CN104010288A - Optimal power control method based on pricing in cognitive network - Google Patents
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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
Technical Field
The invention relates to the technical field of power control in a cognitive network, in particular to an optimal power control method based on pricing in the cognitive network.
Background
Under the cognitive network, the secondary user can access the frequency spectrum for data transmission by leasing the frequency spectrum of the primary user. In order to guarantee the service quality of the primary user and obtain the incentive of leasing the frequency spectrum, the primary user can charge for the interference of the secondary user. Firstly, a master user sets a certain price for the unit interference power of each secondary user, and broadcasts the price to each secondary user; the secondary user controls its power through the non-cooperative game based on the price. This interaction between the primary and secondary users can be analyzed by the Stackelberg game. The pricing of the primary user will affect the power of the secondary users, thereby affecting the income of the users. Therefore, in order to ensure that the total interference of the secondary user to the primary user is less than the interference threshold and maximize the benefit of the primary user, the primary user needs to adopt a reasonable pricing strategy to control the power of the secondary user. Through a recursive method, a function of a revenue function of a primary user can be expressed as a non-convex optimization function of the secondary user power, so that the traditional convex optimization algorithm cannot be directly used for finding the optimal pricing of the primary user and the optimal power of the secondary user.
In recent years, research for controlling power of secondary users based on pricing methods 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 2010IEEE Transactions on vehicle Technology, May.2010, vol.59, No.4, pp.1769-1778. The article considers the situation where both primary and secondary users are in one serving base station, and the secondary user can be allowed to access the base station by paying for the interference of the primary user under a certain interference threshold. As the maximum utility function of the main user is non-convex, the strategy of the main user is set to be a certain linear proportional relation by the article, and a suboptimal pricing algorithm is obtained by the method. Meanwhile, the base stations accessed by the primary user and the secondary user in the system model are the same base station, and the method cannot be directly expanded to the situation that the base stations of the secondary user and the primary user are different.
An article entitled "Price-based resource for spread-sharing femtocell networks" A stackelberg gate address "was published by Xin Kang et al, 2012IEEE Journal on Selected Areas in Communications, Apr.vol.30, No.3, pp.538-549. The model provided by the article expands the models of Hui Yu and the like, and base stations of a primary user and a secondary user can be different. A non-uniform pricing model is proposed to maximize revenue for primary users. Because the algorithm decouples the mutual interference between the secondary users by the worst interference of the secondary users, so as to omit the mutual interference links between the secondary users, the power control method obtained by the pricing method is suboptimal and cannot maximize the benefit of the primary user.
It can be known from related research that, in order to maximize the benefit of the primary user and ensure that the total interference of the secondary users is less than the interference threshold, the primary user needs to use a certain pricing for each secondary user to control the power of the secondary user. The invention provides an optimal power control method for maximizing the income of the main user based on expressing a non-convex function of a utility function of the main user as an equivalent convex optimization problem through variable replacement and based on the equivalent convex optimization problem.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an optimal power control method based on pricing in a cognitive network. The invention provides an optimal power control method for maximizing the income of a primary user aiming at the power control method based on pricing in the existing cognitive network and capable of obtaining the optimal power of the secondary user and maximizing the income of the primary user. The method and the device can enable the primary user to find the optimal power of the secondary user and the optimal pricing of the primary user through iteration for a plurality of times to the secondary user under the condition that the channel information and the preference factor of the secondary user are known. The pricing method finds the optimal power of the secondary user on the basis of the relationship between the optimal solution and variable replacement of the equivalent convex optimization problem for maximizing the income of the primary user on the premise that the interference of the secondary user on the primary user is smaller than a given interference threshold, improves the income of the primary user compared with the traditional non-uniform pricing method, and can allow more users to access the frequency spectrum.
The optimal power control method based on pricing in the cognitive network provided by the invention comprises the following specific steps:
the first step is as follows: initializing the number of secondary users and a secondary user tolerance factor;
the second step is that: sorting the secondary user tolerance factors in descending order;
the third step: calculating a judgment factor of each secondary user;
the fourth step: if the allowable factor of the last secondary user is larger than the judgment factor of the last secondary user, giving the optimal power of the secondary user and the pricing method of the primary user, and ending the method;
the fifth step: and for the fourth step, if the tolerance factor of the last secondary user is less than or equal to the judgment factor, setting the power of the last secondary user to be zero, turning to the fourth step, comparing the tolerance factors of the rest secondary users with the judgment factors of the rest secondary users until the conditions are met, calculating the optimal power and giving the pricing of the primary user.
Preferably, in the first step, in particular, the number K of sub-users n, the tolerance factor a of the sub-user i is initializedi：
Wherein, wiIs 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, hiIs the channel gain, g, from the secondary user i to the base station itselfiIs the channel gain, σ, from the secondary user i to the receiving end of the primary user2Is the sum of interference power and noise of the primary user to the secondary user base station.
Preferably, in the second step, the sorting mode of the secondary users is as follows: alpha is alpha1≥...≥αKIn which α isKIs the tolerance factor for the secondary user K.
Preferably, in the third step, in particular, for the secondary users i, i ═ 1iThe values of (A) are as follows:
wherein: l is the spread spectrum gain of the cognitive user, T is the interference threshold of the primary user, hjIs the channel gain, g, from the secondary user j to its base stationjIs the channel gain, σ, from the secondary user j to the receiving end of the primary user2Is the sum of interference power and noise of a primary user to a secondary user base station, wjIs the preference factor for secondary user j.
Preferably, in a fourth step, in particular if the tolerance factor α of the secondary user K isKGreater than its decision factor betaKThen optimal power of the secondary user iComprises the following steps:
wherein: sigma2Is the sum of interference power and noise of a primary user to a secondary user base station, hiIs the channel gain from the secondary user i to its base station;
pricing of primary usersComprises the following steps:
wherein,for the optimum power of the secondary user i,the algorithm ends for the optimal power for secondary user j.
Preferably, in a fifth step, in particular if the tolerance factor α of the secondary user K isKNot greater than its decision factor betaKAnd if so, the optimal power of the secondary user K is zero, assigning K-1 to K, returning to the step four, and setting the optimal power and pricing of the remaining K secondary users.
Compared with the prior art, the invention has the following beneficial effects:
the method converts the non-convex optimization problem of the income of the primary user into an equivalent convex optimization problem through variable replacement, and provides a closed solution of the optimal power of the secondary user and the optimal pricing of the primary user through the optimality condition of the convex optimization problem. Rather than finding a sub-optimal set of pricing, more traditional non-uniform pricing-based methods can find pricing that maximizes revenue for primary users. The method provided by the invention can increase the number of the secondary users allowed to access while improving the income of the primary users. The algorithm has an analytical expression, so that the execution speed is high, and the feasibility and the practicability are better.
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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 diagram of a simulation system model provided in accordance with the present invention;
FIG. 2 is a graph of primary user revenue as the interference-to-noise ratio increases from-40 dB to 40dB in accordance with the present invention;
FIG. 3 is a graph of the number of secondary users allowed to access when the interference-to-noise ratio is increased from-40 dB to 40dB according to the present invention;
FIG. 4 is a schematic flow chart of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
The invention discloses an optimal power control method based on pricing in a cognitive network, which comprises the following steps: initializing the number of secondary users and tolerance factors; sorting the secondary users in a descending order according to the tolerance factors of the secondary users; calculating a judgment factor of a secondary user; comparing the size relationship between the judgment factor and the allowable factor, and if the conditions are met, giving the optimal power of the secondary user and the pricing method of the primary user, and ending the method; otherwise, the power of the secondary user with the lowest tolerance factor is set to be zero, and the rest secondary users continue to execute according to the fourth step until the conditions are met, and the optimal power of the secondary user and the pricing method of the primary user are given. The method can give the closed solution of the optimal power distribution of the secondary users and the optimal pricing of the primary users by iteration for not more than the total number of the secondary users under the condition that the primary users know the allowable factors of the secondary users and the channel information of the whole cognitive network, and can increase the income of the primary users and allow more secondary users to access the frequency spectrum.
The embodiment is an optimal power control scheme based on pricing, the number of secondary users is 10, the secondary users are distributed in a 200m range of a base station, the distance from a receiving end of a primary user to the base station is 300m, and the sum of interference power and noise of the primary user to the base station of the secondary users is sigma2＝10-12W, spreading gain L of secondary user 128, channel gain of secondary user i to own base stationThe channel gain from the secondary user i to the primary user receiving end isWherein A shows the influence of parameters such as antenna gain and carrier spectrum of the system, and takes a value of 103；αiAnd betaiIs a gaussian distribution meeting a zero mean with a standard deviation of 6 dB; diAnd siThe distances from the secondary users to the base station and to the receiving end of the primary user are respectively, and the interference noise ratio is increased from-40 dB to 40 dB.
In the first step, the number K of each secondary user is initialized to 10, and the tolerance factor a of the secondary useri：
Wherein, wiIs 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, hiIs the channel gain, g, from the secondary user i to the base station itselfiIs the channel gain, σ, from the secondary user i to the receiving end of the primary user2Is the sum of interference power and noise of the primary user to the secondary user base station.
And secondly, sorting all secondary users in a descending order: alpha is alpha1≥...≥αKIn which α isKIs the tolerance factor for the secondary user K.
The third step: calculating a decision factor beta for a secondary user ii：
Wherein L is the spread spectrum gain of the cognitive user, T is the interference threshold of the primary user, hjIs the channel gain, g, from the secondary user j to its base stationjIs the channel gain, σ, from the secondary user j to the receiving end of the primary user2Is the sum of interference power and noise of a primary user to a secondary user base station, wjIs the preference factor for secondary user j.
The fourth step: for secondary users K, e.g. if αK＞βKIs formed, wherein aK,βKThe tolerance factor and the decision factor of the sub-user K, respectively, the optimal power of the sub-user i is:
wherein, aiBy the equation <math>
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</math> Determining that L is the spread spectrum gain of the cognitive user, T is the interference threshold of the primary user, and hiIs the channel gain, g, from the secondary user i to the base station itselfiIs the channel gain, σ, from the secondary user i to the receiving end of the primary user2Is the sum of interference power and noise of a primary user to a secondary user base station, betaKIs the decision factor for the secondary user K. Pricing of primary usersi is 1, …, n, whereinFor the optimum power of the secondary user i,to optimize the power for the next j, the algorithm ends.
The fifth step: for the secondary user K, if αK≤βKIs formed, wherein aK,βKThe admission factor and decision factor for the sub-user K, respectively, then the power of the kth sub-user is set to 0, let K: ═ K-1, and go back to the fourth step.
In this embodiment, fig. 1 is a model diagram of a simulation system of the present invention, in which n secondary users need to pay for interference of a primary user to access a base station, and the primary user sets different interference prices for different secondary users to control total interference of the secondary users to be smaller than an interference threshold. FIG. 2 shows graphs of revenue of a primary user obtained by using a non-uniform pricing method and the method of the present embodiment, respectively; fig. 3 is a graph of the number of secondary users allowed by the system using the non-uniform pricing method and the method of the present embodiment, respectively. As can be seen from fig. 2: the main users obtained by the two methods increase along with the increase of the interference noise ratio, the provided implementation method obtains higher main user income compared with a non-uniform pricing method, and when the interference noise ratio is 40dB, the main user income obtained by the provided method is 1.5 times higher than that obtained by the non-uniform pricing method. As can be seen from fig. 3: the proposed method can allow more secondary users to access the frequency spectrum of the primary user than the non-uniform pricing method, and when the interference noise ratio is 40dB, the number of the secondary users allowed to access the frequency spectrum by the proposed method is 25% higher than that of the non-uniform pricing method. It can be known from fig. 2 and fig. 3 that the proposed method improves the revenue of the primary user compared with the power control method with non-uniform pricing, and allows more secondary users to access the spectrum. The method can obtain the optimal power of the secondary user and the closed solution of the pricing of the primary user, and can effectively solve the related problems of power control and the like based on a price mechanism 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 (6)
1. A pricing-based optimal power control method in a cognitive network is characterized by comprising the following specific steps:
the first step is as follows: initializing the number of secondary users and a secondary user tolerance factor;
the second step is that: sorting the secondary user tolerance factors in descending order;
the third step: calculating a judgment factor of each secondary user;
the fourth step: if the allowable factor of the last secondary user is larger than the judgment factor of the last secondary user, giving the optimal power of the secondary user and the pricing method of the primary user, and ending the method;
the fifth step: and for the fourth step, if the tolerance factor of the last secondary user is less than or equal to the judgment factor, setting the power of the last secondary user to be zero, turning to the fourth step, comparing the tolerance factors of the rest secondary users with the judgment factors of the rest secondary users until the conditions are met, calculating the optimal power and giving the pricing of the primary user.
2. The method for optimal power control based on pricing in cognitive network according to claim 1, wherein in the first step, specifically, initializing the number of sub-users K-n, the tolerance factor a of sub-user ii：
Wherein, wiIs 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, hiIs the channel gain, g, from the secondary user i to the base station itselfiIs the channel gain, σ, from the secondary user i to the receiving end of the primary user2Interference power and noise of primary user to secondary user base stationAnd (4) summing.
3. The method for controlling optimal power based on pricing in cognitive network as claimed in claim 2, wherein in the second step, the secondary users are ranked in the following way: alpha is alpha1≥...≥αKIn which α isKIs the tolerance factor for the secondary user K.
4. A method for pricing-based optimal power control in cognitive networks according to claim 3, characterized in that in the third step, specifically, the decision factor β for the secondary users i, i 1iThe values of (A) are as follows:
wherein: l is the spread spectrum gain of the cognitive user, T is the interference threshold of the primary user, hjIs the channel gain, g, from the secondary user j to its base stationjIs the channel gain, σ, from the secondary user j to the receiving end of the primary user2Is the sum of interference power and noise of a primary user to a secondary user base station, wjIs the preference factor for secondary user j.
5. Method for optimal power control based on pricing in cognitive networks according to claim 4, characterized in that in the fourth step, in particular if the admission factor α of the secondary user K isKGreater than its decision factor betaKThen optimal power of the secondary user iComprises the following steps:
wherein: sigma2Is the sum of interference power and noise of a primary user to a secondary user base station, hiIs the channel gain from the secondary user i to its base station;
pricing of primary usersComprises the following steps:
wherein,for the optimum power of the secondary user i,the algorithm ends for the optimal power of secondary user j.
6. Method for optimal power control based on pricing in cognitive networks according to claim 4, characterized in that in the fifth step, in particular if the admission factor α of the secondary user K isKNot greater than its decision factor betaKAnd if so, the optimal power of the secondary user K is zero, assigning K-1 to K, returning to the step four, and setting the optimal power and pricing of the remaining K secondary users.
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