CN111639993B - Mobile data unloading and pricing method based on multi-item auction mechanism - Google Patents

Mobile data unloading and pricing method based on multi-item auction mechanism Download PDF

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CN111639993B
CN111639993B CN202010471637.4A CN202010471637A CN111639993B CN 111639993 B CN111639993 B CN 111639993B CN 202010471637 A CN202010471637 A CN 202010471637A CN 111639993 B CN111639993 B CN 111639993B
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mobile
bandwidth
price
bandwidth resources
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CN111639993A (en
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刘永文
张素智
范艳焕
王博
孙玉胜
武丰龙
梁辉
马欢
陈锐
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Zhengzhou University of Light Industry
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/08—Auctions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0611—Request for offers or quotes
    • 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

Abstract

The invention relates to a mobile data unloading and pricing method based on a multi-item auction mechanism, wherein all mobile users provide unit bandwidth resource bidding prices of different WiFi hot spots covered by the same base station, and each mobile user also needs to provide the information of the total bandwidth resources required; the mobile operator adopts a multi-article auction mechanism to allocate and price bandwidth resources according to the bidding price of each user, the required bandwidth resource quantity, the historical maximum payment amount of each user, the historical bidding result prices of different WiFi hotspots and the bandwidth resource condition of the current WiFi hotspots; some mobile users bid successfully, obtain the required bandwidth resources, pay the corresponding bandwidth resource and use fees. The mobile data unloading and pricing method can allocate WiFi bandwidth resources among a plurality of mobile users, and reasonable pricing strategies are designed to maximize benefits of mobile operators.

Description

Mobile data unloading and pricing method based on multi-item auction mechanism
Technical Field
The invention relates to the technical field of wireless communication, in particular to a mobile data unloading and pricing method based on a multi-item auction mechanism.
Background
With the rapid growth in the number of mobile users and the widespread popularity of mobile applications, mobile data traffic has exhibited explosive growth. According to the report "Cisco visual network index" published by Cisco (Cisco) in 2019: global mobile data traffic prediction, update edition 2017 to 2022, 17-fold increase in mobile data traffic from 2012 to 2017; from 2017 to 2022, the mobile data traffic would increase 7-fold. The rapid growth of mobile data traffic presents a significant challenge for mobile cellular networks. The rapidly increasing mobile data traffic exceeds the load capacity of conventional cellular networks, increasing network transmission delay and reducing network quality of service. To address these challenges, one solution is to increase the load capacity of the cellular network by purchasing and placing radio access network devices and core infrastructure, but these investments greatly increase the service costs of the mobile operators. Another solution is to stream mobile data traffic to other types of wireless networks, such as WiFi hotspots, to address the problem of cellular network traffic overload. This manner of facilitating mobile data transmission with other wireless networks is known as data-offloading (data-streaming) technology.
While mobile data offloading can significantly reduce cellular network data traffic, the task of designing a comprehensive and reliable mobile data offloading scheme is challenging. One key challenge is how to achieve efficient data offload coordination among multiple mobile devices. When multiple mobile users are covered by multiple WiFi hotspots, the following issues need to be considered: 1) How to realize the matching of the mobile user and the WiFi hot spot, namely, which WiFi hot spot serves a certain mobile user; 2) How to decide how to acquire the amount of bandwidth resources for the mobile user; 3) How to price bandwidth resources acquired by a mobile user; 4) How to obtain the maximum data unloading performance, namely the problem of maximizing the bandwidth resource utilization rate of the WiFi hot spot; 5) How to guarantee that the pricing mechanism can guarantee that the mobile operator achieves the maximum revenue. By utilizing WiFi hotspots, mobile users can obtain better radio access services at lower cost. Meanwhile, the mobile operators deployed with WiFi hotspots can realize the maximum benefit by selling bandwidth resources.
When the bandwidth requirements of the mobile device exceed the limited WiFi bandwidth resources, the mobile operator needs to allocate bandwidth to the mobile device and determine the price of the allocated bandwidth. Auction mechanisms are considered to be a cost-effective way to allocate bandwidth resources that can be allocated to the highest bidding mobile users. In the actual data offloading process, the auction price of the mobile user is private information that the mobile operator cannot learn. Thus, many auction mechanisms assume that the auction price of a user is the highest price that it can afford, which can simplify the difficulty of implementation of the auction mechanism. However, the actual mobile users have a high degree of freedom and may not participate in the auction in this way, resulting in that these auction mechanisms do not create maximum revenue for the mobile operator.
Disclosure of Invention
The invention aims to solve the technical problems and provide a mobile data unloading and pricing method based on a multi-item auction mechanism. WiFi bandwidth resources can be allocated among multiple mobile users while rationally designed pricing policies to maximize mobile operator benefits.
The invention solves the technical problems, and adopts the following technical scheme: a mobile data offloading and pricing method based on a multi-item auction mechanism, comprising:
all mobile users provide bidding prices of unit bandwidth resources of different WiFi hot spots covered by the same base station, and each mobile user also needs to provide information of total bandwidth resources required;
the mobile operator adopts a multi-article auction mechanism to allocate and price bandwidth resources according to the bidding price of each user, the required bandwidth resource quantity, the historical maximum payment amount of each user, the historical bidding result prices of different WiFi hotspots and the bandwidth resource condition of the current WiFi hotspots;
some mobile users bid successfully, obtain the required bandwidth resources, pay the corresponding bandwidth resource and use fees.
The invention is used for optimizing a mobile data unloading and pricing method based on a multi-item auction mechanism: the multi-item auction mechanism includes the following three principles:
1) Rationality principles, the payment per user cannot exceed the obtainedOf (i) i.e.
Figure SMS_1
2) Budget feasibility principle, the payment per user cannot exceed the budget total, i.e
Figure SMS_2
3) Incentive compatibility principle, ensuring that the user can obtain maximum benefit when providing real auction price, namely
Figure SMS_3
wherein />
Figure SMS_4
Representing the real auction price +_>
Figure SMS_5
Representing an arbitrary auction price;
the above auction mechanism is based on the following definition:
defining a set of mobile users participating in a multi-item auction as
Figure SMS_6
Defining a set of WiFi hot spots to participate in a multi-item auction
Figure SMS_7
These WiFi hotspots belong to the same mobile operator;
defining mobile users
Figure SMS_8
In the course of an auction, wiFi hotspots are +.>
Figure SMS_9
Valuation of v ij ；
Define the valuation matrix as v= { v ij I.e N, j.e M, the valuation matrix consists of all valuations
Defining the estimate vector of WiFi hotspot j as v j ＝(v 1j ，...，v nj ) Estimation ofThe vector is an estimate of WiFi hotspot j for all mobile users;
defining the maximum funds for a mobile user as B i ，i∈N；
Defining the maximum bandwidth resource provided by WiFi hot spot as
Figure SMS_10
Defining an uncertainty set describing the mobile user valuation, the mobile operator would define an uncertainty set for each WiFi hotspot, the uncertainty set requiring all possible results including a valuation matrix, formalized as: estimation vector v j ∈
Figure SMS_11
The method comprises the steps of carrying out a first treatment on the surface of the Evaluation matrix->
Figure SMS_12
Figure SMS_13
Is defined as the following formula:
Figure SMS_14
wherein μj and δj The expected value and the variance are obtained according to the historical valuation information of the WiFi hotspot j, and tau is a parameter for controlling the conservation degree of the historical valuation information;
defining decision variables of mobile operators and distributing the decision variables
Figure SMS_15
Representing the amount of bandwidth resources allocated by each mobile user from different WiFi hotspots under the condition that the evaluation matrix is V, paying for decision variable +.>
Figure SMS_16
Representing the cost each mobile user ultimately pays for using bandwidth resources if the valuation matrix is V. />
As a mobile data unloading and pricing method based on a multi-item auction mechanismIs optimized by: the auction mechanism is realized by solving the following optimization problem, the objective of the optimization problem is to realize the maximization of the profit of the operator, and the problem solving result is to decide an allocation decision result and a payment decision result, namely, in the feasible domain defined by the constraint conditions of the following optimization problem, the allocation decision variable x which enables the final benefit R of the operator to be maximum is found v And a payment decision variable p v ；
The optimization problem constraint conditions are defined as:
1)，
Figure SMS_17
the operator ultimately benefits from not exceeding the user's payment sum;
2)，
Figure SMS_18
the total payment amount of each user cannot exceed the estimated total amount of the bandwidth resource used by the user, the user only pays the bandwidth use price approved by the user, and the user cannot pay the price exceeding the expected value or larger than the estimated value;
3)，
Figure SMS_19
the total amount paid by each user cannot exceed the individual budget;
4)，
Figure SMS_20
the bidding returns of each user with real valuations are higher than those with non-real valuations;
5)，
Figure SMS_21
the quantity of bandwidth resources allocated by each WiFi hotspot cannot exceed the total quantity of the existing bandwidth resources;
6)，
Figure SMS_22
the total bandwidth resource amount obtained by each user cannot exceed the bandwidth resource amount applied by the user;
7)，
Figure SMS_23
the allocation decision variable cannot be negative;
8)，
Figure SMS_24
the payment decision variable cannot be negative.
The invention is used for optimizing a mobile data unloading and pricing method based on a multi-item auction mechanism: and decomposing the optimization problem into an initial distribution process and a final distribution process, and sequentially solving.
The invention is used for optimizing a mobile data unloading and pricing method based on a multi-item auction mechanism: the initial allocation process solves two sub-optimization problems, the first sub-optimization problem being solved with the objective of obtaining the result that
Figure SMS_25
The constraint for x, v, which takes the maximum value, is defined as follows:
1)，
Figure SMS_26
the estimated total amount of bandwidth used by each user cannot exceed its paid total amount;
2)，
Figure SMS_27
the quantity of bandwidth resources allocated by each WiFi hotspot cannot exceed the total quantity of current bandwidth resources;
3)
Figure SMS_28
the total amount of bandwidth resources available to each user cannot exceed the amount of bandwidth resources that it requires. />
4)，
Figure SMS_29
The estimated total amount of bandwidth used by each user is the minimum of all possible bidding conditions;
the initial allocation decision result x can be obtained by solving the first constraint variable * And resulting in a worst price bidding matrix W;
the second sub-optimization problem of the initial allocation process is used to calculate the reserve price, r, which can be found by solving the dual problem of the first sub-optimization problem of the initial allocation process * 。
The invention is used for optimizing a mobile data unloading and pricing method based on a multi-item auction mechanism: the final allocation process solves two sub-optimization problems, the first sub-optimization problem being solved with the objective of computing such that
Figure SMS_30
Taking the maximum value y v The constraints of this problem are as follows:
1)，
Figure SMS_31
the secondary allocated bandwidth quantity of each WiFi hotspot is smaller than the difference between the total quantity of the existing bandwidths and the quantity of the initial allocated bandwidths;
2)，
Figure SMS_32
the sum of the secondary payments of each user is less than the difference between the personal total budget and the primary maximum sum of payments;
3)，
Figure SMS_33
the quantity of bandwidth resources acquired by each user is smaller than the difference between the total required quantity and the primary distribution quantity;
the second sub-optimization problem of the final allocation process is to calculate the resulting
Figure SMS_34
Taking the maximum value y v-k The constraints of this problem are defined as follows:
1)，
Figure SMS_35
the secondary allocated bandwidth amount of each WiFi hotspot is smaller than the difference between the total existing bandwidth amount and the initial allocated bandwidth amount without participation of a user k.
2)，
Figure SMS_36
Without the participation of user k, the sum of the secondary payments per user is less than the difference between the individual total budget and the primary maximum sum of payments.
3)，
Figure SMS_37
The number of bandwidth resources acquired by each user is smaller than the difference between the total required number and the initial allocation number without the participation of the user k.
The invention is used for optimizing a mobile data unloading and pricing method based on a multi-item auction mechanism: calculating the distribution result and payment result of each user by solving two sub-optimization problems of the initial distribution process and two sub-optimization problems of the final distribution process, wherein the calculation formula of the distribution result is a v ＝x * +y v The final payment result is the sum of the initial bandwidth allocation usage fee and the secondary bandwidth allocation usage fee minus the mobile operator profit, and the calculation formula of the payment result is that
Figure SMS_38
The invention is used for optimizing a mobile data unloading and pricing method based on a multi-item auction mechanism: the method specifically comprises the following steps:
s1: the mobile user submits bandwidth resource quantity request information D and corresponding unit bandwidth resource bidding information v;
s2: the mobile operator invokes mobile user historical bidding information B and WiFi hotspot historical bidding pricing information, wherein the WiFi hotspot historical bidding pricing information comprises a historical average selling price mu and a variance delta of unit bandwidth resources of the WiFi hotspots.
S3: the mobile operator solves the bilinear optimization problem according to the historical bidding information of the mobile user and the historical sales information of the WiF hot spot to obtain an initial allocation decision result x * And a worst bidding price matrix W;
s4: the mobile operator decides the result x according to the initial allocation * And calculating reserved price r by using worst bidding price matrix W * ；
S5: the mobile operator decides the result x according to the initial allocation * And reserve price r * Calculating a quadratic distribution decision variable y v ；
S6: the mobile operator decides the result x according to the initial allocation * And a quadratic distribution decision variable y v Calculating the final distribution result a v ；
S7: the mobile operator calculates a secondary allocation decision variable under each winning bid user;
s8: the mobile operator calculates the final payment of the winning subscriber.
Advantageous effects
1. The mobile data unloading and pricing method can realize data unloading maximization, namely, a mobile operator can allocate bandwidth resources of WiFi hot spots to mobile users to the maximum. The invention reasonably distributes the bandwidth resources of the WiFi hot spot to the mobile user by means of two constraint conditions that the total bandwidth resources obtained by each user cannot exceed the applied bandwidth resources and the bandwidth resources distributed by each WiFi hot spot cannot exceed the existing bandwidth resources, and simultaneously realizes the maximum distribution of the bandwidth resources to the user by means of a pricing method of a multi-article auction mechanism, thereby finally achieving the aim of maximizing data unloading.
2. The mobile data unloading and pricing method can promote the mobile users to participate in the auction, and the invention ensures the mobile users to participate in the auction rationally by limiting the condition that the total payment sum of each user cannot exceed the estimated total sum of the bandwidth resources used by the users, the users only pay the bandwidth use price approved by the users and cannot pay the price exceeding the expected or larger than the estimated value.
3. The mobile data unloading and pricing method can prevent the mobile user from maliciously operating and damaging the auction mechanism, and the invention specifically prevents the mobile user from maliciously operating and damaging the auction mechanism by constraint conditions that the price of each bidding with real valuation is higher than that with non-real valuation, namely an incentive compatibility principle, because the users cannot obtain the best price with the bidding with non-real valuation, and can effectively stop the malicious price pressing auction of the users.
4. The mobile data unloading and pricing method can ensure that the auction price of the mobile user is in the bearable range, and the invention ensures that the auction price of the mobile user is in the bearable range through the constraint condition that the total payment amount of each user cannot exceed the total personal budget amount, namely the budget feasibility principle.
Drawings
FIG. 1 is a schematic diagram of an auction model in a mobile data unloading and pricing method according to the present invention;
FIG. 2 is a flow chart of a mobile data offloading and pricing method of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Optimization of mobile data offloading and pricing methods based on multi-item auction mechanisms: the method specifically comprises the following steps:
1. an auction system role is defined.
Defining a set of mobile users participating in a multi-item auction as
Figure SMS_39
Defining a set of WiFi hot spots to participate in a multi-item auction
Figure SMS_40
These WiFi hotspots belong to the same mobile operator;
defining mobile users
Figure SMS_41
In the course of an auction, wiFi hotspots are +.>
Figure SMS_42
Valuation of v ij ；
Define the valuation matrix as v= { v ij I e N, j e M, the valuation matrix consists of all valuations;
defining the estimate vector of WiFi hotspot j as v j ＝(v 1j ，...，v nj ) The estimated value vector is the estimated value of the WiFi hotspot j by all mobile users;
defining the maximum funds for a mobile user as B i ，i∈N；
Defining the maximum bandwidth resource provided by WiFi hot spot as
Figure SMS_43
2. An uncertainty set describing the mobile user valuation is defined. Because the mobile user's rating matrix is not visible to the mobile operator, the mobile operator uses an uncertainty set to predict the mobile user's rating matrix. The mobile operator will define an uncertainty set for each WiFi hotspot that needs to include all possible results of the rating matrix, formalized as: estimation vector v j ∈u j The method comprises the steps of carrying out a first treatment on the surface of the Estimation matrix
Figure SMS_44
u j Is defined as the following formula:
Figure SMS_45
wherein μj and δj The expected value and the variance are obtained according to the historical valuation information of the WiFi hotspot j, and tau is a parameter for controlling the conservation degree of the historical valuation information;
3. defining decision variables of mobile operators and distributing the decision variables
Figure SMS_46
Representing the amount of bandwidth resources allocated by each mobile user from different WiFi hotspots under the condition that the evaluation matrix is V, and paying for decision variables
Figure SMS_47
Representing the cost each mobile user ultimately pays for using bandwidth resources if the valuation matrix is V.
4. Auction rules for the mobile operator are defined.
1) Rational principles, the payment of each user cannot exceed the gain obtained, i.e
Figure SMS_48
2) Budget feasibility principle, the payment per user cannot exceed the budget total, i.e
Figure SMS_49
3) Incentive compatibility principle, ensuring that the user can obtain maximum benefit when providing real auction price, namely
Figure SMS_50
wherein />
Figure SMS_51
Representing the real auction price +_>
Figure SMS_52
Representing an arbitrary auction price; />
5. For the definition, a robust optimization method is designed to realize a multi-item auction mechanism, namely the following optimization problem is required to be solved. The problem solving aim is to maximize the income of operators, and the problem solving result is a decision distribution decision result and a payment decision result. I.e. within the feasible domain defined by the following optimization problem constraints, find the allocation decision variable x that maximizes the operator's final benefit R v And a payment decision variable p v ；
The optimization problem constraint conditions are defined as:
1)，
Figure SMS_53
the operator ultimately benefits from not exceeding the user's payment sum;
2)，
Figure SMS_54
the total payment amount of each user cannot exceed the estimated total amount of the bandwidth resource used by the user, the user only pays the bandwidth use price approved by the user, and the user cannot pay the price exceeding the expected value or larger than the estimated value;
3)，
Figure SMS_55
the total amount paid by each user cannot exceed the individual budget;
4)，
Figure SMS_56
the bidding returns of each user with real valuations are higher than those with non-real valuations;
5)，
Figure SMS_57
the quantity of bandwidth resources allocated by each WiFi hotspot cannot exceed the total quantity of the existing bandwidth resources;
6)，
Figure SMS_58
the total bandwidth resource amount obtained by each user cannot exceed the bandwidth resource amount applied by the user;
7)，
Figure SMS_59
the allocation decision variable cannot be negative;
8)，
Figure SMS_60
the payment decision variable cannot be negative.
6. In order to improve the solving speed of the optimization problem, the method decomposes the optimization problem into a plurality of sub-optimization problems to be solved in sequence. The problem solving algorithm is divided into two parts, namely, a first part of an initial distribution process and a second part of a final distribution process.
7. The initial allocation process solves two sub-optimization problems, and the solution target of the first sub-optimization problemIs obtained such that
Figure SMS_61
The constraint for x, v, which takes the maximum value, is defined as follows:
1)，
Figure SMS_62
the estimated total amount of bandwidth used by each user cannot exceed its paid total amount;
2)，
Figure SMS_63
the quantity of bandwidth resources allocated by each WiFi hotspot cannot exceed the total quantity of current bandwidth resources;
3)
Figure SMS_64
the total amount of bandwidth resources available to each user cannot exceed the amount of bandwidth resources that it requires. />
4)，
Figure SMS_65
The estimated total amount of bandwidth used by each user is the minimum of all possible bidding conditions;
8. the initial allocation decision result x can be obtained by solving the first constraint variable * And resulting in a worst price bidding matrix W;
the second sub-optimization problem of the initial allocation procedure is used to calculate a reserve price that guarantees the benefit of the mobile operator by refusing to engage in the auction process for the underbidding mobile user. The second sub-optimization problem ensures that reasonable bidding users participate in the auction by solving the reserved price, and unreasonable bidding users cannot participate in the auction, so that the rationality of the auction rule can be ensured. The reserve price r can be found by solving the dual problem of the first sub-optimization problem of the initial allocation process (step 7) * 。
9. The final allocation process solves two sub-optimization problems, the first sub-optimization problem being solved with the objective of computing such that
Figure SMS_66
Taking the maximum value y v The constraints of this problem are as follows:
1)，
Figure SMS_67
the secondary allocated bandwidth quantity of each WiFi hotspot is smaller than the difference between the total quantity of the existing bandwidths and the quantity of the initial allocated bandwidths;
2)，
Figure SMS_68
the sum of the secondary payments of each user is less than the difference between the personal total budget and the primary maximum sum of payments;
3)，
Figure SMS_69
the quantity of bandwidth resources acquired by each user is smaller than the difference between the total required quantity and the primary distribution quantity;
10. the second sub-optimization problem of the final allocation process is to calculate the resulting
Figure SMS_70
Taking the maximum value y v-k The constraint of this problem is similar to the first sub-optimization problem of the final allocation process, except that the user k is not considered to participate in the auction process, so that the final price of the user k is calculated reasonably by calculating the influence of the user k on the benefits of the mobile operator in the participation and non-participation in the auction process. The constraints of this problem are defined as follows:
1)，
Figure SMS_71
the secondary allocated bandwidth amount of each WiFi hotspot is smaller than the difference between the total existing bandwidth amount and the initial allocated bandwidth amount without participation of a user k.
2)，
Figure SMS_72
Without the participation of user k, the sum of the secondary payments per user is less than the difference between the individual total budget and the primary maximum sum of payments.
3)，
Figure SMS_73
The number of bandwidth resources acquired by each user is smaller than the difference between the total required number and the initial allocation number without the participation of the user k.
11. Calculating the distribution result and payment result of each user by solving two sub-optimization problems of the initial distribution process and two sub-optimization problems of the final distribution process, wherein the calculation formula of the distribution result is a v ＝x * +y v The final payment result is the sum of the initial bandwidth allocation usage fee and the secondary bandwidth allocation usage fee minus the mobile operator profit, and the calculation formula of the payment result is that
Figure SMS_74
That is, the final payment result of each user includes an initial bandwidth allocation usage fee and a secondary bandwidth allocation usage fee. Each user participating in the auction process increases the mobile operator revenue compared to not participating in the auction process. Thus, to encourage users to participate in the resource competition process, the mobile operator needs to give up this part of the revenue to the users, i.e. subtract from the final payment result for each user
Figure SMS_75
This partial value.
The foregoing describes specific embodiments of the present invention. It is to be understood that the invention is not limited to the particular embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the claims without affecting the spirit of the invention.

Claims (6)

1. A mobile data unloading and pricing method based on a multi-article auction mechanism is characterized in that: comprising the following steps:
all mobile users provide bidding prices of unit bandwidth resources of different WiFi hot spots covered by the same base station, and each mobile user also needs to provide information of total bandwidth resources required;
the mobile operator adopts a multi-article auction mechanism to allocate and price bandwidth resources according to the bidding price of each user, the required bandwidth resource quantity, the historical maximum payment amount of each user, the historical bidding result prices of different WiFi hotspots and the bandwidth resource condition of the current WiFi hotspots;
part of mobile users bid successfully to obtain the required bandwidth resources and pay the corresponding bandwidth resource use fees;
the multi-item auction mechanism includes the following three principles:
1) Rational principles, the payment of each user cannot exceed the gain obtained, i.e
Figure QLYQS_1
；
2) Budget feasibility principle, the payment per user cannot exceed the budget total, i.e
Figure QLYQS_2
；
3) Incentive compatibility principle, ensuring that the user can obtain maximum benefit when providing real auction price, namely
Figure QLYQS_3
，
wherein ,
Figure QLYQS_4
representing the real auction price +_>
Figure QLYQS_5
Representing an arbitrary auction price;
defining a set of mobile users participating in a multi-item auction as
Figure QLYQS_6
；
Defining a set of WiFi hot spots to participate in a multi-item auction
Figure QLYQS_7
These WiFi hotspots belong to the same mobile operator;
defining mobile users
Figure QLYQS_8
In the course of an auction, wiFi hotspots are +.>
Figure QLYQS_9
Valuation of +.>
Figure QLYQS_10
；
Defining a rating matrix as
Figure QLYQS_11
The valuation matrix consists of all valuations;
defining the estimate vector of WiFi hotspot j as
Figure QLYQS_12
The estimated value vector is the estimated value of the WiFi hotspot j by all mobile users;
defining maximum funds for a mobile user as
Figure QLYQS_13
；
Defining the maximum bandwidth resource provided by WiFi hot spot as
Figure QLYQS_14
；
Defining an uncertainty set describing the mobile user valuation, the mobile operator would define an uncertainty set for each WiFi hotspot, the uncertainty set requiring all possible results including a valuation matrix, formalized as:
estimation vector
Figure QLYQS_15
The method comprises the steps of carrying out a first treatment on the surface of the Evaluation matrix->
Figure QLYQS_16
，/>
Figure QLYQS_17
Is defined as the following formula:
Figure QLYQS_18
，
wherein
Figure QLYQS_19
The expectations and variances obtained from the historical valuation information of the WiFi hotspot j, respectively, +.>
Figure QLYQS_20
Is a parameter controlling the conservation degree of the historical valuation information;
defining decision variables of mobile operators and distributing the decision variables
Figure QLYQS_21
Representing the amount of bandwidth resources allocated by each mobile user from different WiFi hotspots under the condition that the evaluation matrix is V, paying for decision variable +.>
Figure QLYQS_22
Representing the final charge paid by each mobile user for using the bandwidth resources under the condition that the evaluation matrix is V;
the auction mechanism is realized by solving the following optimization problem, the objective of the optimization problem is to realize the maximization of the profit of the operator, and the problem solving result is to decide an allocation decision result and a payment decision result, namely, in the feasible domain defined by the constraint conditions of the following optimization problem, the allocation decision variable which enables the final benefit R of the operator to take the maximum value is found
Figure QLYQS_23
And payment decision variable +.>
Figure QLYQS_24
；
The optimization problem constraint conditions are defined as:
1)，
Figure QLYQS_25
the operator ultimately benefits from not exceeding the user's payment sum;
2)，
Figure QLYQS_26
the total payment amount of each user cannot exceed the estimated total amount of the bandwidth resource used by the user, the user only pays the self-approved bandwidth use price and cannot pay the price exceeding the expected or larger than the estimated price;
3)，
Figure QLYQS_27
the total amount paid by each user cannot exceed the individual budget;
4)，
Figure QLYQS_28
the bidding returns of each user with real valuations are higher than those with non-real valuations;
5)，
Figure QLYQS_29
the quantity of bandwidth resources allocated by each WiFi hotspot cannot exceed the total quantity of the existing bandwidth resources;
6)，
Figure QLYQS_30
the total bandwidth resources obtained by each user cannot exceed the bandwidth resources applied by the user;
7)，
Figure QLYQS_31
the allocation decision variable cannot be negative;
8)，
Figure QLYQS_32
the payment decision variable cannot be negative.
2. The mobile data offloading and pricing method of claim 1, wherein the mobile data offloading and pricing method is based on a multi-item auction mechanism: and decomposing the optimization problem into an initial distribution process and a final distribution process, and sequentially solving.
3. The mobile data offloading and pricing method of claim 2, wherein the mobile data offloading and pricing method is based on a multi-item auction mechanism: the initial allocation process solves two sub-optimization problems, the first sub-optimization problem being solved with the objective of obtaining the result that
Figure QLYQS_33
Maximum value +.>
Figure QLYQS_34
The constraint is defined as follows:
1)，
Figure QLYQS_35
the estimated total amount of bandwidth used by each user cannot exceed its paid total amount;
2),
Figure QLYQS_36
the quantity of bandwidth resources allocated by each WiFi hotspot cannot exceed the total quantity of current bandwidth resources;
3) ，
Figure QLYQS_37
the total bandwidth resources obtained by each user cannot exceed the required bandwidth resources;
4)，
Figure QLYQS_38
the estimated total amount of bandwidth used by each user is the minimum of all possible bidding conditions;
the initial allocation decision result can be obtained by solving the first constraint variable
Figure QLYQS_39
And result in the worst price bidding matrix +.>
Figure QLYQS_40
；
The second sub-optimization problem of the initial allocation process is used to calculate the reserve price, which can be found by solving the dual problem of the first sub-optimization problem of the initial allocation process
Figure QLYQS_41
。
4. The mobile data offloading and pricing method of claim 3, wherein the mobile data offloading and pricing method is based on a multi-item auction mechanism: the final allocation process solves two sub-optimization problems, the first sub-optimization problem being solved with the objective of computing such that
Figure QLYQS_42
Maximum value +.>
Figure QLYQS_43
The constraints of this problem are as follows:
1)，
Figure QLYQS_44
the secondary allocated bandwidth quantity of each WiFi hotspot is smaller than the difference between the total quantity of the existing bandwidths and the quantity of the initial allocated bandwidths;
2)，
Figure QLYQS_45
the sum of the secondary payments of each user is less than the difference between the personal total budget and the primary maximum sum of payments;
3)，
Figure QLYQS_46
each use ofThe number of bandwidth resources acquired by the user is smaller than the difference between the total required number and the primary allocation number; />
The second sub-optimization problem of the final allocation process is to calculate the resulting
Figure QLYQS_47
Maximum value +.>
Figure QLYQS_48
The constraints of this problem are defined as follows:
1) ，
Figure QLYQS_49
the secondary allocation bandwidth quantity of each WiFi hotspot is smaller than the difference between the total quantity of the existing bandwidths and the quantity of the initial allocation bandwidths without participation of a user k;
2),
Figure QLYQS_50
the sum of the secondary payments of each user is smaller than the difference between the personal total budget and the primary maximum payment sum without the participation of user k;
3)，
Figure QLYQS_51
the number of bandwidth resources acquired by each user is smaller than the difference between the total required number and the primary allocation number without the participation of the user k.
5. The multi-item auction mechanism based mobile data offloading and pricing method of claim 4, wherein: calculating the distribution result and payment result of each user by solving two sub-optimization problems of the initial distribution process and two sub-optimization problems of the final distribution process, wherein the calculation formula of the distribution result is as follows
Figure QLYQS_52
The final payment result is the sum of the initial bandwidth allocation usage fee and the secondary bandwidth allocation usage fee minus the mobile operator profit, and the payment is concludedThe calculation formula of the fruit is
Figure QLYQS_53
。
6. The multi-item auction mechanism based mobile data offloading and pricing method of claim 5, wherein: the method specifically comprises the following steps:
s1: the mobile user submits bandwidth resource quantity request information D and corresponding unit bandwidth resource bidding information v;
s2: the mobile operator invokes mobile user historical bidding information B and WiFi hotspot historical bidding pricing information, wherein the WiFi hotspot historical bidding pricing information comprises historical average selling price mu and variance delta of unit bandwidth resources of WiFi hotspots;
s3: the mobile operator solves the bilinear optimization problem according to the historical bidding information of the mobile user and the historical sales information of the WiF hot spot to obtain an initial allocation decision result x * And a worst bidding price matrix W;
s4: the mobile operator decides the result x according to the initial allocation * And calculating reserved price r by using worst bidding price matrix W * ；
S5: the mobile operator decides the result x according to the initial allocation * And reserve price r * Calculating a quadratic distribution decision variable y v ；
S6: the mobile operator decides the result x according to the initial allocation * And a quadratic distribution decision variable y v Calculating the final distribution result a v ；
S7: the mobile operator calculates a secondary allocation decision variable under each winning bid user;
s8: the mobile operator calculates the final payment of the winning subscriber.
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