

The present invention does not relate to all identical with the prior art prior art that maybe can adopt of part and realizes.

Claims (5)

1. In the cognitive self-organizing network based on the distributed cooperation frequency spectrum sensing method that does not have the supervision cluster; It is characterized in that: under the situation of no control centre; At first confirm to have the cognitive user S set BS of optimal perceived performance through unsupervised clustering; Detect based on the cooperation frequency spectrum between a plurality of cognitive user SUs in the theoretical SBS of realization of common recognition then, obtain the probability that corresponding frequency spectrum takies, utilize broadcast mechanism that testing result is informed outer a plurality of cognitive user SUs to SBS at last.
2. 2. based on the distributed cooperation frequency spectrum sensing method that does not have the supervision cluster, it is characterized in that it may further comprise the steps in the cognitive self-organizing network according to claim 1:

   Step 1. parameter initialization:

   Each SU at first obtains self observed quantity to environment, and random initializtion self is to the local type barycenter and the local Lagrange multiplier of SBS class and non-SBS class then, and on this basis, each SU initialization belongs to coefficient for local type;

   The weighted average of the environmental observation amount of all cognitive user in described " type barycenter " type of being meant;

   The middle transition variable that described " Lagrange multiplier " is clustering algorithm does not have concrete physical significance;

   Described " type ownership coefficient " is to characterize it to belong to the possibility of SBS class and non-SBS class;

   Step 2. is confirmed SBS based on unsupervised clustering:

   Each SU at first with mutual local type of barycenter of a hop neighbor SUs, based on this interactive information, each SU upgrades local type barycenter, local Lagrange multiplier and local type of ownership coefficient successively, this process iteration is carried out, and satisfies until stopping criterion for iteration;

   After algorithm iteration stops; The local type barycenter of all SUs will be tending towards identical in the network; Promptly reach " all SU class barycenter common recognitions ", but the local type of ownership coefficient that each SU obtains has nothing in common with each other, based on this; Each SU realizes not having the supervision cluster according to local type of ownership coefficient type of the carrying out ownership judgement of self;

   Step 3. realizes distributed cooperation frequency spectrum detection in the SBS class based on the common recognition theory:

   Each SU in the SBS class at first carries out local energy perception, and mutual local Perspective of Energy measured value then and between the neighbours SUs carries out data fusion based on the common recognition agreement, and through iteration repeatedly, all SUs in the final SBS class reach the common recognition to the spectrum energy measured value;

   Based on the Perspective of Energy measured value of common recognition, each SU carries out this locality judgement, and to obtain frequency spectrum state-detection result be the frequency spectrum free time or take;

   Step 4. broadcasting testing result:

   The neighbours SU that each SU of SBS class is outer with testing result type of being broadcast to realizes that the whole network SU reaches common understanding to sensing results.
3. 3. based on the distributed cooperation frequency spectrum sensing method that does not have the supervision cluster, it is characterized in that described unsupervised clustering may further comprise the steps in the cognitive self-organizing network according to claim 1 and 2:

   Step 1. parameter initialization:

   1.1 based on historical perception information { E _i(m) | m=1 ..., M}, each SUi ∈ in the network 1 ..., N} at first obtains self observed quantity to spectrum environment:

   $O_i=\frac{1}{M}\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{E}_i\left(m\right)$

   E wherein _iDetected energy value when (m) being the m time perception, M are the number of times that adds up;

   1.2 each SUi ∈ { 1 in the network; ...; N} random initializtion k ∈ { 1; 2} and

   

   k ∈ { 1; 2};

   

   k ∈ { 1 wherein; 2} is respectively SUi ∈ { 1; ..., the SBS class that N} is local and the initial barycenter of non-SBS class, the weighted average of the observed quantity of all cognitive user in described barycenter type of being meant;

   

   k ∈ { 1; 2} be respectively SUi ∈ 1 ..., the SBS class that N} is local and the initial Lagrange multiplier of non-SBS class;

   1.3 each cognitive user SUi ∈ { 1 in the network; ..., local type of ownership coefficient of N} initialization

   $a_i^0\left(k\right)=\frac{{\left|\right|O_i-c_i^0\left(k\right)\left|\right|}^{-\frac{2}{p-1}}}{{\mathrm{\Σ}}_{k=1}^K{\left|\right|O_i-c_i^0\left(k\right)\left|\right|}^{-\frac{2}{p-1}}},\∀i\∈\{1,...,N\},\∀k\∈\left\{\mathrm{1,2}\right\}$

   Wherein, p＞1, here $a_i^0\left(k\right)\∈\left[\mathrm{0,1}\right],\∀i\∈\{1,...,N\},\∀k\∈\left\{\mathrm{1,2}\right\};$

   Step 2. is not confirmed SBS based on there being the supervision cluster:

   Each SU at first with mutual local type of barycenter of a hop neighbor SUs, based on this interactive information, each SU upgrades local type barycenter, local Lagrange multiplier and local type of ownership coefficient successively, this process iteration is carried out, and satisfies until stopping criterion for iteration; After iteration stopped, each SU obtained local type of ownership coefficient and local type barycenter; On this basis, each SU realizes not having the supervision cluster according to local type of ownership coefficient type of the carrying out ownership judgement that obtains;

   Concrete through carrying out following distributed iterative method realization: based on t=0; 1; 2 ... local type of each SUi of barycenter

   

   that inferior iteration obtains, i ∈ { 1; ..., N} carries out iteration the t+1 time:

   2.1 each SUi ∈ 1 ..., N} is with this locality type barycenter Be broadcast to a hop neighbor user

   

   Here S _iBe meant the set of the hop neighbor of SUi, d _IjDistance between expression cognitive user SUi and the one of which hop neighbor user SUj, d _ComRepresent between two SU can proper communication ultimate range;

   2.2 each SUi, i ∈ 1 ..., N} upgrades local type barycenter after receiving all hop neighbor users' local type barycenter, obtains:

   $c_i^{t+1}\left(k\right)={\left(a_i^{t+1}\left(k\right)2\mathrm{\η}\right|S_i\left|\right)}^{-1}\{a_i^{t+1}\left(k\right)O_i-2{\mathrm{\λ}}_i^t\left(k\right)+\mathrm{\η}\underset{j\∈S_i}{\mathrm{\Σ}}[c_i^t\left(k\right)+c_J^t\left(k\right)\left]\right\},\∀i\∈\{1,...,N\},k\∈\left\{\mathrm{1,2}\right\}$

   Wherein, η＞0; | S _i| the hop neighbor S set of expression SUi _iThe number of middle element;

   

   Be a local Lagrange multiplier that dynamically updates, its update rule is according to following step 2.4;

   2.3 each SUi ∈ 1 ..., N} upgrades local type of ownership coefficient, obtains:

   $a_i^{t+1}\left(k\right)=\frac{{\left|\right|O_i-c_i^{t+1}\left(k\right)\left|\right|}^{-\frac{2}{p-1}}}{{\mathrm{\Σ}}_{k=1}^K{\left|\right|O_i-c_i^{t+1}\left(k\right)\left|\right|}^{-\frac{2}{p-1}}},\∀i\∈\{1,...,N\},\∀k\∈\left\{\mathrm{1,2}\right\}$

   Wherein, p＞1;

   2.4 each SUi ∈ 1 ..., N} upgrades local Lagrange multiplier, obtains:

   ${\mathrm{\λ}}_i^{t+1}\left(k\right)={\mathrm{\λ}}_i^t\left(k\right)+\frac{\mathrm{\η}}{2}\underset{j\∈S_i}{\mathrm{\Σ}}[c_j^{t+1}\left(k\right)-c_i^{t+1}\left(k\right)],\∀i\∈\{1,...,N\},k\∈\left\{\mathrm{1,2}\right\}$

   Step 2.1-2.4 iteration is carried out, through iteration repeatedly, if condition

   

   

   With

   

   Satisfy simultaneously, then iteration stops; Wherein, ε _a, ε _cAnd ε _λBe and approach 0 positive number, often get ε in the reality _a=ε _c=ε _λ∈ [10 ^-6, 10 ^-3], if end condition does not satisfy, then rebound step 2.1 satisfies as if condition, then iteration stops;

   After 2.5 iteration stops, each SUi, i ∈ 1 ..., N} carries out this locality type ownership judgement according to following rule:

   Each SUi, i ∈ 1 ..., N} confirms to get into SBS class or non-SBS class;

   The SUi class that gets into k=1 then as

   

   ; And

   

   be changed to 1,

   

   is changed to 0;

   Otherwise the SUi class that gets into k=2 then as ; And

   

   be changed to 0, is changed to 1;

   Through the class ownership judgement of step 2.5, all SU that belong to the SBS class spontaneously flock together, and constitute SBS user's collection:

   

   Wherein, Expression barycenter bigger class, i.e. SBS class, Expression SUi belongs to SBS class k ^◇, S _SBSRepresent that all belong to SBS class k ^◇The set of SU;

   Step 3. realizes distributed cooperation frequency spectrum detection in the SBS class based on the common recognition theory:

   The SUs self-organizing of optimal perceived performance ground forms on the basis of SBS class in step 2, and each SU in the SBS class at first carries out local energy perception, mutual local Perspective of Energy measured value then and between the neighbours SUs; Carry out data fusion based on the common recognition agreement; Through iteration repeatedly, all SUs in the final SBS class reach the common recognition to the spectrum energy measured value, based on this common recognition; Each SU carries out this locality judgement, and it is idle or take to obtain frequency spectrum state-detection result and be frequency spectrum:

   Concrete through carrying out following distributed iterative method realization: based on t=0; 1; 2 ... this locality each SUi of common recognition variable

   

   that inferior iteration obtains, i ∈ { 1; ..., N} carries out iteration the t+1 time:

   3.1 initialization: the SBS class is an optimal perceived performance cognitive user S set _SBSEach interior SUi ∈ S _SBSCarry out local energy measuring, obtain Perspective of Energy measured value E _iAnd its initial local common recognition variable is made as

   

   3.2 each SUi ∈ S _SBSWith the one of which hop neighbor variable of knowing together alternately

   

   Be each SUi ∈ S _SBSWith its common recognition variate-value

   

   Be broadcast to a hop neighbor user

   

   Receive common recognition variable simultaneously from a hop neighbor cognitive user SU

   3.3 each SUi, i ∈ 1 ..., and after N} receives all hop neighbor users' common recognition variable, each SUi ∈ S _SBSCarry out information fusion according to following common recognition agreement:

   $x_i^{t+1}=x_i^t+s_x\underset{j\∈S_i}{\mathrm{\Σ}}(x_j^t-x_i^t)$

   S wherein _xThe＞0th, iteration step length is got usually

   

   If condition

   

   Satisfy, then iteration stops, the whole network asymptotic reaching of on average knowing together, and consensus value is asymptotic does

   

   ε wherein _xBe to approach 0 positive number, often get ε in the reality _a=ε _c=ε _λ∈ [10 ^-6, 10 ^-3], if condition does not satisfy, then rebound step 3.2 satisfies as if condition, then iteration stops;

   In case 3.4 iteration stops, each SU obtains final common recognition variate-value x ^*, carry out following local judgement,

   

   Wherein λ is the decision threshold of frequency spectrum detection, and λ detects performance working point (P corresponding to one _Fa, P _d), P _FaRefer to false alarm probability, promptly actual spectrum is idle, and court verdict is the probability that frequency spectrum takies; P _dBe detection probability, promptly actual spectrum takies the probability that court verdict also takies for frequency spectrum originally;

   Step 4. broadcasting testing result, work below accomplishing:

   Each SUi ∈ S in the SBS class _SBSThe neighbours SU that testing result d type of being broadcast to is outer belongs to the user of non-SBS class among the neighbours of SUi, thereby makes the user of non-SBS class upgrade the cognition for frequency spectrum free time/seizure condition, realizes that the whole network SU reaches common understanding to sensing results.
4. 4. based on the distributed cooperation frequency spectrum sensing method that does not have the supervision cluster, it is characterized in that in the described step 1 in the cognitive self-organizing network according to claim 2, when frequency spectrum is idle, E _i(m) only comprise noise energy; When frequency spectrum takies, E _i(m) be the energy that not only comprises noise, also comprise the energy of PU signal.
5. 5. based on the distributed cooperation frequency spectrum sensing method that does not have the supervision cluster, it is characterized in that described p=2 in the cognitive self-organizing network according to claim 3.

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