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

Abstract

The invention discloses a method for maximizing primary user revenue in a cognitive network, including: initializing secondary user power; judging whether the maximum interference power limit of the secondary user is satisfied according to the secondary user initialization power, and if the condition is met, giving the primary user's pricing method, the method ends; otherwise, enter the next step; for the second step, the initial power of the secondary user does not meet the maximum interference power limit, iteratively search the secondary user power; the fourth step: traverse the secondary user power obtained in the third step iterative search, Find the power of a set of secondary users that maximizes the primary user's revenue, and based on this power, the primary user gives a price. In the present invention, when the primary user knows the channel information of the secondary user interfering with itself, the optimal pricing of the primary user can be searched for at most n iterations, and the power of the secondary user in each search is obtained by calculating the analytical expression, which has the advantages of The advantages of fast search speed, strong practicability and feasibility.

Description

A Pricing Method for Maximizing Primary User Benefits in Cognitive Networks

technical field

The invention relates to the technical field of spectrum resource management, in particular to a pricing method for maximizing primary user revenue in a cognitive network.

Background technique

In the case of spectrum sharing, the primary user can allow the secondary user to access its own spectrum with appropriate power by charging for the interference caused by the secondary user. For the primary user, in order to ensure its own service quality, it is necessary to adopt a reasonable pricing method for each secondary user so that the total interference caused by the secondary user is less than a certain threshold; at the same time, because the price affects the demand of the secondary user, in order to maximize To benefit themselves, the main user needs to adopt a better pricing method. Therefore, in order to ensure that the interference when the secondary user accesses does not affect the normal communication of the primary user and maximize the profit of the primary user, it is necessary for the primary user to price the interference caused by each secondary user reasonably. If the primary user's price is too low, the secondary user's interference will be too large, which is greater than the primary user's interference threshold; if the primary user's price is too high, the secondary user's purchase demand will be reduced, thereby reducing its own income. Therefore, a better pricing method is needed to maximize the income of the head user on the premise of ensuring the service quality of the main user.

In recent years, the study of pricing methods to maximize primary user benefits in cognitive networks has received increasing attention. Existing literature search found that the relevant literature is as follows:

Hui Yu et al published an article entitled "Pricing for uplink power controlin cognitive radio networks" in "2010 IEEE Transactions on Vehicular Technology, May.2010, vol.59, no.4, pp.1769–1778." In this article, the secondary user can rent the spectrum of the primary user, and the secondary user is allowed to access under a certain interference threshold. The model is non-convex because the main user maximizes the utility function. The article sets the main user's strategy to a certain linear proportional relationship, and then transforms the main user's income problem into a univariate optimization problem to solve a set of pricing , however this method can only obtain a suboptimal pricing algorithm.

Yuan Wu et al published a paper entitled "Joint Pricing and Power Allocation for Dynamic Spectrum Access Networks with Stackelberg Game" in "2011 IEEE Transactions on Wireless Communications, Jan.2011, vol.10, no.1, pp.12-19." Model” article. This article proposes a new pricing model, which considers the quality of service of the primary user, models the problem of joint pricing and power control in the cognitive network as a Stackelberg game problem, and proposes a low-complexity heuristic algorithm To maximize the main user's income.

According to relevant research, in order to maximize the primary user's revenue and satisfy its service quality, it is necessary for the primary user to adopt a more reasonable pricing method for secondary users. Based on the monotonicity of the utility function of the primary user with respect to the interference power, the invention transforms the non-convex problem of the primary user's income into an equivalent convex problem, and proposes a pricing method for maximizing the primary user's income.

Contents of the invention

The present invention provides a pricing method for maximizing the primary user's revenue aiming at the deficiency that the existing pricing method for the primary user in the cognitive network cannot guarantee the maximization of the primary user's revenue. The present invention enables the primary user to maximize primary user revenue through an iterative method under the condition of knowing the link channel information of the secondary user and the preference weight of the rate. This pricing method finds the optimal solution for maximizing the primary user's revenue under the premise that the secondary user's interference with the primary user is less than a given interference threshold, which improves the primary user's revenue and increases the primary user's revenue compared with the traditional linear proportional pricing method The total revenue and throughput of the sub-user network.

According to one aspect of the present invention, a pricing method for maximizing primary user revenue in a cognitive network includes the following specific steps:

Step 1: Initialize the secondary user power;

The second step: judging whether the maximum interference power limit of the secondary user is satisfied according to the initial power of the secondary user, if the condition is satisfied, the pricing method of the primary user is given, and the method ends; otherwise, enter the third step;

Step 3: Iteratively search for the power of the secondary user in view of the fact that the initial power of the secondary user does not meet the maximum interference power limit in the second step;

Step 4: By traversing the power of the secondary users obtained in the iterative search in the third step, find a group of secondary user powers that maximize the primary user's revenue. According to the power, the primary user gives a price.

Preferably, in the first step, specifically, the transmit power p _i of each secondary user i is initialized: $p_i=\frac{1}{L-1}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)/h_i,i=1,\&Center\; Dot;\&Center\; Dot;\&Center\; Dot;,\mathrm{no},$

Among them, w _i is the preference factor of secondary user i, n is the number of secondary users, L is the spreading gain of cognitive user, T is the interference threshold of primary user, and _h is the channel gain from secondary user i to the base station , ^σ2 is the background noise; the parameter λ is passed through the equation $\frac{1}{L-1}\underset{i=1}{\overset{\mathrm{no}}{\mathrm{\Σ}}}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)=T$ Given; number of initialization iterations k=1.

为：

次用户的发射功率

i＝1,…,n,定价结束；如果条件不成立，转到第三步。Preferably, in the second step, specifically, if for all i∈{1,…,n}, the condition h _i p _i ≤P _max holds, where P _max is the maximum interference power of each secondary user, the primary user pricing

for:

The transmit power of the secondary user

i=1,...,n, the pricing ends; if the condition is not satisfied, go to the third step.

的k值时，令i＝k-1,

j＝1,…,i-1，第k次迭代次用户j的功率

由等式 $p_{\mathrm{jk}}^{*}=\max (\frac{\sqrt{\frac{{\mathrm{Lw}}_j(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T_k+{\mathrm{\σ}}^2)}{L-1},0)/h_j,j\≥i$ 确定；其中参数T_k通过方程组Preferably, in the third step, specifically, for all

When the value of k, let i=k-1,

j=1,...,i-1, the power of user j in the kth iteration

by the equation $p_{\mathrm{jk}}^{*}=\max (\frac{\sqrt{\frac{{\mathrm{Lw}}_j(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T_k+{\mathrm{\σ}}^2)}{L-1},0)/h_j,j\&Greater\; Equal;i$ determined; where the parameter T _k is passed through the equation

$\left\{\begin{array}{c}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; k</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; max</span>}<span\; class="notranslate">\; (</span>\frac{\sqrt{\frac{{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}\\ \mathrm{<span\; class="notranslate">\; max</span>}<span\; class="notranslate">\; (</span>\frac{\sqrt{\frac{{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}\end{array}\right.<span\; class="notranslate">\; ,</span>$

Obtain, update the number of iterations to k+1 times, where, in the equation system

为：

i＝1,…,n，其中

为次用户i的在主用户最优定价下对应的最佳发射功率：

其中，

为第j次迭代次用户i的功率,

为第k次迭代次用户i的功率。Preferably, in the fourth step, specifically, the main user sets the price

for:

i=1,...,n, where

is the optimal transmit power of secondary user i under the optimal pricing of primary user:

in,

is the power of user i in the jth iteration,

is the power of user i in the kth iteration.

Compared with the prior art, the beneficial effect of the present invention is: the present invention searches for the pricing strategy that maximizes the primary user's revenue through the optimality condition of the primary user, and can find the maximum value compared with the traditional pricing method based on linear proportional constraints Pricing that maximizes user benefits, rather than a set of suboptimal pricing. The method provided by the invention can improve the income and throughput of the secondary user while improving the income of the primary user. Because the algorithm has an analytical expression, it has fast execution speed and good feasibility and practicability.

Description of drawings

Other characteristics, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

Fig. 1 is the flowchart of the pricing method for maximizing primary user revenue in the cognitive network provided according to the present invention;

Fig. 2 is the primary user benefit curve diagram when the interference power threshold increases from 0 to 20 in the present invention;

Fig. 3 is the secondary user total revenue curve when the interference power threshold increases from 0 to 20 in the present invention;

FIG. 4 is a curve diagram of secondary users and rates when the interference power threshold increases from 0 to 20 in the present invention.

Detailed ways

The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following the described embodiment.

This embodiment is a pricing scheme for maximizing the income of the primary user. The background noise is Gaussian white noise with zero mean value σ ² =10, the spreading gain of the secondary user is L=32, and the link gain h of the secondary user satisfies [0 ,1], the preference factor of the secondary user is uniformly distributed in [0,300], and the results are averaged over 10 ⁴ simulations.

The first step is to initialize the transmit power of each secondary user: $p_i=\frac{1}{L-1}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\&sigma;}}^2)}{\mathrm{\&lambda;}}}-(T+{\mathrm{\&sigma;}}^2),0)/h_i,i=1,\&Center\; Dot;\&Center\; Dot;\&Center\; Dot;,\mathrm{no},$ Among them: w _i is the preference factor of secondary user i, n is the number of secondary users, L is the spreading gain of cognitive users, T is the interference threshold set by the primary user, hi is the channel from secondary user i to the base station Gain, ^σ2 is the background noise. λ is the equation: $\frac{1}{L-1}\underset{i=1}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\&sigma;}}^2)}{\mathrm{\&lambda;}}}-(T+{\mathrm{\&sigma;}}^2),0)=T$ solution. The number of initialization iterations k=1.

为：

次用户的发射功率

为：

i＝1,…,n,定价结束。否则，转到第三步。In the second step, if for all i∈{1,…,n}, h _i p _i ≤ P _max holds true, where P _max is the maximum received power from each secondary user to the base station, then the secondary user’s pricing

for:

The transmit power of the secondary user

for:

i=1,...,n, pricing ends. Otherwise, go to step three.

时，令i＝k-1,

j＝1,…,i-1。第k次迭代次用户j的功率

由等式Step Three: When

, let i=k-1,

j=1,...,i-1. The power of user j at the kth iteration

by the equation

$p_{\mathrm{jk}}^{*}=\max (\frac{\sqrt{\frac{{\mathrm{Lw}}_j(T+{\mathrm{\&sigma;}}^2)}{\mathrm{\&lambda;}}}-(T_k+{\mathrm{\&sigma;}}^2)}{L-1},0)/h_j,j\&Greater\; Equal;i$ Sure;

where T _k is the system of equations:

$\left\{\begin{array}{c}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; k</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; max</span>}<span\; class="notranslate">\; (</span>\frac{\sqrt{\frac{{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}\\ \mathrm{<span\; class="notranslate">\; max</span>}<span\; class="notranslate">\; (</span>\frac{\sqrt{\frac{{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}\end{array}\right.<span\; class="notranslate">\; ,</span>$

The solution, update the number of iterations to k+1 times, where, in the equation system

那么次用户的发射功率为： $p_i^{*}=p_{\mathrm{ij}}^{*},$ 主用户的定价为： ${\mathrm{\&lambda;}}_i^{*}=\frac{{\mathrm{Lw}}_i}{{\mathrm{\&Sigma;}}_{j\&NotEqual;i}{h}_j{p}_j^{*}+{\mathrm{\&sigma;}}^2+{\mathrm{Lh}}_i{p}_i^{*}},i=1,\&CenterDot;\&CenterDot;\&CenterDot;,n.$ Step Four: Order

Then the transmit power of the secondary user is: $p_i^{*}=p_{\mathrm{ij}}^{*},$ Pricing for primary users is: ${\mathrm{\&lambda;}}_i^{*}=\frac{{\mathrm{Lw}}_i}{{\mathrm{\&Sigma;}}_{j\&NotEqual;i}{h}_j{p}_j^{*}+{\mathrm{\&sigma;}}^2+{\mathrm{Lh}}_i{p}_i^{*}},i=1,\&CenterDot;\&CenterDot;\&CenterDot;,\mathrm{no}.$

The secondary user initialization power is: $p_i=\frac{1}{L-1}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\&sigma;}}^2)}{\mathrm{\&lambda;}}}-(T+{\mathrm{\&sigma;}}^2),0)/h_i,,$ i=1,2,...,nT is the interference threshold of the primary user, L is the spreading gain of the secondary user, h _i is the channel gain from the secondary user i to the base station, and σ ² is the background noise. where λ is passed through the equation: $\frac{1}{L-1}\underset{i=1}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\&sigma;}}^2)}{\mathrm{\&lambda;}}}-(T+{\mathrm{\&sigma;}}^2),0)=T$ given.

其中：为次用户i的发射功率。如果有至少一个次用户到基站功率大于P_max，那么通过如下迭代搜索获得可能的最优发射功率。The optimal pricing criterion for the primary user is: if the power to the base station for the secondary user used is less than P _max , then the pricing for the primary user is:

in: is the transmit power of secondary user i. If there is at least one secondary user whose power to the base station is greater than P _max , then the possible optimal transmit power is obtained through the following iterative search.

The iterative search is as follows:

时，令i＝k-1,

j＝1,…,i-1.when

, let i=k-1,

j=1,...,i-1.

${\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; jk</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; max</span>}<span\; class="notranslate">\; (</span>\frac{\sqrt{\frac{{\mathrm{<span\; class="notranslate">\; Lw</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; .</span>$

where T _k is the system of equations:

$\left\{\begin{array}{c}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; k</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; max</span>}<span\; class="notranslate">\; (</span>\frac{\sqrt{\frac{{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}\\ \mathrm{<span\; class="notranslate">\; max</span>}<span\; class="notranslate">\; (</span>\frac{\sqrt{\frac{{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}\end{array}\right.<span\; class="notranslate">\; ,</span>$

更新迭代次数到k+1次；The solution of , where, in this system of equations

Update the number of iterations to k+1 times;

为次用户的最优发射功率，j的取值为：The optimal pricing for said primary user is: ${\mathrm{\&lambda;}}_i^{*}=\frac{{\mathrm{Lw}}_i}{{\mathrm{\&Sigma;}}_{j\&NotEqual;i}{h}_j{p}_j^{*}+{\mathrm{\&sigma;}}^2+{\mathrm{Lh}}_i{p}_i^{*}},i=1,\&Center\; Dot;\&Center\; Dot;\&Center\; Dot;,\mathrm{no},$ in

is the optimal transmit power of the secondary user, and the value of j is:

In this embodiment, Figure 2 shows the primary user revenue curve obtained by using the proportional linear pricing method and the method of this embodiment respectively; Figure 3 is the total revenue of the secondary users obtained by using the proportional linear pricing method and the method of this embodiment respectively Graph; FIG. 4 is a graph of secondary users and rates obtained by using the proportional linear pricing method and the method of this embodiment respectively. It can be seen from Figure 2 that the proposed implementation method obtains higher primary user benefits than the proportional linear pricing method. It can be seen from Figure 3 that the total revenue of the secondary users obtained by the proposed method is higher than that obtained by the proportional linear pricing method. It can be seen from Figure 4 that the sum rate of the secondary users obtained by the proposed method is higher than that obtained by the proportional linear pricing method. Combined with Figure 2, Figure 3, and Figure 4, it can be seen that the proposed method improves the sum rate of the cognitive network compared with the traditional cost-based method. This method obtains the optimal pricing strategy of the primary user, and the proposed method can effectively solve related problems such as pricing-based power control in cognitive networks.

Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention.

Claims (5)

1. A pricing method for maximizing revenue of a main user in a cognitive network is characterized by comprising the following specific steps:

the first step is as follows: initializing secondary user power;

the second step is that: judging whether the maximum interference power limit of the secondary user is met or not according to the secondary user initialization power, if so, giving a pricing method of the primary user, and ending the method; otherwise, entering the third step;

the third step: aiming at the condition that the initialization power of the secondary user does not meet the maximum interference power limit in the second step, iteratively searching the power of the secondary user;

the fourth step: and (4) finding a group of power of the secondary users maximizing the benefits of the primary users by traversing the power of the secondary users obtained by the iterative search in the third step, and pricing by the primary users according to the power.

2. A pricing method for maximizing primary user revenue in cognitive network according to claim 1, characterized in that in the first step, specifically, initializing the transmission power p of each secondary user i_i： $p_i=\frac{1}{L-1}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\&sigma;}}^2)}{\mathrm{\&lambda;}}}-(T+{\mathrm{\&sigma;}}^2),0)/h_i,i=1,\&CenterDot;\&CenterDot;\&CenterDot;,n,$

Wherein, w_iIs a preference factor of a secondary user i, n is the number of secondary users, L is the spread spectrum gain of a cognitive user, T is the interference threshold of a primary user, h_iIs the channel gain, σ, at the secondary user i to the base station²Is background noise; parameter lambda pass equation $\frac{1}{L-1}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\&sigma;}}^2)}{\mathrm{\&lambda;}}}-(T+{\mathrm{\&sigma;}}^2),0)=T$ Giving out; the number of initialization iterations k is 1.

3. A pricing method in cognitive networks maximizing primary user revenue as claimed in claim 2, characterized by the fact that in the second step, condition h is applied, in particular if for all i e {1, …, n }, all i e {1, …, n }_ip_i≤P_maxIs established, wherein P_maxPricing of primary user for maximum interference power per secondary user

Comprises the following steps:

transmission power of secondary userThe pricing is finished; if the condition is not satisfied, go to the third step.

4. A pricing method for maximizing primary user revenue in cognitive networks according to claim 3, wherein in the third step, specifically for all less than all

When the value of k is (i) ═ k-1,

j-1, …, i-1, power of the kth iteration sub-user j

Is given by the equation $p_{\mathrm{jk}}^{*}=\max (\frac{\sqrt{\frac{{\mathrm{Lw}}_j(T+{\mathrm{\&sigma;}}^2)}{\mathrm{\&lambda;}}}-(T_k+{\mathrm{\&sigma;}}^2)}{L-1},0)/h_j,$ j is more than or equal to i; wherein the parameter T_kBy a system of equations

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

Obtaining, updating the iteration number to k +1 times, wherein the equation set

5. A pricing method for maximizing primary user revenue in cognitive network according to claim 4, wherein in the fourth step, in particular, primary user pricingComprises the following steps:

i is 1, …, n, wherein

Optimal transmit power for secondary user i:wherein,

for the power of user i for the jth iteration,the power of the sub-user i for the kth iteration.

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