CN104010288A - Optimal power control method based on pricing in cognitive network - Google Patents

Optimal power control method based on pricing in cognitive network Download PDF

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CN104010288A
CN104010288A CN201410219921.7A CN201410219921A CN104010288A CN 104010288 A CN104010288 A CN 104010288A CN 201410219921 A CN201410219921 A CN 201410219921A CN 104010288 A CN104010288 A CN 104010288A
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optimal power
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CN104010288B (en
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王正强
蒋铃鸽
何晨
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Shanghai Jiaotong University
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Abstract

The invention provides an optimal power control method based on pricing in a cognitive network. The optimal power control method comprises the following steps: initiating the number of secondary users and tolerance factors; conducting descending order on the secondary users according to the tolerance factors of the secondary users; calculating judging factors of the secondary users; determining the magnitude relation of the judging factors and the tolerance factors, if the condition is met, giving the optimal power of the secondary users and a pricing method of a primary user, and completing the method; otherwise, setting the power of the secondary user with the lowest tolerance factor into zero, continuously executing operations on the other secondary users according to the fourth step, and giving the optimal power of the secondary users and the pricing method of the primary user. According to the optimal power control method based on pricing in the cognitive network, under the conditions that the primary user knows the tolerance factors of the secondary users and channel information of the whole cognitive network, a closed-form solution of the optimal power distribution of the secondary users and the optimal price of the primary user can be given through iteration with the frequencies not exceeding the total number of the secondary users.

Description

Optimal power control method based on price in cognition network
Technical field
The present invention relates to power control techniques field in cognition network, particularly, relate to the optimal power control method based on price in cognition network.
Background technology
Under cognition network, inferior user can, by leasing primary user's frequency spectrum, access frequency spectrum and carry out transfer of data.In order to ensure the service quality of self and to obtain the motivation that frequency spectrum is leased, primary user can charge by the interference to inferior user.First, primary user formulates certain price for each user's unit interference power, by this price being broadcast to each time to user; Inferior user controls the power of self by non-cooperative game based on this price.This interbehavior between primary user and inferior user can be analyzed by Stackelberg game.Because primary user's price will affect time user's power, thus the income of impact oneself.Therefore,, in order to ensure that time user is less than interference threshold for total interference of primary user and maximizes self income, primary user need to adopt rational pricing strategy to control time user's power.By the method for recurrence, the function of primary user's revenue function can be expressed as the non-protruding majorized function of time user power, and therefore, traditional convex optimized algorithm can not directly be used for finding primary user's optimal pricing and time user's optimal power.
In recent years, the power of controlling time user based on pricing method in cognition network is studied and is just received increasing concern.Existing literature search is found, pertinent literature is as follows:
The people such as Hui Yu are at " 2010IEEE Transactions on Vehicular Technology, May.2010, vol.59, no.4, pp.1769 – 1778. " on delivered the article that is entitled as " Pricing for uplink power control in cognitive radio networks ".This article is considered primary user and time all situations in a serving BS of user, and under certain interference threshold, inferior user can be by paying and allow access base station primary user's interference.Because primary user's maximum utility function right and wrong are protruding, article is set primary user's strategy for certain linear ratio relation, and the method obtains the pricing algorithm of a suboptimum.Meanwhile, in system model, the base station of primary user and time user's access is same base station, and the base station that can not directly be extended to time user and primary user is different situation.
The people such as Xin Kang are at " 2012IEEE Journal on Selected Areas in Communications, Apr.vol.30, no.3, pp.538 – 549. " on delivered the article that is entitled as " Price-based resource allocation for spectrum-sharing femtocell networks:A stackelberg game approach ".The Model Extension that this article has proposed the people's such as Hui Yu model, primary user and time user's base station can be different.The income that a kind of incomparable inconsistent pricing model carrys out maximum primary user has been proposed.Because this algorithm is by the interference of inferior user's worst case being carried out to the phase mutual interference between decoupling zero time user, thereby neglect the mutual interfering link between time user, the Poewr control method that this pricing method obtains is suboptimum, can not maximize primary user's income.
From correlative study, in order to maximize primary user's income, ensure that total interference of time user is less than interference threshold simultaneously, need primary user to adopt certain price to control time user's power to each user.The present invention is based on by variable and replace the non-convex function of primary user's utility function is expressed as to a protruding optimization problem of equivalence, based on this equivalence optimization problem, propose the optimal power control method of the income that maximizes primary user.
Summary of the invention
For defect of the prior art, the object of this invention is to provide the optimal power control method based on price in a kind of cognition network.The present invention is directed to the Poewr control method based on price in existing cognition network and also can obtain time user's optimal power and maximize primary user's income, provide a kind of Poewr control method of optimum to maximize primary user's income.The present invention can make primary user in the case of the channel information and the preference factor of knowing time user, by finding inferior user's optimal power and primary user's optimal pricing to the iteration for several times of user repeatedly.This pricing method is ensureing that time user is less than for primary user's interference under the prerequisite of given interference threshold, optimal solution and the variable of the protruding optimization problem of equivalence based on maximizing primary user's income replace it a relation, find time user's optimal power, improve primary user's income than tradition based on incomparable inconsistent pricing method, and can allow that more times user accesses frequency spectrum.
According to the optimal power control method based on price in a kind of cognition network provided by the invention, comprise following concrete steps:
The first step: initialization time user's number and time user's tolerance factor;
Second step: by the descending of inferior user's tolerance factor;
The 3rd step: the judgement factor of calculating each user;
The 4th step: if last user's tolerance factor is greater than its judgement factor, provide time user's optimal power and primary user's pricing method, method finishes;
The 5th step: for the 4th step, if last user's tolerance factor is less than or equal to its judgement factor, be zero by last user's power setting, forward the 4th step to, tolerance factor to remaining user and its judgement factor compare, until condition meets, calculate optimal power and the price that provides primary user.
Preferably, in the first step, particularly, initialization time user's number K=n, the tolerance factor a of inferior user i i:
α i = L w i T + σ 2 g i h i , ( i = 1 , · · · , n )
Wherein, w ifor the preference factor of inferior user i, n is time user's number, and L is the spreading gain of cognitive user, and T is primary user's interference threshold, h ithe channel gain of time user i to own base station place, g ithe channel gain of time user i to primary user's receiving terminal, σ 2that primary user is for inferior user base station interference power and noise sum.
Preferably, in second step, inferior user's sortord is: α 1>=...>=α k, wherein α kfor the tolerance factor of inferior user K.
Preferably, in the 3rd step, particularly, and for inferior user i, i=1 ..., the judgement factor-beta of K ivalue be:
β i = L ( T Σ j = 1 i w j T + σ 2 g j h j + σ 2 Σ j = 1 i w j T + σ 2 g j h j g j h j ) 2 ( L - 1 ) 2 ( T + KT L - 1 + σ 2 L - 1 Σ j = 1 i g j h j ) 2 ,
Wherein: L is the spreading gain of cognitive user, T is primary user's interference threshold, h jthe channel gain of time user j to own base station place, g jthe channel gain of time user j to primary user's receiving terminal, σ 2that primary user is for inferior user base station interference power and noise sum, w jit is the preference factor of time user j.
Preferably, in the 4th step, particularly, if the tolerance factor α of inferior user K kbe greater than its judgement factor-beta k, the optimal power of time user i so for:
P i * = σ 2 a i h i ( 1 - Σ i = 1 n a i ) ( i = 1 , . . . , K ) ,
a i = max ( 0 , 1 L - 1 ( L w i β K T + β K σ 2 g i / h i - 1 ) ) , ( i = 1 , · · · , n )
Wherein: σ 2that primary user is for inferior user base station interference power and noise sum, h ithe channel gain of time user i to own base station place;
Primary user's price for:
λ i * = L w i h i g i ( Σ j ≠ i h j p j * + σ 2 + L h i p i * ) , i = 1 , . . . , n ,
Wherein, for the optimal power of inferior user i, for being the optimal power of time user j, algorithm finishes.
Preferably, in the 5th step, particularly, if the tolerance factor α of inferior user K kbe not more than its judgement factor-beta k, the optimal power of time user K is zero so, and K-1 assignment, to K, is returned to step 4, and remaining K time user's optimal power and price are arranged.
Compared with prior art, the present invention has following beneficial effect:
The present invention is replaced the non-protruding optimization problem of primary user's income is converted into a protruding optimization problem of equivalence by variable, provides the closed solutions of time user's optimal power and primary user's optimal pricing by the optimality condition of protruding optimization problem.Can find based on non-uniform pricing method the price that maximizes primary user's income compared with traditional, instead of the price of one group of suboptimum.Method provided by the present invention, in having improved primary user's income, can also increase the inferior number of users of allowing access.Because algorithm has analytical expression, therefore execution speed is fast, has good feasibility and practicality.
Brief description of the drawings
By reading the detailed description of non-limiting example being done with reference to the following drawings, it is more obvious that other features, objects and advantages of the present invention will become:
Fig. 1 is according to Simulation Model figure provided by the invention;
Fig. 2 primary user's yield curve figure that is the present invention in the time that interference-to-noise ratio is increased to 40dB from-40dB;
What Fig. 3 was the present invention in the time that interference-to-noise ratio is increased to 40dB from-40dB allows access time number of users curve chart;
Fig. 4 is schematic flow sheet of the present invention.
Embodiment
Below in conjunction with specific embodiment, the present invention is described in detail.Following examples will contribute to those skilled in the art further to understand the present invention, but not limit in any form the present invention.It should be pointed out that to those skilled in the art, without departing from the inventive concept of the premise, can also make some distortion and improvement.These all belong to protection scope of the present invention.
Optimal power control method based on price in the open cognition network of the present invention, comprising: initialization time user's number and tolerance factor; According to inferior user's tolerance factor, inferior user is carried out to descending; Calculate time user's the judgement factor; Compare to determine the factor and tolerance factor magnitude relationship, if condition meets, provide time user's optimal power and primary user's pricing method, method finishes; Otherwise the inferior user power that tolerance factor is minimum is set to zero, all the other user continues to carry out according to the 4th step, until condition meets, provides time user's optimal power and primary user's pricing method.The tolerance factor that the present invention knows time user in the case of primary user and the channel information of whole cognition network, just can provide time user's the closed solutions of optimal power allocation and primary user's optimal pricing by being no more than the iteration of total several of time user, the method can increase primary user's income and allow that more time user accesses frequency spectrum.
The present embodiment is the optimal power control program based on price, and inferior user's number is 10, and inferior user distribution is within the scope of the 200m of base station, and primary user's receiving terminal is 300m to the distance of base station, and primary user is σ for inferior user base station interference power and noise sum 2=10 -12w, inferior user's spreading gain L=128, inferior user i is to the channel gain at own base station place inferior user i to the channel gain of primary user's receiving terminal is wherein A shows the antenna gain of system, the isoparametric impact of carrier spectrum, value 10 3; α iand β ito meet zero-mean, the Gaussian Profile that standard deviation is 6dB; d iand s ibe respectively time user to base station and arrive the distance of primary user's receiving terminal, interference-to-noise ratio is increased to 40dB from-40dB.
The first step, each user's of initialization number K=10, and inferior user's tolerance factor a i:
α i = L w i T + σ 2 g i h i , ( i = 1 , · · · , n )
Wherein, w ifor the preference factor of inferior user i, n is time user's number, and L is the spreading gain of cognitive user, and T is primary user's interference threshold, h ithe channel gain of time user i to own base station place, g ithe channel gain of time user i to primary user's receiving terminal, σ 2that primary user is for inferior user base station interference power and noise sum.
Second step, the employing descending sort for all users: α 1>=...>=α k, wherein α kfor the tolerance factor of inferior user K.
The 3rd step: the judgement factor-beta that calculates time user i i:
β i = L ( T Σ j = 1 i w j T + σ 2 g j h j + σ 2 Σ j = 1 i w j T + σ 2 g j h j g j h j ) 2 ( L - 1 ) 2 ( T + KT L - 1 + σ 2 L - 1 Σ j = 1 i g j h j ) 2 ,
Wherein, L is the spreading gain of cognitive user, and T is primary user's interference threshold, h jthe channel gain of time user j to own base station place, g jthe channel gain of time user j to primary user's receiving terminal, σ 2that primary user is for inferior user base station interference power and noise sum, w jit is the preference factor of time user j.
The 4th step: for inferior user K, if as α k> β kset up wherein α k, β kbe respectively the tolerance factor of time user K and judge the factor, the optimal power of time user i is so:
P i * = σ 2 a i h i ( 1 - Σ i = 1 n a i ) ( i = 1 , . . . , K ) ,
Wherein, a ipass through equation a i = max ( 0 , 1 L - 1 ( L w i β K T + β K σ 2 g i / h i - 1 ) ) , ( i = 1 , · · · , n ) Determine, L is the spreading gain of cognitive user, and T is primary user's interference threshold, h ithe channel gain of time user i to own base station place, g ithe channel gain of time user i to primary user's receiving terminal, σ 2that primary user is for inferior user base station interference power and noise sum, β kfor the judgement factor of inferior user K.Primary user's price i=1 ..., n, wherein for the optimal power of inferior user i, for being the inferior optimal power with j, algorithm finishes.
The 5th step: for inferior user K, if α k≤ β kset up wherein α k, β kbe respectively the tolerance factor of time user K and judge the factor, K time user power is set to 0 so, makes K:=K-1, gets back to the 4th step.
In the present embodiment, Fig. 1 is Simulation Model figure of the present invention, in figure, n user need to could access base station by primary user's interference is paid, and primary user controls inferior user the total interference of self is less than to interference threshold by homogeneous user not being formulated to different interference prices.Fig. 2 has provided the primary user's yield curve figure that adopts respectively non-uniform pricing method and the present embodiment method to obtain; Fig. 3 is the inferior number of users curve chart that the system that adopts respectively non-uniform pricing method and the present embodiment method to obtain is allowed.As seen from Figure 2: the primary user that two kinds of methods obtain is along with the increase of interference-to-noise ratio, the more non-uniform pricing method of the implementation method of carrying obtained higher primary user's income, in the time that interference-to-noise ratio is 40dB, the more non-uniform pricing method of primary user's income obtaining by institute's extracting method has exceeded 1.5 times.As seen from Figure 3: the more non-uniform pricing method of institute's extracting method can allow that more time user accesses primary user's frequency spectrum, in the time that interference-to-noise ratio is 40dB, allow that by institute's extracting method the more non-uniform pricing method of inferior number of users of access frequency spectrum has exceeded 25%.Promote primary user's income in conjunction with the known institute of Fig. 2, Fig. 3 extracting method than the Poewr control method of non-uniform price, can allow that more time user accesses frequency spectrum simultaneously.Because institute's extracting method can obtain the closed solutions of inferior user's optimal power and primary user's price, institute's extracting method can solve in cognition network the relevant issues such as the power control based on price mechanism effectively.
Above specific embodiments of the invention are described.It will be appreciated that, the present invention is not limited to above-mentioned specific implementations, and those skilled in the art can make various distortion or amendment within the scope of the claims, and this does not affect flesh and blood of the present invention.

Claims (6)

1. the optimal power control method based on price in cognition network, is characterized in that, comprises following concrete steps:
The first step: initialization time user's number and time user's tolerance factor;
Second step: by the descending of inferior user's tolerance factor;
The 3rd step: the judgement factor of calculating each user;
The 4th step: if last user's tolerance factor is greater than its judgement factor, provide time user's optimal power and primary user's pricing method, method finishes;
The 5th step: for the 4th step, if last user's tolerance factor is less than or equal to its judgement factor, be zero by last user's power setting, forward the 4th step to, tolerance factor to remaining user and its judgement factor compare, until condition meets, calculate optimal power and the price that provides primary user.
2. the optimal power control method based on price in cognition network according to claim 1, is characterized in that, in the first step, particularly, initialization time user's number K=n, the tolerance factor a of inferior user i i:
α i = L w i T + σ 2 g i h i , ( i = 1 , · · · , n )
Wherein, w ifor the preference factor of inferior user i, n is time user's number, and L is the spreading gain of cognitive user, and T is primary user's interference threshold, h ithe channel gain of time user i to own base station place, g ithe channel gain of time user i to primary user's receiving terminal, σ 2that primary user is for inferior user base station interference power and noise sum.
3. the optimal power control method based on price in cognition network according to claim 2, is characterized in that, in second step, inferior user's sortord is: α 1>=...>=α k, wherein α kfor the tolerance factor of inferior user K.
4. the optimal power control method based on price in cognition network according to claim 3, is characterized in that, in the 3rd step, particularly, and for inferior user i, i=1 ..., the judgement factor-beta of K ivalue be:
β i = L ( T Σ j = 1 i w j T + σ 2 g j h j + σ 2 Σ j = 1 i w j T + σ 2 g j h j g j h j ) 2 ( L - 1 ) 2 ( T + KT L - 1 + σ 2 L - 1 Σ j = 1 i g j h j ) 2 ,
Wherein: L is the spreading gain of cognitive user, T is primary user's interference threshold, h jthe channel gain of time user j to own base station place, g jthe channel gain of time user j to primary user's receiving terminal, σ 2that primary user is for inferior user base station interference power and noise sum, w jit is the preference factor of time user j.
5. the optimal power control method based on price in cognition network according to claim 4, is characterized in that, in the 4th step, particularly, if the tolerance factor α of inferior user K kbe greater than its judgement factor-beta k, the optimal power of time user i so for:
P i * = σ 2 a i h i ( 1 - Σ i = 1 n a i ) ( i = 1 , . . . , K ) ,
a i = max ( 0 , 1 L - 1 ( L w i β K T + β K σ 2 g i / h i - 1 ) ) , ( i = 1 , · · · , n )
Wherein: σ 2that primary user is for inferior user base station interference power and noise sum, h ithe channel gain of time user i to own base station place;
Primary user's price for:
λ i * = L w i h i g i ( Σ j ≠ i h j p j * + σ 2 + L h i p i * ) , i = 1 , . . . , n ,
Wherein, for the optimal power of inferior user i, for the optimal power of inferior user j, algorithm finishes.
6. the optimal power control method based on price in cognition network according to claim 4, is characterized in that, in the 5th step, particularly, if the tolerance factor α of inferior user K kbe not more than its judgement factor-beta k, the optimal power of time user K is zero so, and K-1 assignment, to K, is returned to step 4, and remaining K time user's optimal power and price are arranged.
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