CN103200670A - Cognitive radio master user location method based on backtracking check convex set projection - Google Patents
Cognitive radio master user location method based on backtracking check convex set projection Download PDFInfo
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Abstract
The invention provides a cognitive radio master user location method based on backtracking check convex set projection. L coordinates for sensing a user are regarded as a circle center, a convex set projection algorithm is used for carrying out Mc step rectangular projection iteration on the a master user; mc step backtracking comparing checking is carried out, the distances between adjacent iteration points are computed and are compared with a threshold value lambada; if parts of the distances between the adjacent iteration points are zero or larger than lambada, Mb step projection iteration and mb step backtracking comparing checking on the border of a convex set circular region are carried out, distance values between two adjacent iteration points are computed and are compared with the threshold value lambada again, and if the distance values are all smaller than lambada, the Mb step iteration result bMb is determined as a locating result of position information of the master user. According to the backtracking check convex set projection location algorithm, shortcomings of an existing convex set projection location algorithm are overcome, the location algorithm is good, influence from distance-measuring errors is small, and the method is suitable for a link in which a sensing user acquires the location information of the master user in a cognitive radio network.
Description
Technical field
The present invention relates in a kind of cognitive radio networks the method to main user location, particularly a kind of based on the convex set projection localization method of recalling inspection.
Background technology
More and more ripe in conjunction with application development along with wireless mobile communications and computer network, mobile Internet have been called that the world today is with the fastest developing speed, market potential is maximum, one of development service that commercial value is the highest.Abundant application mainly relies on information bearing modes such as literal, image, video, and along with people to using the continuous pursuit of quality, require the transmission of information more and more efficient, convenient.The innovation of these application and development need broad spectrum and higher download speed.The concept of cognitive radio has been catered to the needs of frequency spectrum recycling, can realize by the perception to wireless environment avoiding with conflicting of main user, utilizes optimized decision-making effectively dynamically to utilize frequency spectrum cavity-pocket.If can obtain main user position information, the performance to frequency spectrum perception will be greatly improved so, and the management of follow-up frequency spectrum resource with distribute in also will play very big help.
In cognition network main user is positioned, the main effect of obtaining main user position information has the following aspects:
1. provide support for the frequency spectrum resource management.Under the known situation of main customer position information, can improve the availability of frequency spectrum better according to its positional information, instruct better the perception user not the frequency spectrum of interfere with primary users use.
2. reduce the power consumption of user in the cognition network.Under the known situation of main customer position information, the perception user in the cognition network can determine the direction of frequency spectrum perception according to main user position information, under the running status of minimum power, just can accurately judge main user's frequency spectrum operating position.
3. avoid the interference to main user.Under the known situation of main customer position information, can carry out frequency spectrum perception at main user's direction position in conjunction with multi-antenna technology, avoided the possibility of phase mutual interference between frequency spectrum.
4. the position optimization that is conducive to the perception user.Under the known situation of main customer position information, according to main user position information, frequency spectrum and utilization of space are improved in the perception user's that can reasonably distribute position, avoid better main user is disturbed.
Convex set projecting method commonly used at present comprises Circular POCS, Hyperbolic POCS, Boundary POCS and Hybrid POCS etc., wherein Hybrid POCS is the merging of preceding two kinds of POCS methods, show according to result of study, the positioning accuracy of Hybrid POCS method is better than preceding several method, yet, as main user during away from the perception user, because the hyperbolic projections location is subjected to noise fluctuations bigger for main user convergence point under the situation outside the perception user polygon in the Hybrid POCS algorithm, so error increases and increases along with range finding.
Summary of the invention
The present invention is intended to solve above-mentioned technological deficiency, proposes a kind of be applied in the cognition network and recalls convex set projection algorithm (BackCheck POCS) to what main user positioned.
This method may further comprise the steps:
Step 2. to Mc the iteration point that obtains in the step 1, carry out the mc step and recall audit by comparison, calculate the distance between the adjacent iteration point || x
M+1-x
m||, wherein, m=Mc-1 ..., Mc-mc
Step 3. if recalling in the audit by comparison in the step 2, the distance between the adjacent iteration point be all less than λ and non-vanishing, then with last L iteration average in the step 1 as the positioning result of main customer position information; If it is zero or greater than the situation of λ that there are part in recalling in the audit by comparison in the step 2, the distance between the adjacent iteration point, continue execution in step four;
Step 4. go on foot iteration x as a result with Mc
McBe initial point b
0, carry out rectangular projection iteration on the border, convex set circle territory, iteration checks that step number is Mb, obtains Mb iteration point b
h, h=1 wherein, 2,3 ... Mb;
Step 6. if recalling in the audit by comparison in the step 5, the distance value between adjacent iteration point be all less than λ, then with last L iteration average in the step 4 as the positioning result of main customer position information; If recalling in the audit by comparison in the step 5, there is the situation greater than λ in the distance value between adjacent iteration point, jumps to step 4 and goes on foot iteration b as a result with Mb
MbBe initial point b
0, and conversion projection iteration order, the distance value up between adjacent iteration point is all less than λ.
Preferably, described step 1 comprises:
1.1) initialization step: initial point x is set
0, x wherein
0For on the optional position a bit;
1.2) utilize following formula to carry out the projection iteration:
Wherein,
Expression quadrature convex set subpoint,
Expression P
iTo P
I+1Vector; P
iBe i perception user's position coordinates, i ∈ [1, L].D
iFor being the center of circle with i perception user, record with i perception user and main user between distance measure be the convex set circle territory of radius.
Preferably, the value size of λ depends on the perception user to find range result's mean value of main user, and λ is a very little value with respect to this average distance.
Preferably, mb is identical with the mc value, is the integral multiple of L.
Preferably, described step 4 comprises:
4.1) initialization step: 1) initial point b is set
0, b
0=x
Mc
4.2) utilize following formula to carry out the projection iteration:
b
h+1=P
hmodL(b
h),h=0,1,2,3…Mb-1
Wherein,
Wherein, P
iBe i perception user's position coordinates, i ∈ [1, L].d
iBe that i perception user records and main user between distance measure; C
i={ y ∈ R
2: || y-P
i||=d
iBe that i the determined radius of perception user is d
iRound edge circle.
This algorithm is based on the improvement of convex set projection location algorithm, remedied the deficiency of existing convex set projection location algorithm, tested influence apart from error is less relatively, be fit to be applied to that the perception user can realize the location to main user more accurately to the link of obtaining of main customer position information in the cognitive radio networks.
Description of drawings
Fig. 1 among the present invention based on the flow chart of BackCheck POCS localization method.
Fig. 2 is the iteration schematic diagram that the present invention is based on BackCheck POCS localization method.
Fig. 3 is the position error contrast schematic diagram of BackCheck POCS and Hybrid POCS.
Fig. 4 is that different range errors are to the contrast schematic diagram of BackCheck POCS and the influence of Hybrid POCS position error.
Be exemplary below by the embodiment that is described with reference to the drawings, only be used for explaining the present invention, and can not be interpreted as limitation of the present invention.
Suppose to have L perception user participation to main user's location, L perception user's position coordinates is known, is expressed as:
Distance measure between that L perception user records and the main user is expressed as:
Be the center of circle with each perception user, with distance measure d
iFor the round domain representation of the convex set of radius is:
I the determined radius of perception user is d
iRound edge circle be expressed as follows:
C
i={y∈R
2:||y-P
i||=d
i}
The used POCS algorithm of localization method of the present invention can be Circular POCS, Hyperbolic POCS and Boundary POCS.Be example with Circular POCS algorithm now, provide the concrete steps based on BackCheck POCS algorithm:
Check that in this iteration of setting Circular POCS step number is Mc, because the convergence rate of Circular POCS is very fast, the value of Mc can suitably be got a smaller value.
According to the projection rule of iteration of Circular POCS, the step of main user being carried out Mc step rectangular projection iteration is:
1) initialization: initial point x is set
0, x wherein
0For in the plane more arbitrarily, shown in square among Fig. 2;
2) the rectangular projection iteration of the circular POCS that simplifies:
Wherein
Expression quadrature convex set subpoint,
Expression P
iTo P
I+1Vector, can express the order of iteration by i, this is to determine according to perception user's order in proper order; When successive iterations enters endless loop or does not restrain, can change the order of iteration, continue iteration.
Step 2. to Mc the iteration point that obtains in the step 1, carry out the mc step and recall inspection.Calculate the distance between the adjacent iteration point || x
M+1-x
m||, wherein, m=Mc-1 ..., Mc-mc, and compare with threshold value λ.Wherein, the value size of λ depends on that the perception user is to find range result's mean value of main user, and λ is a very little value with respect to this average distance, for example: the ratio of λ and this average distance is smaller or equal to 0.02, consider the computational complexity of algorithm, this ratio further can be limited in 0.005 ~ 0.02.
Recalling the inspection step number is mc, and the value of this mc is the integral multiple of the perception user's of participation location number L.
If step 3. recalling in the audit by comparison in the step 2, distance between the adjacent iteration point is all less than λ and non-vanishing, can judge that then main user is positioned within the formed polygon of perception user, with last L iteration average in the step 1, the i.e. average of the last iteration of each convex set under the perception user
Positioning result as main user position information; If it is zero or greater than the situation of λ that there are part in recalling in the audit by comparison in the step 2, the distance between the adjacent iteration point, continue execution in step four.
Step 4. go on foot iteration x as a result with Mc
McBe initial point b
0, carry out rectangular projection iteration on the border, convex set circle territory, iteration checks that step number is Mb, obtains Mb iteration point.
The inspection step number of setting border rectangular projection iteration is Mb, owing to cancelled by the judgement of iteration point position, the convergence rate of border rectangular projection iteration is uncertain, very rapid convergence is near the main user, also may be absorbed in slow circulation, therefore Mb gets higher value, makes border rectangular projection iteration abundant.
Wherein, according to the rule of border rectangular projection iteration, the iterative step that main user is carried out the Mb step is:
1) initial point b is set
0, b
0=x
Mc
2)b
h+1=P
hmodL(b
h),h=0,1,2,3...Mb-1
Wherein,
Recalling the inspection step number is mb, and this mb value is identical with mc, is the integral multiple of L.
Step 6. if recalling in the audit by comparison in the step 5, the distance value between adjacent iteration point are all less than λ, then with last L iteration average in the step 4, the i.e. average of the last iteration of each convex set under the perception user
Positioning result as main user position information; If recalling in the audit by comparison in the step 5, there is the situation greater than λ in distance value between adjacent iteration point, illustrate that border rectangular projection having passed through after Mb goes on foot abundant iteration, does not still converge near the main customer location, but has been absorbed in slow loop iteration.At this moment, jump procedure four continues to carry out the borderline rectangular projection iteration in convex set circle territories, wherein, and with the b as a result of Mb step iteration in the Mb step rectangular projection iteration last time
MbBe initial point b
0, the original projection iteration order of conversion, the distance value up between adjacent iteration point is all less than λ.
Come below in conjunction with accompanying drawing and concrete example that the present invention is described in further detail.
Setting the perception number of users is L=3, perception user's position coordinates be [(700m1500m), (500m1000m), (1000m, 1000m)].Wherein, input white noise obtain as the perception user and main user between the measured value d of distance
i, variance is 8m.Set the boss collection projection iteration step number Mc=10 that reaches the standard grade, with coordinate position x
0=(1600m 2100m) carries out the projection iteration for original position (shown in Fig. 2 square), obtains 10 iteration point x
k, k=1,2,3 ... 10.
Step 2. to 10 iteration points that obtain in the step 1, carry out the mc step and recall inspection, recalling the inspection step number is mc=2L=6.Calculate the distance between the adjacent iteration point, and compare with threshold value λ.The mean value of supposing the measured value of distance between that all perception users that participate in the location obtain and the main user is R, and then inspection door limit value λ is set at one of relative R in a small amount, and the ratio of setting λ and range averaging value here is λ/R=0.01.
|| x
M+1-x
m||≤λ, wherein, m=Mc-1 ..., Mc-mc
Step 3. because recalling in the audit by comparison in the step 2, it is zero or greater than the situation of λ that there is part in the distance between the adjacent iteration point, continues execution in step four.
As can be seen from Figure 2, from initial point x
0Begin the iteration through two step Circular POCS, iteration point is just stagnated on the intersection area in three convex set circle territories, check the result that judge according to recalling this moment, it is zero situation that there is a part in the variation difference of iteration point, thereby continue execution in step four, carry out to the borderline rectangular projection iteration in convex set circle territory.
Step 4. go on foot iteration x as a result with Mc
McBe initial point, carry out the borderline rectangular projection iteration in convex set circle territory main user is carried out Mb step rectangular projection iteration, obtain Mb iteration point b
h, h=1,2,3 ... Mb.
At first, set border rectangular projection iteration step number upper limit Mb=30, owing to cancelled by the judgement of iteration point position, the convergence rate of border rectangular projection iteration is uncertain, very rapid convergence is near the main user, also may be absorbed in slow circulation, so Mb gets higher value, make border rectangular projection iteration abundant; Afterwards, according to the rule of border rectangular projection iteration, main user is carried out Mb step positioning projection, obtain 10 iteration point b
h, h=1,2,3 ... 30.
Step 6. in iteration after Mb step, recall and check the mb=6 iteration point changing value in step, find that there is the situation greater than λ in distance value between adjacent iteration point, the projection iteration has been absorbed in slow circulation projection iteration, therefore, need jump to step 4, and the iteration result who goes on foot with Mb in the iteration of Mb step rectangular projection first is initial point b
0Carry out the borderline Mb step rectangular projection in convex set circle territory iteration again, conversion this moment projection iteration order first, change the order of former projection iteration into P2-P3-P1 by P1-P2-P3, through Mb step iteration, recall the inspection mb=6 step when again, find that the changing value of iteration point is less than thresholding λ, illustrate near the iteration convergence master customer location that therefore last L iteration average (shown in Fig. 2 asterisk) determined main customer location in the iteration of Mb step rectangular projection for the second time.
Fig. 3 is the location simulation comparison diagram as a result of Hybrid POCS location algorithm and BackCheck POCS location algorithm.Abscissa is the emulation number of repetition among the figure, and ordinate is that difference between estimated position and the target actual position and perception user are to the ratio of actual distance mean value between the main user.As can be seen from Figure 3, the positioning accuracy of two kinds of algorithms is more approaching generally speaking, but in some cases, the positioning accuracy of BackCheck POCS location algorithm relatively has superiority.This be because, as main user during away from the perception user, it is apparent in view that hyp asymptote character causes hyp intersection point to be subjected to the influence of fluctuations of distance measuring noises easily, and therefore in this case, the locating effect of BackCheck POCS algorithm is more superior than Hybrid POCS algorithm as can be seen.
Fig. 4 has described Hybrid POCS location algorithm and BackCheck POCS location algorithm under different range errors influences, the comparison of positioning accuracy.As can be seen from Figure 4, BackCheck POCS location algorithm has certain advantage than Hybrid POCS location algorithm, this mainly be since in the Hybrid POCS algorithm hyperbolic projections location be subjected to noise fluctuations bigger for main user convergence point under the situation outside the perception user polygon, therefore along with the increase of range error.
Although illustrated and described embodiments of the invention, for the ordinary skill in the art, be appreciated that without departing from the principles and spirit of the present invention and can carry out multiple variation, modification, replacement and modification to these embodiment that scope of the present invention is by claims and be equal to and limit.
Claims (5)
- In the cognitive radio networks to the method for main user location, it is characterized in that this method may further comprise the steps:Step 1, be the center of circle with L perception user's coordinate, utilize the convex set projection algorithm that main user is carried out Mc step rectangular projection iteration, obtain Mc iteration point x K, k=1 wherein, 2,3 ..., Mc;Step 2. to Mc the iteration point that obtains in the step 1, carry out the mc step and recall audit by comparison, calculate the distance between the adjacent iteration point || x M+1-x m||, wherein, m=Mc-1 ..., Mc-mc, and compare with threshold value λ;Step 3. if recalling in the audit by comparison in the step 2, the distance between the adjacent iteration point be all less than λ and non-vanishing, then with last L iteration average in the step 1 as the positioning result of main customer position information; If it is zero or greater than the situation of λ that there are part in recalling in the audit by comparison in the step 2, the distance between the adjacent iteration point, continue execution in step four;Step 4. go on foot iteration x as a result with Mc McBe initial point b 0, carry out rectangular projection iteration on the border, convex set circle territory, iteration checks that step number is Mb, obtains Mb iteration point b h, h=1 wherein, 2,3 ..., Mb;Step 5. to Mb the iteration point that obtains in the step 4, carry out the mb step and recall audit by comparison, calculate the distance value between adjacent two iteration points || b N+1-b n||, wherein, n=Mb-1 ..., Mb-mb, and compare with threshold value λ;Step 6. if recalling in the audit by comparison in the step 5, the distance value between adjacent iteration point be all less than λ, then with last L iteration average in the step 4 as the positioning result of main customer position information; If recalling in the audit by comparison in the step 5, there is the situation greater than λ in the distance value between adjacent iteration point, jumps to step 4 and goes on foot iteration b as a result with Mb MbBe initial point b 0, and conversion projection iteration order, the distance value up between adjacent iteration point is all less than λ.
- In the cognitive radio networks as claimed in claim 1 to the method for main user location, it is characterized in that described step 1 comprises:1.1) initialization step: initial point x is set 0, x wherein 0For on the optional position a bit;1.2) utilize following formula to carry out the projection iteration:Wherein, Expression quadrature convex set subpoint, Expression P iTo P I+1Vector; P iBe i perception user's position coordinates, i ∈ [1, L].D iFor being the center of circle with i perception user, record with i perception user and main user between distance measure be the convex set circle territory of radius.
- In the cognitive radio networks as claimed in claim 1 to the method for main user location, it is characterized in that the value size of λ depends on the perception user to find range result's mean value of main user, and λ is a very little value with respect to this average distance.
- 4. the method for in the cognitive radio networks as claimed in claim 1 main user being located is characterized in that mb is identical with the mc value, is the integral multiple of L.
- In the cognitive radio networks as claimed in claim 1 to the method for main user location, it is characterized in that described step 4 comprises:4.1) initialization step: 1) initial point b is set 0, b 0=x Mc4.2) utilize following formula to carry out the projection iteration:b h+1=P hmodL(b h),h=0,1,2,3...Mb-1Wherein,Wherein, P iBe i perception user's position coordinates, i ∈ [1, L].d iBe that i perception user records and main user between distance measure; C i={ y ∈ R 2: || y-P i||=d iBe that i the determined radius of perception user is d iRound edge circle.
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