CN108541071B - Wireless communication system multi-user resource distribution system based on the double-deck game - Google Patents

Wireless communication system multi-user resource distribution system based on the double-deck game Download PDF

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CN108541071B
CN108541071B CN201810318244.2A CN201810318244A CN108541071B CN 108541071 B CN108541071 B CN 108541071B CN 201810318244 A CN201810318244 A CN 201810318244A CN 108541071 B CN108541071 B CN 108541071B
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resource allocation
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CN108541071A (en
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姜春晓
倪祖耀
匡麟玲
吴胜
葛宁
朱向明
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Tsinghua University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Computer Networks & Wireless Communication (AREA)
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  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The present invention provides the wireless communication system multi-user resource distribution systems based on the double-deck game, including evolutionary Game solves evolutionary Game solution module between module, group, information collection module, energetic optimum resource distribution module and Stackelberg game solution module in group；Information collection module is for collecting channel information；Evolutionary Game solves module for solving Evolutionary Equilibrium service selection result in user group in group；Evolutionary Game solves module for solving Evolutionary Equilibrium business result between different user group between group；Energetic optimum resource distribution module is for solving energetic optimum resource allocation and least energy loss result；Stackelberg game solves module and solves operator's maximum system effectiveness and optimal resource allocation structure according to the service selection result under different prices.The present invention obtains optimal pricing relationship, operator's optimal resource allocation structure between operator and user by solving the double-deck game, minimizes system capacity consumption, maximizes system benefit.

Description

Wireless communication system multi-user resource allocation system based on double-layer game
Technical Field
The invention relates to the technical field of communication, in particular to a wireless communication system multi-user resource allocation system based on double-layer game.
Background
With the development of mobile communications, the increasing demand for communications places greater and greater pressure on the communication networks. By 2020, the amount of data communicated is expected to increase more than 1000 times. Under the condition of limited resources, how to reasonably distribute resources among multiple users to realize the maximum utility of the system becomes a problem to be solved urgently.
Disclosure of Invention
In view of this, the present invention provides a multi-user resource allocation system for a wireless communication system based on a two-tier game, which obtains an optimal pricing relationship between an operator and a user and an optimal system resource allocation structure of the operator by solving the two-tier game, so as to minimize energy consumption of the system, maximize system benefits, and have low complexity.
In a first aspect, the embodiment of the invention provides a wireless communication system multi-user resource allocation system based on a double-layer game, which comprises an intra-group evolutionary game solving module, an inter-group evolutionary game solving module, an information collecting module, an energy optimal resource allocation module and a Starkelberg game solving module;
the information collection module is connected with the energy optimal resource allocation module and is used for collecting first channel information of a first user group and second channel information of a second user group;
the intra-group evolutionary game solving module is connected with the inter-group evolutionary game solving module and is used for solving a first service selection result under the condition of balanced user evolution in the first user group and the second user group;
the inter-group evolutionary game solving module is respectively connected with the energy optimal resource allocation module and the Starkelberg game solving module and is used for solving a second service selection result under the evolutionary equilibrium between a first user group and a second user group according to the first service selection result;
the energy optimal resource allocation module is connected with the Stark Burger game solving module and is used for obtaining an energy optimal resource allocation result and an energy loss result of the system according to the first channel information and the second channel information under the condition that the second service selection result is met;
and the Stark-Berger game solving module is used for obtaining the maximum system utility and the optimal resource allocation structure of the operator according to the second service selection result and the energy optimal resource allocation result under different pricing.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the information collecting module includes a first user group channel information collecting module and a second user group channel information collecting module.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the method further includes:
the first user group channel information collection module is used for collecting the first channel information;
and the second user group channel information collection module is used for collecting the second channel information.
With reference to the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the operator provides low-quality services and high-quality services.
With reference to the third possible implementation manner of the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the first service selection result includes a first user proportion and a second user proportion, where the first user proportion is a proportion of the first user group that selects the low-quality service, and the second user proportion is a proportion of the second user group that selects the low-quality service.
With reference to the fourth possible implementation manner of the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the intra-group evolutionary game solving module includes a first user group evolutionary game solving module and a second user group evolutionary game solving module.
With reference to the fifth possible implementation manner of the first aspect, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect, where the method further includes:
the first user group evolutionary game solving module is used for solving the first user proportion of the first user group under the condition that the second user proportion of the second user group is given;
the second user group evolutionary game solving module is used for solving the second user proportion of the second user group under the condition that the first user proportion of the first user group is given.
With reference to the first aspect, an embodiment of the present invention provides a seventh possible implementation manner of the first aspect, where the intra-group evolutionary game solving module solves the first service selection result through a dynamic price policy.
With reference to the fourth possible implementation manner of the first aspect, an embodiment of the present invention provides an eighth possible implementation manner of the first aspect, wherein the inter-group evolutionary game solving module solves the second service selection result according to a mutual function of the first user proportion and the second user proportion.
In a second aspect, an embodiment of the present invention provides a two-tier game-based wireless communication system multi-user resource allocation system, including the above-mentioned two-tier game-based wireless communication system multi-user resource allocation system, further including:
the information collection module is further configured to estimate channel information for the first group of users and the second group of users using the pilots.
The invention provides a wireless communication system multi-user resource allocation system based on a double-layer game, which comprises an intra-group evolutionary game solving module, an inter-group evolutionary game solving module, an information collecting module, an energy optimal resource allocation module and a Stark Berger game solving module; the information collection module is used for collecting channel information; the in-group evolution game solving module is used for solving the result of selecting the evolution balance service in the user group; the inter-group evolutionary game solving module is used for solving inter-group evolutionary equilibrium service results of different users; the energy optimal resource allocation module is used for solving the energy optimal resource allocation and minimum energy loss result; and the Stark-Berger game solving module solves the maximum system utility and the optimal resource allocation structure of the operator according to the service selection result under different pricing. According to the invention, the optimal pricing relation between the operator and the user and the optimal resource allocation structure of the operator are obtained by solving the double-layer game, so that the energy consumption of the system is minimized, and the system income is maximized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic diagram of a multi-user resource allocation system of a wireless communication system based on a two-tier game according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of game evolution equilibrium points of a first user group according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a system evolution process provided by an embodiment of the present invention;
fig. 4 is a comparison diagram of the total profit of the system according to the embodiment of the present invention.
Icon:
10-an information collection module; 20-an intra-group evolutionary game solving module; 30-a group evolution game solving module; 40-an energy optimal resource allocation module; and the 50-Stark Berger game solving module.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Currently, with the development of mobile communications, the increasing communication demands put more and more pressure on the communication network. By 2020, the amount of data communicated is expected to increase more than 1000 times. Under the condition of limited resources, how to reasonably distribute resources among multiple users to realize the maximum utility of the system becomes a problem to be solved urgently. Based on this, the multi-user resource allocation system of the wireless communication system based on the double-layer game provided by the embodiment of the invention obtains the optimal pricing relationship between the operator and the user and the optimal system resource allocation structure of the operator by solving the double-layer game, can minimize the energy consumption of the system, maximizes the system benefit, and has lower complexity.
The first embodiment is as follows:
in the future 5G communication, system delay becomes an important consideration of system performance, and operators will provide different delay guarantees for different users and services. Because the user can select different services according to the delay and the price of the services, an operator needs to make an optimal price decision, thereby maximizing the system utility. This process can be viewed as a typical Stark Boerg game, which has wide application in pricing and price-based resource allocation problems. The Stackelberg game model is a price leader model, two game parties play the roles of a leader and a follower respectively, and the follower follows the leader to make own decision after the leader makes a decision, so that own benefits are optimized.
Meanwhile, the development of mobile communication also brings complexity of user groups, and the user groups can have competition of communication resources from simple single structures to complex multi-user group structures. Evolutionary gaming is commonly used to model the competitive process between different populations. The evolutionary game is based on a biological evolution theory, and the game balance among different user groups is obtained by considering the competition relationship among the user groups which are not completely rational.
Based on the double-layer game model, the resource competition relationship between an operator and a user and between the user and the user can be effectively described, so that the system resources are further optimized.
Fig. 1 is a schematic diagram of a multi-user resource allocation system of a wireless communication system based on double-layer gaming.
Referring to fig. 1, the multi-user resource allocation system of the wireless communication system based on the double-layer game comprises an intra-group evolutionary game solving module 20, an inter-group evolutionary game solving module 30, an information collecting module 10, an energy optimal resource allocation module 40 and a starkeberg game solving module 50;
the information collection module 10 is connected with the energy optimal resource allocation module 40 and is used for collecting first channel information of a first user group and second channel information of a second user group;
the intra-group evolutionary game solving module 20 is connected with the inter-group evolutionary game solving module 30 and is used for solving a first service selection result under the equilibrium of user evolution in the first user group and the second user group;
the inter-group evolutionary game solving module 30 is respectively connected with the energy optimal resource allocation module 40 and the Starkelberg game solving module 50, and is used for solving a second service selection result under the evolutionary equilibrium between the first user group and the second user group according to the first service selection result;
the energy optimal resource allocation module 40 is connected with the starkeberg game solving module 50 and is used for obtaining an energy optimal resource allocation result and a system energy loss result according to the first channel information and the second channel information under the condition that a second service selection result is met;
and the stoker berg game solving module 50 is used for obtaining the maximum system utility and the optimal resource allocation structure of the operator according to the second service selection result and the energy optimal resource allocation result under different pricing.
Further, the information collection module comprises a first user group channel information collection module and a second user group channel information collection module.
Further, still include:
the first user group channel information collection module is used for collecting first channel information;
and the second user group channel information collection module is used for collecting second channel information.
Further, operators provide low quality services and high quality services.
Further, the first service selection result includes a first user ratio and a second user ratio, where the first user ratio is a ratio of selecting a low-quality service in the first user group, and the second user ratio is a ratio of selecting a low-quality service in the second user group.
Further, the intra-group evolutionary game solving module 20 includes a first user group evolutionary game solving module and a second user group evolutionary game solving module.
Further, still include:
the first user group evolutionary game solving module is used for solving the first user proportion of the first user group under the condition that the second user proportion of the second user group is given;
and the second user group evolutionary game solving module is used for solving the second user proportion of the second user group under the condition that the first user proportion of the first user group is given.
Further, the intra-group evolutionary game solving module 20 solves the first service selection result through a dynamic price strategy.
Further, the inter-group evolutionary game solving module 30 solves the second service selection result as a function of the first user ratio and the second user ratio.
Example two:
in the scheme of the embodiment of the invention, an operator is considered to provide two different services (low-quality service 1 and high-quality service 2), and the time delay of the two services is tau1>τ2Price of o1<o2. Meanwhile, consider that there are two user groups in the system, user group S ═ { u ═S,1,...uS,MAnd user population T ═ uT,1,...uT,K}. For two different services, different users will have different choices, denoted bS,m∈{1,2},bT,kE {1,2 }. It should be noted that the user group S and the user group T in the embodiment of the present invention correspond to the first user group and the second user group in the above embodiment.
The utility function of the operator consists of two parts, revenue and energy consumption:
Uope＝(πoOS+OT)-ηe(ES+ET) (1)
wherein,is the revenue from the user population S,is a benefit from the user population T, pio>1 is the price coefficient between different user groups, we assume that the price of the user group S is higher than the user group T. ESAnd ETη energy consumption to meet the selected business needs of the user population S and the user population TeIs the energy cost factor.
Because the user can select different services according to the delay and the price of the services, an operator needs to make an optimal price decision, analyze the competitive behavior among the users, reasonably distribute system resources, maximize the income from the users, and minimize the energy consumption of the system, thereby maximizing the system utility.
The embodiment of the invention can be divided into the following 5 modules: the system comprises an intra-group evolution game solving module 20, an inter-group evolution game solving module 30, an information collecting module 10, an energy optimal resource allocation module 40 and a Stark Berger game solving module 50.
The function of the intra-group evolutionary game solving module 20 is to solve the service selection result under the equilibrium user evolution in each user group. Comprises the following 2 parts: the system comprises a user group S evolution game solving module and a user group T evolution game solving module.
In traditional gaming theory, nash equilibrium points, where all users do not actively leave the equilibrium point, are generally treated as the system optimal solution. However, the traditional nash equilibrium point is based on the assumption of absolute rationality of users, which assumes that all users choose the choice that maximizes their utility. Such assumptions are not always correct, however, and in practical scenarios, rationality may be a more reasonable assumption. Therefore, the evolutionary game theory originated from biology is widely applied in more fields. In the evolutionary game, the system cannot reach the optimal state immediately, and the user can change the selection continuously to know that the evolutionary balance is reached. In the evolution equilibrium, the concept of a population is applied, wherein users with the same selection are uniformly regarded as one population, and the finally obtained evolution is balanced into the population ratios of different populations.
Considering the user's limited rationality, static pricing strategies may suffer a large loss of utility before the system reaches the equilibrium of evolution, especially if the evolution process is long. We therefore consider a dynamic pricing strategy: each time the operator receives a service selection from a user, the operator will dynamically adjust the price for the next service. We used kappaS∈[0,1],κT∈[0,1]The user proportion for selecting low-quality service in the user group S and the user group T is shown, and the user can obtain
o2,next＝ηupo2 (2)
ηup＝πup[ηup,1κS+ηup,2(1-κS)]+[ηup,1κT+ηup,2(1-κT)]. (3)
Wherein, ηup,1And ηup,2Is a price adjustment factor, pi, for two servicesup>1 is an additional adjustment factor for the user population S. naturally, we have ηup,1<1,ηup,2>1. Due to the dynamic price adjustment of the operator, the user also needs to take the price adjustment into account when deciding the service selection. We use rho epsilon [0,1 ]]The larger ρ is, the more the user pays attention to the current price and the adjusted price is not paid attention to. With q1,q2Representing the gain, q, obtained by the user from two delay services1<q2. The utility function of a user can be expressed as:
US,1＝q1-πoo1,US,2＝q2-πoo2[ρ+(1-ρ)ηup] (4)
UT,1＝q1-o1,UT,2＝q2-o2[ρ+(1-ρ)ηup] (5)
utility and price settings for each user, and other user preferencesAre all correlated. Different evolutionary games can be formed for different price settings, and the proportion kappa of different users can be obtained by solving the gamesS,κT。
For the user group S evolution game solving module, the function is to select kappa of the user group TTUnder the condition of constant value, solving the evolution equilibrium solution k of the user group SS。
The average utility of the user population S is:
evolution Rate of the user population S, i.e. kappaSCan be calculated using the replica dynamic equation as follows
The evolutionary equilibrium point of the system is the stable motionless point of the replication dynamic equation, at which point κSIs 0 and any small perturbation away from this point will return to this point. By solving for FS(κS) At 0, we can get the following fixed point:
wherein Δ q ═ q2-q1,Δηup＝ηup,2-ηup,1. When in useWhen the values of (a) are in different ranges, the evolution equilibrium points of the system have different results, and the analysis is as follows:
(1)as shown in FIG. 2(a), for any initial value κS∈(0,1)，κSWill eventually change to 0, and the stable equilibrium point of the system is κS＝0。
(2)As shown in FIG. 2(b), for any initial value κS∈(0,1)，κSWill eventually change to 1, and the stable equilibrium point of the system is κS＝1。
(3)As shown in FIG. 2(c), for any initial value κS∈(0,1)，κSWill eventually change intoThe stable equilibrium point of the system is
Once we getBy taking the value of (a), we can obtain a stable equilibrium solution k of the evolutionary game as described in realityS. However,is not independent, and is actually the T selection k of the user groupTAs a function of (c). Wherein,is equivalent to
In the same way as above, the first and second,is equivalent to Is equivalent toDue to kappaT∈[0,1]According to which we areAnd kappaTDiscussion of values of (K)SIs solved as follows:
(1)at this time, for any kappaTAll take values ofIs equivalent toTherefore we have κS＝0。
(2)At this time, for any kappaTAll take values ofIs equivalent toTherefore we have κS＝1。
(3)At this time, the process of the present invention,need to discuss kappa furtherTValue of
(a)At this time, the process of the present invention,we have
(b)At this time, the process of the present invention,we have a kappaS＝0。
(4)At this time, for any kappaTAll take values ofIs equivalent toTherefore we have
(5)At this time, the process of the present invention,need to discuss kappa furtherTValue of
(a)At this time, the process of the present invention,we have a kappaS＝1。
(b)At this time, the process of the present invention,we have
For the user group T evolutionary game solving module, the function is to select kappa of the user group SSUnder the condition of being regarded as a fixed value, the evolutionary equilibrium solution k of the user group T is solvedT。
Similar to the user group S evolution game solving module, the result is as follows:
(1)
(2)
(3)
(a)
(b)
(4)
(a)
(b)
(c)
(5)
(a)
(b)
wherein,andthe following can be analyzed and calculated according to the same method of the user group S:
the function of the inter-group evolutionary game solving module is to solve the service selection under the evolutionary equilibrium among the multi-user groups.
Under the condition of fixing selection of another user group, evolution equilibrium solutions of the user group S and the user group T are obtained respectively. However, in practice, the selection k of the user population S and the user population TSAnd kappaTAnd (3) as functions of each other, and the influence of the two functions is considered at the same time to solve the evolution equilibrium solution of the system.
Based on the above analysis, we can obtainAndthe relationship between them is as follows:
due to kappaSAnd kappaTIs thatAndaccording to which we areAndthe values of (a) discuss the equilibrium solution of the system. Since there are too many cases, we present some typical solution cases as follows:
(1)in this case, sinceWe can get the equilibrium solution of the system to (k)S＝0,κT＝0)。
(2)In this case, sinceWe can get the equilibrium solution of the system to (k)S＝1,κT＝1)。
(3)In this case, κSAnd kappaTAre functions of each other in the following relationship
(a)
(b)
As shown in fig. 3, we discuss the equalization solution of the system in 4 regions.
Region 1:in this case, κSTends to be 0, kTTend to beThe point in region 1 will tend to move to region 4, with no equalization points in region 1.
Region 2:in this case, κSTends to be 0, kTTending towards 0. The points in region 1 will tend to move to regions 1, 3, 4, and no equalization points in region 2.
Region 3:in this case, κSTend to beκTTending towards 0. So that the points in region 3 will tend to move to equilibrium points
Region 4:in this case, κSTend to beκTTend to beThe point in region 4 will tend to move to region 3 with no equalization points in region 4.
Based on the above analysis, we obtain the system stable equilibrium point as
Similarly, by analyzing all cases, we obtained the final system equilibrium solution as shown in table 1.
TABLE 1 systematic evolution equalization solution
The function of the information collection module is to collect channel information required by the system. Comprises the following 2 parts: the system comprises a user group S channel information collection module and a user group T channel information collection module.
(1) User group S channel information collecting module
The function of the user group S channel information collection module is to collect the channel information of the user group S. Using the pilots, the channel information of all user groups S is estimated.
(2) User group T channel information collecting module
The function of the user group T channel information collecting module is to collect the channel information of the user group T users. With the pilot, channel information of all user groups T is estimated.
The energy optimal resource allocation module has the functions of optimizing resource allocation among users and minimizing system energy consumption under the condition of meeting different user service choices.
Based on the evolutionary game solving module, the service selection under the equilibrium of the evolution among the multi-user groups under the condition of different service pricing is obtained. Based on the channel information collected by the information collection module, under the condition of meeting the service selected by different users, the resource allocation among all users is optimized, and the energy consumption of the system is minimized.
The function of the Stark Berger game solving module is to maximize the utility of the operator system according to the user service selection and the energy optimal resource allocation result under different pricing, and obtain an optimal resource allocation structure.
Based on phiSAnd phiTBy definition of (a), we can prove thatSAnd phiTValue of (a) is dependent on the price o2Is increased by an increase of, and isSuppose o2＝o1Is sometimes phiSLess than or equal to 0, when the operator increases the price o2The user's selection will go through the 5 stages shown in table 1. Since the number of users is a discrete value, the maximum effective use is maximized just before the user chooses to make a change. Since there are M + K users in the system in total, the number of users changing the selection at the same time will not exceed 1, so we only need to calculate the following M + K +1 cases.
(1)φS＝0。φSAll users with a quality less than or equal to 0 select high quality service, so the maximum efficiency is used in phiSIs reached when being equal to 0.
(2)0<φS<πup. In this caseAnd the users in part of the user group S change the selection to select the low-quality service. Since the number of users can only be an integer, we need only compute the following M-1 cases,
(3)φS≥πup,φTless than or equal to 1. In this case, all users of the user group S select low quality service, all users of the user group T select high quality service, and the maximum utility is phiTObtained when the product is 1.
(4)In this case, the users in the partial user group T change the selection and select low quality services. Similarly, since the number of users is an integer, we calculate K-1 cases.
(5)In this case, all users select low quality traffic and the system utility is unchanged.
By calculating all the conditions of M + K +1, the system finally obtains the Stark Berger game equilibrium point of the system realizing the maximum utility, and obtains the optimal price setting.
According to the embodiment of the invention, the optimal pricing relation between the operator and the user and the optimal system resource allocation structure of the operator are obtained by solving the double-layer game, so that the energy consumption of the system can be minimized, the system income can be maximized, and meanwhile, the complexity is lower.
Example three:
to simplify the model without loss of generality, the price of the low-quality service is defined as 1, and the gains of different delay services are calculated asη thereinqIs the business revenue factor. Price coefficient pio1.5, increasing the valence coefficient piupThe price adjustment influence coefficient ρ is 0.5, and the service profit difference Δ q is 3, the system gains under different price increasing coefficients are as follows, based on the two-tier game, the optimal resource optimization structure under different scenes is obtained, and the system gains are maximized, it is to be explained that, three curves in fig. 4, wherein, the uppermost curve corresponds to ηup,10.25, curve at the middle position corresponds to ηup,10.5, the lowest curve corresponds to ηup,1＝0.75。
The multi-user resource allocation system of the wireless communication system based on the double-layer game has the same technical characteristics as the above embodiments, so that the same technical problems can be solved, and the same technical effects can be achieved.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (8)

1. A multi-user resource allocation system of a wireless communication system based on a double-layer game is characterized by comprising an intra-group evolution game solving module, an inter-group evolution game solving module, an information collecting module, an energy optimal resource allocation module and a Stark Berger game solving module;
the information collection module is connected with the energy optimal resource allocation module and is used for collecting first channel information of a first user group and second channel information of a second user group;
the intra-group evolutionary game solving module is connected with the inter-group evolutionary game solving module and is used for solving a first service selection result under the condition of balanced user evolution in the first user group and the second user group;
the inter-group evolutionary game solving module is respectively connected with the energy optimal resource allocation module and the Starkelberg game solving module and is used for solving a second service selection result under the evolutionary equilibrium between a first user group and a second user group according to the first service selection result;
the energy optimal resource allocation module is connected with the Stark Burger game solving module and is used for obtaining an energy optimal resource allocation result and an energy loss result of the system according to the first channel information and the second channel information under the condition that the second service selection result is met;
the Stark-Berger game solving module is used for obtaining the maximum system utility and the optimal resource allocation structure of an operator according to the second service selection result and the energy optimal resource allocation result under different pricing;
the intra-group evolutionary game solving module solves the first service selection result through a dynamic price strategy;
and the inter-group evolutionary game solving module is used for solving the second service selection result by taking the first user proportion and the second user proportion as functions.
2. The system of claim 1, wherein the information collection module comprises a first user group channel information collection module and a second user group channel information collection module.
3. The multi-user resource allocation system for a dual-tier gaming-based wireless communication system of claim 2, further comprising:
the first user group channel information collection module is used for collecting the first channel information;
and the second user group channel information collection module is used for collecting the second channel information.
4. The dual-tier gaming-based wireless communication system multi-user resource allocation system of claim 1, wherein the operator provides low quality services and high quality services.
5. The system of claim 4, wherein the first service selection result comprises a first user ratio and a second user ratio, wherein the first user ratio is a ratio of the first user group to select the low-quality service, and the second user ratio is a ratio of the second user group to select the low-quality service.
6. The dual-tier game-based wireless communication system multi-user resource allocation system of claim 5, wherein the intra-group evolutionary game solving module comprises a first user group evolutionary game solving module and a second user group evolutionary game solving module.
7. The multi-user resource allocation system of a wireless communication system based on two-tier gaming according to claim 6, further comprising:
the first user group evolutionary game solving module is used for solving the first user proportion of the first user group under the condition that the second user proportion of the second user group is given;
the second user group evolutionary game solving module is used for solving the second user proportion of the second user group under the condition that the first user proportion of the first user group is given.
8. A multi-user resource allocation system of a wireless communication system based on double-deck gaming, comprising the multi-user resource allocation system of the wireless communication system based on double-deck gaming as claimed in any one of claims 1 to 7, further comprising:
the information collection module is further configured to estimate channel information for the first group of users and the second group of users using the pilots.
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