CN103052078A - Pricing method for maximizing revenue of primary user in cognitive network - Google Patents

Abstract

The invention discloses a method for maximizing the revenue of a primary user in a cognitive network. The method comprises the following steps of: initializing the power of a secondary user; judging whether the initialized power of the secondary user meets the maximum interference power limit of the secondary user or not according to the initialized power of the secondary user, and if the initialized power of the secondary user meets the maximum interference power limit, providing a pricing method of the primary user, and finishing the method, otherwise entering the next step; for the condition that the initialized power of the secondary user does not meet the maximum interference power limit in the second step, iteratively searching for the power of the secondary user; traversing the power of the secondary user, which is obtained by the iterative searching, to find the power, by which the revenue of the primary user can be maximized, of a group of secondary users, and making a price according to the power by the primary user. Under the condition that the primary user knows the information of an interference channel of the secondary user for the primary user, an optimal price for the primary user can be found by iteration of at most n times, and the power of the secondary user during the searching of each time is calculated by an analytical expression; and the method has the advantages of high searching speed, practicability and feasibility.

Description

The pricing method of maximization primary user income in the cognition network

Technical field

The present invention relates to the dynamic spectrum resource management technical field, specifically the pricing method of maximization primary user income in the cognition network.

Background technology

In the frequency spectrum share situation, the primary user can charge by the interference that inferior user is caused and allow time user to adopt suitable power access self frequency spectrum.For the primary user, at first in order to guarantee the service quality of self, need to for each time user adopt rational pricing method so that inferior user to its total interference that causes less than certain thresholding; Simultaneously, because price time user's demand, in order to maximize own income, the primary user need to adopt a kind of preferably pricing method.Therefore, the interference when guaranteeing that time user accesses does not affect primary user's proper communication and maximization primary user's income, needs the primary user reasonably to fix a price for the interference that each time user causes.Because primary user's the too low meeting of price causes time user's interference excessive, thereby greater than primary user's interference threshold; Primary user's price is too high, has reduced inferior user's purchasing demand, thus so that the income of self minimizing.So, need a kind of preferably pricing method under the prerequisite that guarantees primary user's service quality, maximization householder user's income.

In recent years, pricing method being maximized primary user's income in cognition network studies and just receives increasing concern.Existing literature search is found that pertinent literature is as follows:

The people such as Hui Yu are at " 2010 IEEE 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 ".Time user can lease primary user's frequency spectrum in this article, allows time user's access under certain interference threshold.This model is because primary user's maximum utility functional expression is non-protruding, article is set primary user's strategy for certain linear ratio relation, then primary user's income problem is converted into a univariate optimization problem and solves one group of price, yet the method can only obtain the pricing algorithm of a suboptimum.

The people such as Yuan Wu are at " 2011 IEEE Transactions on Wireless Communications, Jan.2011, vol.10, no.1, pp.12-19. " on delivered the article that is entitled as " Joint Pricing and Power Allocation for Dynamic Spectrum Access Networks with Stackelberg Game Model ".This article has proposed a kind of new pricing model, this model is considered and is guaranteed primary user's service quality, the problem of alliance pricing in the cognition network and power control is modeled as the Stackelberg problem of game, and the heuristic algorithm that has proposed a kind of low complex degree maximizes primary user's income.

By correlative study as can be known, in order to maximize primary user's income, expire simultaneously its service quality, need the primary user to adopt a more rational pricing method for inferior user.The present invention is based on primary user's utility function about the monotonicity of interference power, the non-protruding problem of primary user's income is converted into a protruding problem of equivalence, proposed a kind of pricing method of the primary user's of maximization income.

Summary of the invention

The pricing method that the present invention is directed to primary user in the existing cognition network can not guarantee to maximize the deficiency of primary user's income, and a kind of pricing method of the primary user's of maximization income is provided.The present invention can so that the primary user in the situation of the preference weight of the link channel information of knowing time user and speed, the method by a kind of iteration realizes maximizing primary user's income.This pricing method is guaranteeing under time prerequisite of interference less than given interference threshold of user for the primary user, find the optimal solution of maximization primary user income, improve primary user's income than tradition based on the pricing method of linear scale, and increased total income and the throughput of inferior user network.

According to an aspect of the present invention, provide the pricing method of maximization primary user income in a kind of cognition network to comprise following concrete steps:

The first step: initialization time user power;

Second step: judge whether that according to inferior user's initialization power the maximum interference power that satisfies time user limits, if condition satisfies, provide primary user's pricing method, method finishes; Otherwise, entered for the 3rd step;

The 3rd step: do not satisfy the maximum interference power limited case for second step time user's initialization power, iterative search time user power;

The 4th step: the inferior user power that obtains by traveling through the 3rd step iterative search, find one group of power that maximizes the inferior user of primary user's income, according to this power, the primary user provides price.

Preferably, in the first step, particularly, the transmitting power p of each time of initialization user i _i: $p_i=\frac{1}{L-1}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)/h_i,i=1,\·\·\·,n,$

Wherein, w _iBe 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 that time user i locates to the base station, σ ²It is background noise; Parameter lambda is passed through equation $\frac{1}{L-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)=T$ Provide; Initialization iterations k=1.

Preferably, in second step, particularly, if for all i ∈ 1 ..., n}, condition h _ip _i≤ P _Max, set up, wherein P _MaxBe each time user's maximum interference power, primary user's price

For:

Inferior user's transmitting power

I=1 ..., n, price finishes; If condition is false, forwarded for the 3rd step to.

Preferably, the 3rd the step in, particularly, for all less than

The k value time, make i=k-1,

J=1 ..., i-1, the power of the k time iteration time user j

By equation $p_{\mathrm{jk}}^{*}=\max (\frac{\sqrt{\frac{{\mathrm{Lw}}_j(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T_k+{\mathrm{\σ}}^2)}{L-1},0)/h_j,j\≥i$ Determine; Parameter T wherein _kPass through equation group

$\left\{\begin{array}{c}\underset{i=k}{\overset{n}{\mathrm{\Σ}}}\max (\frac{\sqrt{\frac{w_i{a}_k}{\mathrm{\λ}}}-a_k}{b},0)=T_k\\ \max (\frac{\sqrt{\frac{w_j{a}_k}{\mathrm{\λ}}}-a_k}{b},0)=p_{\max }\end{array}\right.,$

Obtain, upgrade iterations to k+1 time, wherein, in this equation group

Preferably, in the 4th step, particularly, primary user's price

For:

I=1 ..., n, wherein

Corresponding best transmit power under primary user's optimal pricing for inferior user i:

Wherein,

Be the power of the j time iteration time user i,

Be the power of the k time iteration time user i.

Compared with prior art, the invention has the beneficial effects as follows: the present invention searches for the pricing strategy of maximization primary user income by primary user's optimality condition, more traditional pricing method based on the linear scale constraint can find the price of maximization primary user income, rather than the price of one group of suboptimum.Method provided by the present invention can also improve time user's income and throughput in the income that has improved the primary user.Because algorithm has analytical expression, so execution speed is fast, has preferably feasibility and practicality.

Description of 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 the flow chart according to the pricing method of maximization primary user income in the cognition network provided by the invention;

Fig. 2 is that the present invention is increased to primary user's yield curve figure of 20 o'clock at the interference power thresholding from 0;

Fig. 3 is that the present invention is increased to inferior user's total revenue curve figure of 20 o'clock at the interference power thresholding from 0;

Fig. 4 is that the present invention is increased to inferior user and rate profile at the interference power thresholding at 20 o'clock from 0.

Embodiment

Below in conjunction with accompanying drawing embodiments of the invention are elaborated: present embodiment is implemented under take technical solution of the present invention as prerequisite, provided detailed execution mode and concrete operating process, but protection scope of the present invention is not limited to following embodiment.

Present embodiment is the pricing scheme of maximization primary user income, and background noise is the white Gaussian noise value σ of zero-mean ²=10, inferior user's spreading gain L=32, inferior user's link gain h are the even distributions on satisfied [0,1], and inferior user's the preference factor is the even distribution of [0,300], and the result is by 10 ⁴Inferior emulation averages.

The first step, each time of initialization user's transmitting power: $p_i=\frac{1}{L-1}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)/h_i,i=1,\·\·\·,n,$ Wherein: w _iBe 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 the interference threshold that the primary user sets, and hi is the channel gain that time user i locates to the base station, σ ²It is background noise.λ is equation: $\frac{1}{L-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)=T$ Solution.Initialization iterations k=1.

Second step, if for all i ∈ 1 ..., n} has h _ip _i≤ P _MaxSet up, wherein P _MaxBe the maximal received power of each time user to the base station, so time user's price

For:

Inferior user's transmitting power

For:

I=1 ..., n, price finishes.Otherwise, forwarded for the 3rd step to.

The 3rd step: when

The time, make i=k-1,

J=1 ..., i-1.The power of the k time iteration time user j

By equation

$p_{\mathrm{jk}}^{*}=\max (\frac{\sqrt{\frac{{\mathrm{Lw}}_j(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T_k+{\mathrm{\σ}}^2)}{L-1},0)/h_j,j\≥i$ Determine;

Wherein, T _kEquation group:

$\left\{\begin{array}{c}\underset{i=k}{\overset{n}{\mathrm{\Σ}}}\max (\frac{\sqrt{\frac{w_i{a}_k}{\mathrm{\λ}}}-a_k}{b},0)=T_k\\ \max (\frac{\sqrt{\frac{w_j{a}_k}{\mathrm{\λ}}}-a_k}{b},0)=p_{\max }\end{array}\right.,$

Solution, upgrade iterations to k+1 time, wherein, in this equation group

The 4th step: order

Time user's transmitting power is so: $p_i^{*}=p_{\mathrm{ij}}^{*},$ The primary user is priced at: ${\mathrm{\λ}}_i^{*}=\frac{{\mathrm{Lw}}_i}{{\mathrm{\Σ}}_{j\≠i}{h}_j{p}_j^{*}+{\mathrm{\σ}}^2+{\mathrm{Lh}}_i{p}_i^{*}},i=1,\·\·\·,n.$

Described time user's initialization power is: $p_i=\frac{1}{L-1}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)/h_i,,$ I=1,2 ..., n.T is primary user's interference threshold, L is time user's spreading gain, h _iBe the channel gain of inferior user i to the base station, σ ²Be background noise.Wherein λ passes through equation: $\frac{1}{L-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)=T$ Given.

Described primary user's optimal pricing criterion is: if arrive base station power less than P for used user _Max, being priced at of primary user so:

Wherein: Transmitting power for inferior user i.If there is at least one time user to arrive base station power greater than P _Max, obtain possible optimal transmit power by following iterative search so.

It is as follows that described iteration is searched element:

When

The time, make i=k-1,

J=1 ..., i-1.

$p_{\mathrm{jk}}^{*}=\max (\frac{\sqrt{\frac{{\mathrm{Lw}}_j(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T_k+{\mathrm{\σ}}^2)}{L-1},0)/h_j,j\≥i.$

T wherein _kEquation group:

$\left\{\begin{array}{c}\underset{i=k}{\overset{n}{\mathrm{\Σ}}}\max (\frac{\sqrt{\frac{w_i{a}_k}{\mathrm{\λ}}}-a_k}{b},0)=T_k\\ \max (\frac{\sqrt{\frac{w_j{a}_k}{\mathrm{\λ}}}-a_k}{b},0)=p_{\max }\end{array}\right.,$

Solution, wherein, in this equation group

Upgrade iterations to k+1 time;

The best of described primary user is priced at: ${\mathrm{\λ}}_i^{*}=\frac{{\mathrm{Lw}}_i}{{\mathrm{\Σ}}_{j\≠i}{h}_j{p}_j^{*}+{\mathrm{\σ}}^2+{\mathrm{Lh}}_i{p}_i^{*}},i=1,\·\·\·,n,$ Wherein

Be inferior user's optimal transmit power, the value of j is:

In the present embodiment, Fig. 2 has provided the primary user's yield curve figure that the respectively linear pricing method of adoption rate and present embodiment method obtain; Fig. 3 is inferior user's total revenue curve figure of obtaining of the linear pricing method of adoption rate and present embodiment method respectively; Fig. 4 is the inferior user and the rate profile that obtain of the linear pricing method of adoption rate and present embodiment method respectively.As seen from Figure 2: the implementation method of carrying obtained higher primary user's income than the proportional linearity pricing method.As seen from Figure 3: institute's extracting method is higher than inferior user's that the proportional linearity pricing method obtains total revenue, and as seen from Figure 4: institute's extracting method is higher with speed than the inferior user's of proportional linearity pricing method acquisition.In conjunction with Fig. 2, Fig. 3, Fig. 4 as can be known institute's extracting method than traditional promote cognition network based on the cost method and speed.The method has obtained primary user's optimal pricing strategy, and institute's extracting method can solve in the cognition network effectively based on the relevant issues such as power control of fixing a price.

More than specific embodiments of the invention are described.It will be appreciated that the present invention is not limited to above-mentioned particular implementation, those skilled in the art can make various distortion or modification within the scope of the claims, and this does not affect flesh and blood of the present invention.

Claims (5)

1. the pricing method of maximization primary user income in the cognition network is characterized in that, comprises following concrete steps:

The first step: initialization time user power;

Second step: judge whether that according to inferior user's initialization power the maximum interference power that satisfies time user limits, if condition satisfies, provide primary user's pricing method, method finishes; Otherwise, entered for the 3rd step;

The 3rd step: do not satisfy the maximum interference power limited case for second step time user's initialization power, iterative search time user power;

The 4th step: the inferior user power that obtains by traveling through the 3rd step iterative search, find one group of power that maximizes the inferior user of primary user's income, according to this power, the primary user provides price.

2. the pricing method of maximization primary user income in the cognition network according to claim 1 is characterized in that, in the first step, particularly, the transmitting power p of each time of initialization user i _i: $p_i=\frac{1}{L-1}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)/h_i,i=1,\·\·\·,n,$

Wherein, w _iBe 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 that time user i locates to the base station, σ ²It is background noise; Parameter lambda is passed through equation $\frac{1}{L-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)=T$ Provide; Initialization iterations k=1.

3. the pricing method of maximization primary user income in the cognition network according to claim 2 is characterized in that, in second step, particularly, if for all i ∈ 1 ..., n}, condition h _ip _i≤ P _Max, set up, wherein P _MaxBe each time user's maximum interference power, primary user's price

For:

Inferior user's transmitting power Price finishes; If condition is false, forwarded for the 3rd step to.

4. the pricing method of maximization primary user income in the cognition network according to claim 3 is characterized in that, in the 3rd step, particularly, for all less than

The k value time, make i=k-1,

J=1 ..., i-1, the power of the k time iteration time user j

By equation $p_{\mathrm{jk}}^{*}=\max (\frac{\sqrt{\frac{{\mathrm{Lw}}_j(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T_k+{\mathrm{\σ}}^2)}{L-1},0)/h_j,$ J 〉=i determines; Parameter T wherein _kPass through equation group

$\left\{\begin{array}{c}\underset{i=k}{\overset{n}{\mathrm{\Σ}}}\max (\frac{\sqrt{\frac{w_i{a}_k}{\mathrm{\λ}}}-a_k}{b},0)=T_k\\ \max (\frac{\sqrt{\frac{w_j{a}_k}{\mathrm{\λ}}}-a_k}{b},0)=p_{\max }\end{array}\right.,$

Obtain, upgrade iterations to k+1 time, wherein, in this equation group

5. the pricing method of maximization primary user income in the cognition network according to claim 4 is characterized in that, in the 4th step, particularly, primary user's price For:

I=1 ..., n, wherein

Best transmit power for inferior user i: Wherein,

Be the power of the j time iteration time user i, Be the power of the k time iteration time user i.

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