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

Pricing method for maximizing revenue of main user in cognitive network

Technical Field

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

Background

In spectrum sharing situations, a primary user may allow a secondary user to access its own spectrum with appropriate power by charging for interference caused by the secondary user. For a master user, firstly, in order to ensure the service quality of the master user, a reasonable pricing method needs to be adopted for each secondary user so that the total interference caused by the secondary users to the master user is smaller than a certain threshold; meanwhile, as the price affects the demand of the secondary user, the primary user needs to adopt a better pricing method in order to maximize the income of the primary user. 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 benefit of the primary user, the primary user needs to reasonably price the interference caused by each secondary user. The pricing of the primary user is too low, so that the interference of the secondary user is too large and is larger than the interference threshold of the primary user; the pricing of the primary user is too high, the purchase demand of the secondary user is reduced, and therefore the income of the secondary user is reduced. Therefore, a better pricing method is needed to maximize the income of the user owner on the premise of ensuring the service quality of the user owner.

In recent years, research on pricing methods to maximize revenue for primary users in cognitive networks is receiving increasing attention. The prior literature search shows that the relevant literature is as follows:

an article entitled "printing for uplink power control magnetic networks" was published by Hui Yu et al in 2010 IEEE Transactions on vehicle Technology, May.2010, vol.59, No.4, pp.1769-1778. In the article, a secondary user can rent the frequency spectrum of a primary user and is allowed to access under a certain interference threshold. In the model, as the maximum utility function formula of the main user is non-convex, the strategy of the main user is set into a certain linear proportional relation by the article, and then the income problem of the main user is converted into a univariate optimization problem to solve a group of pricing, however, the method can only obtain a suboptimal pricing algorithm.

Yuan Wu et al, 2011 IEEE Transactions on Wireless Communications, Jan.2011, vol.10, No.1, pp.12-19, published an article entitled "Joint printing and Power Allocation for Dynamic Spectrum Access Networks with Stackelberg Game Model". The article provides a new pricing model, the model considers the guarantee of the service quality of a main user, the problem of joint pricing and power control in the cognitive network is modeled into a Stackelberg game problem, and a low-complexity heuristic algorithm is provided to maximize the income of the main user.

As known from relevant research, in order to maximize the income of the primary user and simultaneously satisfy the service quality of the primary user, the primary user needs to adopt a more reasonable pricing method for the secondary user. The invention provides a pricing method for maximizing the income of the main user, which is based on monotonicity of a utility function of the main user about interference power and converts a non-convex problem of the income of the main user into an equivalency convex problem.

Disclosure of Invention

The invention provides a pricing method for maximizing the income of a master user, aiming at the defect that the conventional pricing method for the master user in a cognitive network cannot guarantee the maximization of the income of the master user. The method and the device can enable the primary user to realize the maximization of the primary user benefit through an iterative method under the condition that the primary user knows the link channel information and the preference weight of the rate of the secondary user. The pricing method finds the optimal solution for maximizing the benefits of the primary user on the premise of ensuring that the interference of the secondary user to the primary user is smaller than a given interference threshold, improves the benefits of the primary user compared with the traditional pricing method based on linear proportion, and increases the total benefits and the throughput of a secondary user network.

According to one aspect of the invention, the pricing method for maximizing the income of the primary user in the cognitive network comprises 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.

Preferably, in a first step, in particular, the transmission power p of the individual secondary users i is initialized_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_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 $\frac{1}{L-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)=T$ Giving out; the number of initialization iterations k is 1.

Preferably, in the second step, in particular if for all i ∈ {1, …, n }, the condition h_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 user

1, …, n, and pricing is finished; if the condition is not satisfied, go to the third step.

Preferably, in the third step, in particular for all less than

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 $p_{\mathrm{jk}}^{*}=\max (\frac{\sqrt{\frac{{\mathrm{Lw}}_j(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T_k+{\mathrm{\σ}}^2)}{L-1},0)/h_j,j\≥i$ Determining; wherein the parameter T_kBy a system of equations

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

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

Preferably, in a fourth step, primary user pricing in particular

Comprises the following steps:

i is 1, …, n, wherein

The corresponding optimal transmitting power of the secondary user i under the optimal pricing of the primary user is as follows:

wherein,

for the power of user i for the jth iteration,

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

Compared with the prior art, the invention has the beneficial effects that: the method searches the pricing strategy for maximizing the income of the main user through the optimality condition of the main user, and can find the pricing for maximizing the income of the main user instead of a set of suboptimal pricing compared with the traditional pricing method based on linear proportion constraint. The method provided by the invention can improve the income of the primary user and simultaneously improve the income and the throughput of the secondary user. The algorithm has an analytical expression, so that the execution speed is high, and the feasibility and the practicability are better.

Drawings

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

FIG. 1 is a flow chart of a pricing method for maximizing revenue of primary users in a cognitive network provided in accordance with the present invention;

FIG. 2 is a graph of primary user gain when the interference power threshold is increased from 0 to 20 in accordance with the present invention;

FIG. 3 is a graph of the total secondary user gain when the interference power threshold is increased from 0 to 20 in accordance with the present invention;

fig. 4 is a graph of the secondary user and rate for the present invention when the interference power threshold is increased from 0 to 20.

Detailed Description

The embodiments of the present invention will be described in detail below with reference to the accompanying drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

This embodiment is a pricing scheme for maximizing the revenue of the primary user, and the background noise is zero mean gaussian white noise value σ²The spreading gain L of the secondary user is 32, and the link gain h of the secondary user satisfies 0,1]Is uniformly distributed, the preference factor of the secondary user is [0,300]Is uniformly distributed, the result is 10⁴The sub-simulations were averaged.

The first step, initializing the transmitting power of each secondary user: $p_i=\frac{1}{L-1}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)/h_i,i=1,\·\·\·,n,$ wherein: w is a_iPreference factor for secondary user i, n is number of secondary users, L is spread spectrum gain of cognitive user, T is interference threshold set by primary user, hi is channel gain from secondary user i to base station, sigma²Is background noise. λ is the equation: $\frac{1}{L-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)=T$ the solution of (1). The number of initialization iterations k is 1.

Second, if there is h for all i ∈ {1, …, n }, there is h_ip_i≤P_maxIs formed, wherein P_maxPricing of secondary users for maximum received power from each secondary user to the base station

Comprises the following steps:

transmission power of secondary user

Comprises the following steps:

and i is 1, …, n, and the pricing is finished. Otherwise, go to the third step.

The third step: when in use

When the number i is equal to k-1,

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

Is given by the equation

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

wherein, T_kIs a system of equations:

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

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

The fourth step: order to

Then the transmit power of the secondary user is: $p_i^{*}=p_{\mathrm{ij}}^{*},$ the pricing of the main user is as follows: ${\mathrm{\λ}}_i^{*}=\frac{{\mathrm{Lw}}_i}{{\mathrm{\Σ}}_{j\≠i}{h}_j{p}_j^{*}+{\mathrm{\σ}}^2+{\mathrm{Lh}}_i{p}_i^{*}},i=1,\·\·\·,n.$

the secondary user initialization power is as follows: $p_i=\frac{1}{L-1}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)/h_i,,$ i is 1,2, …, n.T is the primary user interference threshold, L is the secondary user spreading gain, h is the secondary user spreading gain_iChannel gain, σ, for sub-user i to base station²Is background noise. Where λ is given by the equation: $\frac{1}{L-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)=T$ and (4) giving.

The optimal pricing criterion of the master user is as follows: if the power to the base station is less than P for the used secondary users_maxThen the pricing of the master user is:

wherein:is the transmit power of the secondary user i. If there is at least one secondary user to base station power greater than P_maxThen the best possible transmit power is obtained by an iterative search as follows.

The iterative search is as follows:

when in use

When the number i is equal to k-1,

j＝1,…,i-1.

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

wherein T is_kIs a system of equations:

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

wherein, in the system of equations

Updating the iteration times to k +1 times;

the optimal pricing of the master user is as follows: ${\mathrm{\λ}}_i^{*}=\frac{{\mathrm{Lw}}_i}{{\mathrm{\Σ}}_{j\≠i}{h}_j{p}_j^{*}+{\mathrm{\σ}}^2+{\mathrm{Lh}}_i{p}_i^{*}},i=1,\·\·\·,n,$ wherein

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

in this embodiment, fig. 2 shows primary user profit graphs obtained by respectively adopting a proportional linear pricing method and the method of this embodiment; FIG. 3 is a graph of the total profit of the secondary user obtained by the proportional linear pricing method and the method of the present embodiment; fig. 4 is a graph of secondary users and rates obtained using the proportional linear pricing method and the method of the present embodiment, respectively. As can be seen from fig. 2: compared with a proportional linear pricing method, the implementation method obtains higher income of the main user. As can be seen from fig. 3: the total benefit of the secondary user obtained by the proposed method is higher than that obtained by the proportional linear pricing method, and as can be seen from fig. 4: the proposed method achieves a higher rate of secondary user summation than the proportional linear pricing method. The combination of fig. 2, fig. 3 and fig. 4 shows that the sum rate of the cognitive network is improved by the method compared with the traditional cost-based method. The method obtains the optimal pricing strategy of the master user, and the method can effectively solve related problems such as power control based on pricing in the cognitive network.

The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the 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： $p_i=\frac{1}{L-1}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)/h_i,i=1,\·\·\·,n,$

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 $\frac{1}{L-1}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\max (\sqrt{\frac{{\mathrm{Lw}}_i(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T+{\mathrm{\σ}}^2),0)=T$ 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 $p_{\mathrm{jk}}^{*}=\max (\frac{\sqrt{\frac{{\mathrm{Lw}}_j(T+{\mathrm{\σ}}^2)}{\mathrm{\λ}}}-(T_k+{\mathrm{\σ}}^2)}{L-1},0)/h_j,$ j is more than or equal to i; wherein the parameter T_kBy a system of equations

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

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.

Images