CN110972160B - Design method for unmanned aerial vehicle traffic unloading contract mechanism in heterogeneous cellular network - Google Patents
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Abstract
The invention belongs to the technical field of unmanned aerial vehicle traffic unloading, and particularly relates to a design method of an unmanned aerial vehicle traffic unloading contract mechanism in a heterogeneous cellular network. Applying a contract model based on market driving to an unmanned aerial vehicle flow unloading task, and establishing a base station model and an unmanned aerial vehicle model under asymmetric information; considering the selfishness of the unmanned aerial vehicle, the unmanned aerial vehicle may be unwilling to participate in a plurality of traffic unloading tasks without additional rewards, and the unmanned aerial vehicle is stimulated to participate in the plurality of traffic unloading tasks by providing a multidimensional contract stimulation method; aiming at mutual noninfluency among flow unloading tasks, analyzing the task independence problem in the contract design process, and establishing a random parameter independent model and a task independent model; by evaluating the performance of the unmanned aerial vehicle, the base station rewards and motivates the unmanned aerial vehicle to participate in traffic offloading tasks and work more hard, so that the purpose of maximizing the utility of the unmanned aerial vehicle and the base station is achieved.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle traffic unloading, and particularly relates to a design method of an unmanned aerial vehicle traffic unloading contract mechanism in a heterogeneous cellular network.
Background
Heterogeneous cellular networks are widely recognized as a solution to the explosive growth of data traffic. In the heterogeneous cellular network, when the base station communication overload can not meet the user demand of the current region, the unmanned aerial vehicle can be rapidly deployed to the target region for traffic unloading. Upon completion of the traffic offload task in the target area, the unmanned aerial vehicle may consume various resources, such as data processing and transmission costs, flight consumption, and energy consumption for traffic offload. Thus, drones may be less willing to participate in traffic offload tasks. Meanwhile, when the unmanned aerial vehicle participates in the traffic offloading task, the traffic offloading task may be performed with lower efficiency in order to pursue more utility. However, since the natural environment and the communication network where the drone is located are changing, the base station cannot obtain the actual workload of the drone in the traffic offloading task, thereby resulting in information asymmetry between the base station and the drone.
At present, the problem of information asymmetry in unmanned aerial vehicle traffic offloading is receiving attention of researchers. The most common incentive method is the auction mechanism, but the auction mechanism can only ensure that the unmanned aerial vehicle obtains fixed utility, and cannot maximize the utility of the unmanned aerial vehicle. In addition, current contract research only defines the incentive mechanism as one-dimensional, and in fact, in most cases, the drone needs to complete different traffic offloading tasks such as: voice conversation between users, video watching, information transmission, and the like. Therefore, a multidimensional incentive contract is needed to be designed to encourage the drone to participate in traffic offloading tasks.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a design method of an unmanned aerial vehicle traffic unloading contract mechanism in a heterogeneous cellular network.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a design method for an unmanned aerial vehicle traffic offload contract mechanism in a heterogeneous cellular network is characterized by comprising the following steps:
step 1, applying a contract model based on market driving to an unmanned aerial vehicle flow unloading task, and establishing a base station model and an unmanned aerial vehicle model under asymmetric information;
step 2, considering the selfishness of the unmanned aerial vehicle, the unmanned aerial vehicle may be unwilling to participate in a plurality of traffic unloading tasks under the condition of no extra reward, and the unmanned aerial vehicle is stimulated to participate in the plurality of traffic unloading tasks by providing a multidimensional contract stimulation method;
step 3, aiming at mutual noninfluency among the flow unloading tasks, analyzing the task independence problem in the contract design process, and establishing a random parameter independent model and a task independent model;
and 4, by evaluating the performance of the unmanned aerial vehicle, the base station rewards and stimulates the unmanned aerial vehicle to participate in the traffic unloading task and make more effort, so that the purpose of maximizing the utility of the unmanned aerial vehicle and the base station is achieved.
Further, the base station model design adopts a method that: after the unmanned aerial vehicle completes the flow unloading task, the base station gives reward to the unmanned aerial vehicle for evaluation, and random parameters are introduced to reflect the consistency between the actual completion degree of the unmanned aerial vehicle participating in the flow unloading task and the evaluation result of the base station due to the fact that the deviation exists between the actual completion degree of the unmanned aerial vehicle participating in the flow unloading task and the evaluation result of the base station
The matrix psi is a symmetric matrix, n represents n traffic offload tasks, main diagonal
The variance represents that the actual completion degree of the unmanned aerial vehicle flow unloading task is different from the base station evaluation result, and the non-diagonal elements
Indicating that the completion of task i has an impact on the base station's evaluation of task j. If the variance is large, the difference between the actual completion degree of the unmanned aerial vehicle flow unloading task and the base station evaluation result is large; on the contrary, if the unmanned aerial vehicle flow unloading task completion degree is consistent with the base station evaluation result, the variance is very small or even 0; covariance exists between two traffic unloading tasks participated by the unmanned aerial vehicle, because the completion of one task can affect the evaluation of the other task by the base station;
when the unmanned aerial vehicle participates in the flow unloading task, the base station sets the task completion degree e as (e) according to the unmanned aerial vehicle task completion degree e 1,e2,...,en)TPaying consideration R (alpha, e, beta, psi), e to the dronei(1 ≦ i ≦ n) indicates the completion of task i, where α is the basic payroll for the drone and β ≦ n1,β2,...,βn)TIs a bonus coefficient, beta, related to the performance of the dronei(i is more than or equal to 1 and less than or equal to n) represents the bonus coefficient of the unmanned aerial vehicle for completing the task i, the unmanned aerial vehicle completes the traffic unloading task to enable the base station to obtain the income P (alpha, e, beta, psi), and the base station obtains the effectiveness as follows:
UBS(α,e,β)＝P(α,e,β,ψ)-R(α,e,β,ψ)。
further, the method adopted by the unmanned aerial vehicle model design is as follows: the unmanned aerial vehicle participates in the flow unloading and arbitrarily consumes various resources, including data processing and transmission cost, flight consumption and flow unloading energy consumption, because the flow unloading task cost participated by the unmanned aerial vehicle is different, the cost for the unmanned aerial vehicle to finish each task is different, and considering that the flow unloading tasks may have mutual influence, the unit cost for the unmanned aerial vehicle to finish the tasks is defined as the unit cost
Matrix array
Main diagonal element of
Representing the cost of the traffic offload task i itself, other off-diagonal elements of the matrix
Indicating that the traffic offload task cost i has an impact on the traffic offload task j. The cost of the unmanned aerial vehicle participating in a certain traffic unloading task will be increased by the cost of another traffic unloading task, so that the cost of the unmanned aerial vehicle participating in the traffic unloading task can be obtained as 
After the unmanned aerial vehicle completes the task of unloading the traffic, a reward R (α, e, β, ψ) is obtained from the base station, and the utility of the unmanned aerial vehicle participating in the traffic unloading task can be defined as:
further, the multidimensional contract incentive method comprises the following steps: because of the selfness of the base station and the drones, both try to maximize their utility, so in order for a drone employed by the base station to intentionally accept this contract, the base station needs to ensure that the expected utility achieved by the drone is positive, i.e. greater than or equal to zero, i.e. the cooperation contract needs to meet personal rational IR constraints;
although the base station already signs a contract with the unmanned aerial vehicle, the base station still cannot acquire the traffic unloading task completion degree of the unmanned aerial vehicle due to asymmetric information, so the contract should ensure the highest traffic unloading task completion degree e of the unmanned aerial vehicle*For maximum utility for the drone, the incentive-compliant IC constraints are given by:
to maximize the expected utility of a base station, the optimal contract design problem can be written as:
where the IR constraints ensure that the drone achieves positive expected utility, the IC constraints ensure that the expected utility achieved by the drone is maximized.
Further, the method adopted by the design of the random parameter independent model is as follows: when the unmanned aerial vehicle participates in the traffic offloading task, the covariance of the random parameter between any two tasks is zero, that is, the evaluation result of one traffic offloading task base station does not affect the evaluation result of the other traffic offloading task base station, and the evaluation result of the base station is random and independent after the traffic offloading task is completed, so that the random parameter matrix ψ is mapped as a diagonal matrix to solve, and therefore, the maximization problem can be expressed as:
Where the IR constraints ensure that the drone achieves positive expected utility, the IC constraints ensure that the expected utility achieved by the drone is maximized.
Further, the method adopted by the task independent model design is as follows: when the unmanned aerial vehicle participates in the traffic unloading task, the covariance between the cost of any two tasks is zero, namely the unmanned aerial vehicle finishes one of the traffic unloading tasks without influencing the cost for finishing the other task, therefore, the unit cost matrix for finishing the tasks is the
The mapping is solved as a diagonal matrix, so the maximization problem can be expressed as:
where the IR constraints ensure that the drone achieves positive expected utility, the IC constraints ensure that the expected utility achieved by the drone is maximized.
Further, in step 4, the measures for the base station to award and encourage the drone are: the base station is an employer to issue tasks, the unmanned aerial vehicle is an employee to complete flow unloading tasks issued by the base station and obtain returns, in an unmanned aerial vehicle incentive mechanism based on a contract theory, the base station serves as an active contracting party and provides incentive contracts consisting of a series of contract terms for the employee, and the contract terms comprise optimal basic salary alpha*And an optimum bonus factor beta*The unmanned aerial vehicle decides whether to accept the contract and informs the base station, after accepting the contract, the unmanned aerial vehicle needs to pay the specified effort and inform the base station of the contract result, finally the base station accepts the result from the unmanned aerial vehicle, and the base station decides whether to pay the reward to the unmanned aerial vehicle based on the received information.
Compared with the prior art, the invention has the beneficial effects that: the incentive mechanism provided by the invention is multidimensional, and can encourage the unmanned aerial vehicle to participate in different flow unloading tasks. The contract maximizes the utility of the base station and stimulates the unmanned aerial vehicle to complete tasks under the asymmetric information scene. Meanwhile, the unmanned aerial vehicle excitation mechanism method based on the contract theory is easy to realize, and the unmanned aerial vehicle can finish a plurality of flow unloading tasks independently at one time, so that the flying times of the unmanned aerial vehicle are reduced, and unnecessary energy consumption is reduced.
Detailed Description
The present invention will be described in further detail with reference to examples for the purpose of facilitating understanding and practice of the invention by those of ordinary skill in the art, and it is to be understood that the present invention has been described in the illustrative embodiments and is not to be construed as limited thereto.
The embodiment applies the market-driven contract model to the unmanned aerial vehicleIn the flow unloading task, the base station is an employer to issue a task, and the unmanned aerial vehicle is an employee to complete the flow unloading task and obtain a return. In the unmanned plane incentive mechanism based on the contract theory, the base station serves as an active contracting party and provides an incentive contract consisting of a series of contract terms to the employee, wherein the contract terms comprise the optimal basic payroll alpha *And an optimum bonus factor beta*. The drone makes its prescribed effort and informs the base station of the contract result, and finally the base station will accept the result from the drone, and the base station decides whether to pay a reward to the drone based on the received information.
The market-driven contract model is applied to the unmanned aerial vehicle flow unloading task, and a base station model and an unmanned aerial vehicle model under asymmetric information are established; considering the selfish property of the unmanned aerial vehicle, the unmanned aerial vehicle may be unwilling to participate in a plurality of traffic offloading tasks without additional rewards, and the unmanned aerial vehicle is encouraged to participate in the plurality of traffic offloading tasks by designing a multidimensional contractual incentive method; aiming at mutual influence among flow unloading tasks, the problem of task independence is analyzed in the contract design process, and a random parameter independent model and a task independent model are established; by evaluating the performance of the drone, the base station will reward and motivate them to participate in traffic offload tasks and work harder, thereby achieving the goal of maximizing utility of the drone and the base station.
And after the unmanned aerial vehicle finishes the flow unloading task, the base station pays consideration to the unmanned aerial vehicle for evaluating the performance of the unmanned aerial vehicle. Because the actual completion degree of the unmanned aerial vehicle participating in the traffic unloading task is deviated from the base station evaluation result, random parameters are introduced to reflect the consistency of the actual completion degree of the unmanned aerial vehicle participating in the traffic unloading task and the base station evaluation result 
The matrix psi is a symmetric matrix, n represents n traffic offload tasks, main diagonal
The variance represents the difference between the actual completion degree of the unmanned aerial vehicle flow unloading task and the base station evaluation result, and the off-diagonal elements
Indicating that the completion of task i will have an impact on the base station's evaluation of task j. And if the variance is larger, the difference between the actual completion degree of the unmanned aerial vehicle flow unloading task and the base station evaluation result is larger. For example: when the user talks, the conversation environment established by the unmanned aerial vehicle is poor, and the deviation between the task completion degree of the unmanned aerial vehicle and the evaluation result of the base station is large. Conversely, if the unmanned aerial vehicle traffic offload task completion is consistent with the base station evaluation result, the variance will be small or even 0. For example: and a user downloads a file, and the difference between the actual completion degree of the unmanned aerial vehicle flow unloading task and the base station evaluation result is small. Covariance exists between the two traffic offload tasks that the drone participates in because the completion of one task can have an impact on the base station's evaluation of the other task. For example: music online playing and video online playing have strong connection.
When the unmanned aerial vehicle participates in the flow unloading task, the base station sets the task completion degree e as (e) according to the unmanned aerial vehicle task completion degree e1,e2,...,en)TPaying rewards R (alpha, e, beta, psi), e to the drone i(1 ≦ i ≦ n) represents the completion of task i, where α is the basic payroll for the drone and β ≦ n1,β2,...,βn)TIs a bonus coefficient, beta, related to the performance of the dronei(i is more than or equal to 1 and less than or equal to n) represents a bonus coefficient of the unmanned aerial vehicle for completing the task i, the unmanned aerial vehicle completes the traffic unloading task to enable the base station to obtain benefits P (alpha, e, beta, psi), and the base station obtains effects as follows:
UBS(α,e,β)＝P(α,e,β,ψ)-R(α,e,β,ψ)
the participation of drones in traffic offloading is any resource consuming, such as data processing and transmission costs, flight consumption, and energy consumption for traffic offloading. Because the traffic unloading task cost of the unmanned aerial vehicle is different, the cost for the unmanned aerial vehicle to complete each task is different. Considering the possible mutual influence among the traffic unloading tasks, the unit cost of the unmanned aerial vehicle for completing the tasks is defined as
Matrix of
Main diagonal element of
Representing the cost of the traffic offload task i itself, other non-zero elements of the matrix
Indicating that the traffic offload task cost i has an impact on the traffic offload task j. The participation of an drone in a traffic offload task will increase the cost of another traffic offload task, for example: participation of the drone in the video call task will increase the voice call task cost. Therefore, the cost of the unmanned aerial vehicle participating in the traffic unloading task can be obtained as 
After the drone completes the task of offloading traffic, a reward R (α, e, β, ψ) will be obtained from the base station. The utility obtained by the unmanned aerial vehicle participating in the traffic offload task can be defined as:
due to the selfish nature of base stations and drones, both base stations and drones try to maximize their utility. Therefore, in order for a base station-employed drone to intentionally accept this contract, the base station needs to ensure that the desired utility achieved by the drone is positive (equal to or greater than zero), i.e., the collaboration contract needs to meet personal rational (IR) constraints:
although the base station has already signed a contract with the unmanned aerial vehicle, the base station still cannot acquire the traffic unloading task completion degree of the unmanned aerial vehicle due to asymmetric information. Therefore, the contract should ensure the highest completion degree e of the unmanned aerial vehicle flow unloading task*Make unmanned aerial vehicle obtain the maximum utility. The excitation Compatibility constraint (IC) is given by:
to maximize the expected utility of a base station, the optimal contract design problem can be written as:
where the IR constraints ensure that the drone obtains positive expected utility, the IC constraints ensure that the expected utility obtained by the drone is maximized.
When the unmanned aerial vehicle participates in different flow unloading tasks in a target area, considering that the flow unloading tasks are not mutually influenced, the flow unloading tasks can be divided into random independence and technology independence.
(1) Independent of randomness
When the unmanned aerial vehicle participates in the traffic unloading task, the covariance of random parameters between any two tasks is zero, namely the evaluation result of one traffic unloading task base station does not influence the evaluation result of the other traffic unloading task base station, and the evaluation results of the base stations are random and independent after the traffic unloading task is completed. For example: the evaluation results of the base station are not affected after the speech communication flow unloading task and the information sending flow unloading task are completed. Therefore, the random parameter matrix ψ is mapped as a diagonal matrix to be solved. Thus, the maximization problem can be expressed as:
where the IR constraints ensure that the drone obtains positive expected utility, the IC constraints ensure that the expected utility obtained by the drone is maximized.
(2) Technology independence
When the unmanned aerial vehicle participates in the traffic unloading task, the covariance between the cost of any two tasks is zero, namely the cost for completing the other task is not influenced when the unmanned aerial vehicle completes one of the traffic unloading tasks. For example: the unmanned aerial vehicle can not increase the cost of completing the conversation task when completing the file downloading task. Thus, the unit cost matrix of the task to be completed
The mapping is solved as a diagonal matrix. Thus, the maximization problem can be expressed as:
Where the IR constraints ensure that the drone obtains positive expected utility, the IC constraints ensure that the expected utility obtained by the drone is maximized.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (1)
1. A design method for an unmanned aerial vehicle traffic offload contract mechanism in a heterogeneous cellular network is characterized by comprising the following steps:
step 1, applying a contract model based on market driving to an unmanned aerial vehicle flow unloading task, and establishing a base station model and an unmanned aerial vehicle model under asymmetric information;
step 2, considering the selfishness of the unmanned aerial vehicle, the unmanned aerial vehicle may be unwilling to participate in a plurality of traffic unloading tasks under the condition of no extra reward, and the unmanned aerial vehicle is stimulated to participate in the plurality of traffic unloading tasks by providing a multidimensional contract stimulation method;
Step 3, aiming at mutual noninfluency among the flow unloading tasks, analyzing the task independence problem in the contract design process, and establishing a random parameter independent model and a task independent model;
step 4, by evaluating the performance of the unmanned aerial vehicle, the base station rewards and stimulates the unmanned aerial vehicle to participate in the traffic unloading task and make more effort, thereby achieving the purpose of maximizing the utility of the unmanned aerial vehicle and the base station;
the base station model design adopts the following method: after the unmanned aerial vehicle finishes the flow unloading task, the base station pays the reward for the performance of the unmanned aerial vehicle, and the actual completion degree of the flow unloading task participated by the unmanned aerial vehicle is deviated from the evaluation result of the base station so as to reflect the participation of the unmanned aerial vehicle in the flowThe consistency of the actual completion degree of the volume unloading task and the evaluation result of the base station is introduced with random parameters
The matrix psi is a symmetric matrix, n represents n traffic offload tasks, main diagonal
The variance is used for representing the difference between the actual completion degree of the flow unloading task of the unmanned aerial vehicle and the evaluation result of the base station, and i is more than or equal to 1 and less than or equal to n; off diagonal elements
Indicating that the completion of the task i can influence the base station evaluation task j, wherein i is more than or equal to 1 and less than or equal to n, and j is more than or equal to 1 and less than or equal to n; if the variance is large, the difference between the actual completion degree of the unmanned aerial vehicle flow unloading task and the base station evaluation result is large; on the contrary, if the unmanned aerial vehicle flow unloading task completion degree is consistent with the base station evaluation result, the variance is very small or even 0; covariance exists between two traffic unloading tasks participated by the unmanned aerial vehicle, because the completion of one task can affect the evaluation of the other task by the base station;
When the unmanned aerial vehicle participates in the flow unloading task, the base station sets the task completion degree e as (e) according to the unmanned aerial vehicle1,e2,...,en)TPaying consideration R (alpha, e, beta, psi), e to the droneiRepresenting the completion of task i, i ≦ 1 ≦ n, where α is the basic payroll for the drone and β ≦ β (β ≦ n)1,β2,...,βn)TIs a bonus coefficient, beta, related to the performance of the droneiThe bonus coefficient of the unmanned aerial vehicle for completing the task i is larger than or equal to 1 and smaller than or equal to n, the unmanned aerial vehicle completes the traffic unloading task to enable the base station to obtain benefits P (alpha, e, beta, psi), and the base station obtains the effectiveness as follows:
UBS(α,e,β)＝P(α,e,β,ψ)-R(α,e,β,ψ)
the method adopted by the unmanned aerial vehicle model design is as follows: the participation of the unmanned aerial vehicle in the traffic offload arbitrary consumes various resources, including data processing and transmission costs, flight consumption, and traffic offload energy consumptionBecause the traffic unloading tasks participated by the unmanned aerial vehicles have different costs and the cost paid by the unmanned aerial vehicles for completing each task is different, considering that the traffic unloading tasks may have mutual influence, the unit cost for completing the tasks by the unmanned aerial vehicles is defined as
Matrix array
Main diagonal element of
The cost of the flow unloading task i is expressed, i is more than or equal to 1 and less than or equal to n, and other off-diagonal elements of the matrix
The flow unloading task cost i is shown to influence the flow unloading task j, i is more than or equal to 1 and less than or equal to n, and j is more than or equal to 1 and less than or equal to n; the cost of the unmanned aerial vehicle participating in a certain traffic unloading task will be increased by the cost of another traffic unloading task, so that the cost of the unmanned aerial vehicle participating in the traffic unloading task can be obtained as 
After the drone completes the task of offloading traffic, a reward R (α, e, β, ψ) is obtained from the base station, and the utility of the drone for participating in the traffic offloading task can be defined as:
the multi-dimensional contract excitation method comprises the following steps: because of the selfishness of the base station and the drone, both base station and drone attempt to maximize their utility, in order for a drone employed by the base station to intentionally accept the contract, the base station needs to ensure that the expected utility achieved by the drone is positive, i.e., greater than or equal to zero, i.e., the cooperative contract needs to meet personal rational IR constraints;
although the base station already signs a contract with the unmanned aerial vehicle, the base station still cannot acquire the completion degree of the traffic unloading task of the unmanned aerial vehicle due to asymmetric information, so that the contract should ensure the highest completion degree e of the traffic unloading task of the unmanned aerial vehicle*Maximizing utility for the drone, the incentive compatible IC constraint is given by:
(IC)
to maximize the expected utility of a base station, the optimal contract design problem can be written as:
s.t.(IR)
(IC)
wherein the IR constraints ensure that the drone obtains positive expected utility, and the IC constraints ensure that the expected utility obtained by the drone is maximized;
the method for designing the random parameter independent model is as follows: when the unmanned aerial vehicle participates in the traffic offloading task, the covariance of the random parameter between any two tasks is zero, that is, the evaluation result of one traffic offloading task base station does not affect the evaluation result of the other traffic offloading task base station, and the evaluation result of the base station is random and independent after the traffic offloading task is completed, so that the random parameter matrix psi is mapped into a diagonal matrix for solving, and therefore, the maximization problem can be expressed as:
s.t.(IR)
(IC)
Wherein the IR constraints ensure that the drone obtains positive expected utility, and the IC constraints ensure that the expected utility obtained by the drone is maximized;
the method for designing the task independent model comprises the following steps: when the unmanned aerial vehicle participates in the traffic unloading task, the covariance between the cost of any two tasks is zero, namely the unmanned aerial vehicle finishes one of the traffic unloading tasks without influencing the cost for finishing the other task, therefore, the unit cost matrix for finishing the tasks is the
The mapping is solved as a diagonal matrix, so the maximization problem can be expressed as:
s.t.(IR)
(IC)
wherein the IR constraints ensure that the drone obtains positive expected utility, and the IC constraints ensure that the expected utility obtained by the drone is maximized;
in the step 4, the process of the method,the measures for rewarding and exciting the unmanned aerial vehicle by the base station are as follows: the base station is an employer to issue tasks, the unmanned aerial vehicle is an employee to complete flow unloading tasks issued by the base station and obtain returns, in an unmanned aerial vehicle incentive mechanism based on a contract theory, the base station serves as an active contracting party and provides incentive contracts consisting of a series of contract terms for the employee, and the contract terms comprise optimal basic salary alpha*And an optimum bonus coefficient beta*The unmanned aerial vehicle decides whether to accept the contract and informs the base station, after accepting the contract, the unmanned aerial vehicle needs to pay the specified effort and inform the base station of the contract result, finally the base station accepts the result from the unmanned aerial vehicle, and the base station decides whether to pay the reward to the unmanned aerial vehicle based on the received information.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107846690A (en) * | 2017-10-19 | 2018-03-27 | 湖北工业大学 | Collaboration communication dynamic bargain motivational techniques under a kind of independent asymmetrical information |
| EP3375216A1 (en) * | 2015-11-12 | 2018-09-19 | Sony Mobile Communications Inc. | Dynamic positioning method for mobile cells |
| CN109005057A (en) * | 2018-07-19 | 2018-12-14 | 华北电力大学 | A kind of computational resource allocation based on contract matching theory and task unload scheme |
| CN109362087A (en) * | 2018-11-05 | 2019-02-19 | 北京邮电大学 | Flow unloading collaboration method and system based on isomery cloud Radio Access Network |
| CN109982328A (en) * | 2019-01-31 | 2019-07-05 | 湖北工业大学 | A kind of shared intelligent contract design method of the collaboration frequency spectrum based on deep neural network |
| CN110336861A (en) * | 2019-06-18 | 2019-10-15 | 西北工业大学 | The unloading method for allocating tasks of mobile edge calculations system based on the double-deck unmanned plane |
-
2019
- 2019-10-21 CN CN201910998025.8A patent/CN110972160B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3375216A1 (en) * | 2015-11-12 | 2018-09-19 | Sony Mobile Communications Inc. | Dynamic positioning method for mobile cells |
| CN107846690A (en) * | 2017-10-19 | 2018-03-27 | 湖北工业大学 | Collaboration communication dynamic bargain motivational techniques under a kind of independent asymmetrical information |
| CN109005057A (en) * | 2018-07-19 | 2018-12-14 | 华北电力大学 | A kind of computational resource allocation based on contract matching theory and task unload scheme |
| CN109362087A (en) * | 2018-11-05 | 2019-02-19 | 北京邮电大学 | Flow unloading collaboration method and system based on isomery cloud Radio Access Network |
| CN109982328A (en) * | 2019-01-31 | 2019-07-05 | 湖北工业大学 | A kind of shared intelligent contract design method of the collaboration frequency spectrum based on deep neural network |
| CN110336861A (en) * | 2019-06-18 | 2019-10-15 | 西北工业大学 | The unloading method for allocating tasks of mobile edge calculations system based on the double-deck unmanned plane |
Non-Patent Citations (2)
| Title |
|---|
| 基于D2D通信的流量卸载技术综述;颜学文等;《计算机科学》;20181115(第11期);全文 * |
| 面向异构无人机中继网络的负载均衡:一种分层博弈方法;杨婷婷等;《通信技术》;20181110(第11期);全文 * |
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