Unmanned aerial vehicle base station resource allocation method and device based on hierarchical game, equipment and storage medium

Abstract

The invention discloses an unmanned aerial vehicle base station resource allocation method and device based on a hierarchical game, equipment and a storage medium, and relates to the technical field of data communication, and the method comprises the steps: obtaining access link transmission information and return link transmission information when a macro base station, an unmanned aerial vehicle base station and a user side carry out wireless information return operation, the unmanned aerial vehicle base station resource allocation problem is divided into an incidence relation optimization problem corresponding to a user level and a transmission resource optimization problem corresponding to a resource level according to the user level and the flight position information of the unmanned aerial vehicle base station; respectively modeling the receiving rate of the user side and the transmission resource of the target base station to obtain a corresponding first game model and a corresponding second game model; and determining a target personal utility and a target resource backhaul utility corresponding to the two models in the game equilibrium state to obtain a target resource allocation strategy for the unmanned aerial vehicle base station to carry out wireless information backhaul operation. In this way, the user association state can be accurately analyzed, and optimal allocation of unmanned aerial vehicle base station resources is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data communication, and particularly relates to a method, device, equipment and storage medium for allocating resources of an unmanned aerial vehicle (UAV) base station based on hierarchical game theory. Background Art

[0002] When conducting network communication between a user terminal and a UAV base station, existing research usually models the association between the terminal user and the UAV base station as completely rational and static, that is, immediately reaching a network stable state rather than dynamically deriving, which deviates from reality. In the actual implementation of wireless backhaul, both the terminal user and the UAV base station have time-varying decisions. Therefore, the static model cannot accurately capture the resulting dynamics.

[0003] Therefore, how to better analyze the user association state when modeling the association between the terminal user and the base station establishment to achieve the optimal allocation of UAV base station resources needs to be solved. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, equipment and storage medium for allocating resources of a UAV base station based on hierarchical game theory, which can accurately analyze the user association state and achieve the optimal allocation of UAV base station resources. The specific solutions are as follows:

[0005] In a first aspect, the present application discloses a method for allocating resources of a UAV base station based on hierarchical game theory, including:

[0006] Obtaining the access link transmission information and the backhaul link transmission information when the macro base station, the UAV base station and the user terminal perform wireless information backhaul operations, and the flight position information of the UAV base station;

[0007] Based on the access link transmission information, the backhaul link transmission information, and the flight position information, splitting the UAV base station resource allocation problem into an association relationship optimization problem corresponding to the user level and a transmission resource optimization problem corresponding to the resource level; the association relationship optimization problem is an optimization problem for the network association relationship established between the user terminal and the target base station for performing wireless information backhaul operations; the target base station includes the macro base station and the UAV base station;

[0008] Respectively modeling the receiving rate of the user terminal and the transmission resources of the target base station to obtain a first game model corresponding to the association relationship optimization problem and a second game model corresponding to the transmission resource optimization problem;

[0009] Determine the target individual utility and the target resource feedback utility corresponding to the first game model and the second game model respectively in the game equilibrium state, and determine the target resource allocation strategy for the UAV base station to perform the wireless information feedback operation based on the target individual utility and the target resource feedback utility.

[0010] Optionally, the obtaining of the access link transmission information and the feedback link transmission information when the macro base station, the UAV base station and the user terminal perform the wireless information feedback operation, and the flight position information of the UAV base station includes:

[0011] Establish a network association relationship between the user terminal and the macro base station and the UAV base station based on the user auction method, and establish a network connection between the macro base station, the UAV base station and the user terminal based on the network association relationship to perform the wireless information feedback operation;

[0012] Determine the flight position information of the UAV base station based on the horizontal deployment position, the vertical deployment position and the corresponding flight period of the UAV base station;

[0013] Determine the first access link transmission rate corresponding to the UAV base station according to the first transmission power of the UAV base station, the noise power spectral density and the first channel gain set of the user terminals having a network association relationship with the UAV base station;

[0014] Determine the second access link transmission rate corresponding to the macro base station according to the second transmission power of the macro base station, the noise power spectral density and the second channel gain set of the user terminals having a network association relationship with the macro base station;

[0015] Determine the feedback link transmission information of the UAV base station based on a preset feedback link transmission bandwidth allocation ratio.

[0016] Optionally, the respectively modeling the receiving rate of the user terminal and the transmission resources of the target base station to obtain the first game model corresponding to the association relationship optimization problem and the second game model corresponding to the transmission resource optimization problem includes:

[0017] Model the receiving rate of the user terminal based on a preset evolutionary game model to obtain the first game model corresponding to the association relationship optimization problem;

[0018] Model the transmission resources of the UAV base station and the macro base station based on a preset Stackelberg game comprehensive research method to obtain the second game model corresponding to the transmission resource optimization problem.

[0019] Optionally, modeling the receiving rate of the client based on a preset evolutionary game model to obtain a first game model corresponding to the association relationship optimization problem includes:

[0020] Determine the client during the wireless information backhaul operation as a game participant, and determine the current association number of the clients having a network association relationship with the target base station as a game strategy;

[0021] Construct a participant utility model corresponding to the game participant based on the first access link transmission rate, the second access link transmission rate, and the game strategy;

[0022] Construct a replicator dynamic model corresponding to the game participant, and determine a first game model corresponding to the association relationship optimization problem based on the participant utility model and the replicator dynamic model.

[0023] Optionally, modeling the transmission resources of the UAV base station and the macro base station based on a preset Stackelberg game comprehensive research method to obtain a second game model corresponding to the transmission resource optimization problem includes:

[0024] Based on the difference between the fee charged by the UAV base station to the client and the fee paid to the macro base station in the user auction method, determine the wireless information backhaul profit of the UAV base station during the wireless information backhaul operation;

[0025] Determine the wireless information backhaul cost corresponding to the first access link transmission rate, the second access link transmission rate, and the preset backhaul link transmission bandwidth allocation ratio, and model the utility of the UAV base station based on the wireless information backhaul profit and the wireless information backhaul cost to obtain a first utility sub-model;

[0026] Model the utility of the macro base station based on the difference between the profit obtained by the macro base station from the UAV base station and the client and the cost incurred during the wireless information backhaul operation to obtain a second utility sub-model, and determine a second game model corresponding to the transmission resource optimization problem based on the first utility sub-model and the second utility sub-model.

[0027] Optionally, determining the target individual utility and the target resource backhaul utility respectively corresponding to the first game model and the second game model in the game equilibrium state includes:

[0028] Adjust the game strategy in the participant utility model based on the flight position information of the UAV base station, the preset backhaul link transmission bandwidth allocation ratio, and the replicator dynamic model to obtain the current individual utility of the participant utility model;

[0029] Determine a new current association quantity of the user equipment having a network association relationship with the target base station based on the current personal utility;

[0030] Determine the current resource feedback utility corresponding to the second game model based on the current association quantity, and determine a new flight position information of the unmanned aerial vehicle (UAV) base station and a new preset transmission bandwidth allocation ratio of the feedback link based on the current resource feedback utility;

[0031] Jump to the step of adjusting the game strategy in the participant utility model based on the flight position information of the UAV base station, the preset transmission bandwidth allocation ratio of the feedback link, and the replicator dynamics model to obtain the current personal utility of the participant utility model, until a preset game equilibrium state is satisfied, so as to obtain the current personal utility and the current resource feedback utility respectively determined as the target personal utility and the target resource feedback utility.

[0032] Optionally, determining the current resource feedback utility corresponding to the second game model based on the current association quantity includes:

[0033] Fix the utility of the macro base station in the second utility sub-model, and transform the first utility sub-model based on the feedback link transmission information and the current association quantity to obtain a first constrained control problem;

[0034] Fix the utility of the UAV base station in the first utility sub-model, and transform the second utility sub-model based on the preset transmission bandwidth allocation ratio of the feedback link and the current association quantity to obtain a second constrained control problem;

[0035] Determine the optimal solutions corresponding to the first constrained control problem and the second constrained control problem based on the Pontryagin maximum principle to obtain the current resource feedback utility corresponding to the second game model in the open-loop Stackelberg equilibrium state.

[0036] In a second aspect, the present application discloses a UAV base station resource allocation device based on hierarchical game, including:

[0037] An information acquisition module, configured to acquire access link transmission information and feedback link transmission information when the macro base station, the UAV base station, and the user equipment perform wireless information feedback operations, and the flight position information of the UAV base station;

[0038] A problem splitting module, configured to split the UAV base station resource allocation problem into an association relationship optimization problem corresponding to the user level and a transmission resource optimization problem corresponding to the resource level based on the access link transmission information, the backhaul link transmission information, and the flight position information; the association relationship optimization problem is an optimization problem for the network association relationship established between the user terminal and the target base station for wireless information backhaul operations; the target base station includes the macro base station and the UAV base station;

[0039] A game model creation module, configured to respectively model the reception rate of the user terminal and the transmission resources of the target base station to obtain a first game model corresponding to the association relationship optimization problem and a second game model corresponding to the transmission resource optimization problem; the target base station includes the macro base station and the UAV base station;

[0040] An allocation strategy determination module, configured to determine the target individual utility and the target resource backhaul utility respectively corresponding to the first game model and the second game model in the game equilibrium state, and determine the target resource allocation strategy for the UAV base station to perform the wireless information backhaul operation based on the target individual utility and the target resource backhaul utility.

[0041] In a third aspect, the present application discloses an electronic device, including:

[0042] A memory, configured to store a computer program;

[0043] A processor, configured to execute the computer program to implement the foregoing UAV base station resource allocation method based on hierarchical game.

[0044] In a fourth aspect, the present application discloses a computer-readable storage medium, configured to store a computer program, and when the computer program is executed by a processor, the foregoing UAV base station resource allocation method based on hierarchical game is implemented.

[0045] It can be seen that in this application, the access link transmission information and the backhaul link transmission information during the wireless information backhaul operation of the macro base station, the UAV base station, and the user terminal are obtained, as well as the flight position information of the UAV base station; based on the access link transmission information, the backhaul link transmission information, and the flight position information, the UAV base station resource allocation problem is split into an association relationship optimization problem corresponding to the user level and a transmission resource optimization problem corresponding to the resource level; the association relationship optimization problem is an optimization problem for the network association relationship established between the user terminal and the target base station for the wireless information backhaul operation; the target base station includes the macro base station and the UAV base station; the receiving rate of the user terminal and the transmission resources of the target base station are respectively modeled to obtain a first game model corresponding to the association relationship optimization problem and a second game model corresponding to the transmission resource optimization problem; the target individual utility and the target resource backhaul utility corresponding to the first game model and the second game model in the game equilibrium state are determined, and based on the target individual utility and the target resource backhaul utility, the target resource allocation strategy for the UAV base station to perform the wireless information backhaul operation is determined. That is, based on the rules of the predetermined game, the UAV base station resource allocation problem is split into an association relationship optimization problem corresponding to the user level and a transmission resource optimization problem corresponding to the resource level. And a game model corresponding to the optimization problem is generated to analyze the user association state. The resource level uses the user association state evolved from the user level to promote resource trading between base stations to obtain the optimal allocation of UAV base station resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to the provided drawings.

[0047] Figure 1 It is a flowchart of a method for allocating UAV base station resources based on hierarchical game disclosed in this application;

[0048] Figure 2 It is a flowchart of a specific method for allocating UAV base station resources based on hierarchical game disclosed in this application;

[0049] Figure 3 It is a schematic structural diagram of a device for allocating UAV base station resources based on hierarchical game disclosed in this application;

[0050] Figure 4 It is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] Next, in combination with the accompanying drawings in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0052] When performing network communication between an end user and a base station establishment, existing research usually models the association between the end user and the base station establishment as completely rational and static, that is, immediately reaching a network stable state rather than dynamically deriving, which deviates from reality. Therefore, the present application will specifically introduce a method for allocating unmanned aerial vehicle (UAV) base station resources based on hierarchical game theory, which can analyze the user association status to obtain the optimal allocation of UAV base station resources.

[0053] See Figure 1 As shown, the embodiments of the present application disclose a method for allocating UAV base station resources based on hierarchical game theory, including:

[0054] Step S11: Obtain the access link transmission information and the backhaul link transmission information during the wireless information backhaul operation of the macro base station, the UAV base station, and the user terminal, as well as the flight position information of the UAV base station.

[0055] In this embodiment, the obtaining the access link transmission information and the backhaul link transmission information during the wireless information backhaul operation of the macro base station, the UAV base station, and the user terminal, as well as the flight position information of the UAV base station, includes: establishing a network association relationship between the user terminal and the macro base station and the UAV base station based on the user auction method, and establishing a network connection between the macro base station, the UAV base station, and the user terminal based on the network association relationship to perform the wireless information backhaul operation; determining the flight position information of the UAV base station based on the horizontal deployment position, the vertical deployment position, and the corresponding flight period of the UAV base station; determining the first access link transmission rate corresponding to the UAV base station according to the first transmission power of the UAV base station, the noise power spectral density, and the first channel gain set of the user terminal having a network association relationship with the UAV base station; determining the second access link transmission rate corresponding to the macro base station according to the second transmission power of the macro base station, the noise power spectral density, and the second channel gain set of the user terminal having a network association relationship with the macro base station; determining the backhaul link transmission information of the UAV base station based on a preset backhaul link transmission bandwidth allocation ratio.

[0056] Specifically, during network backhaul, there is a macro base station, which is connected to UAV base stations through the backhaul method, and the set of UAV base stations is defined as Distribute among the network models terminal users, and the set of terminal users is defined as . The terminal users purchase the first access link transmission service of the base station through an auction method and pay fees to the purchased base station during the first access link transmission service time. The first access link transmission service time period is defined as Define as the drone base station Charge the first access link transmission service price of each associated terminal user per unit time, and define as the first access link transmission service price that the macro base station charges each associated terminal user per unit time. Define the drone base station and the available bandwidths of the macro base station are respectively Hz and Hz. The base station, according to the number of associated terminal users, the drone base station needs to lease the second backhaul link transmission bandwidth from the macro base station to bear its first access link transmission service, where is defined as the second backhaul link transmission bandwidth allocation ratio. The second backhaul link transmission capacity of the drone base station directly affects the number of its associated terminal users, that is, the more associated users, the more the required second backhaul link transmission capacity, resulting in a higher value of the allocated second backhaul link transmission bandwidth allocation ratio , and vice versa. The payment price for the drone base station to lease the second backhaul link transmission bandwidth from the macro base station is defined as , and the payment price is dynamically determined according to the load condition of the macro base station, so it is defined as a time function . At moment, the second backhaul link transmission bandwidth allocation state of the network model is defined as , and the remaining bandwidth of the macro base station is defined as , and the remaining bandwidth is used for the first access link transmission service between the macro base station and its associated terminal users.

[0057] Among them, the macro base station is deployed at the horizontal position , and the terminal user is deployed at the horizontal position . The drone base station is deployed at the same vertical position and different horizontal positions , and the drone base station flies above the ground at a fixed height during each flight cycle, and the flight cycle can be divided into ​​Equal time slots. Considering safety factors such as terrain or obstacle avoidance, should be set as small as possible. Then, the drone base station at the th time slot, the horizontal position can be expressed as .

[0058] At the th time slot, the channel gain between the drone base station and the end user is defined as:

[0059] ;

[0060] where is the reference channel power per unit distance.

[0061] At time, the transmission rate formula of the first access link of the drone base station is defined as , where is defined as the transmission power of the drone base station , is defined as the set of channel gains of the end users associated with the drone base station , is defined as the noise power spectral density. Further, the set of transmission rates of the first access link of the drone base stations in the network is defined as . The transmission rate of the first access link of the macro base station is defined as . Further, at time, the number of end users associated with the drone base station is defined as , and the number of end users associated with the macro base station is defined as . Even further, at time, the proportion share of the number of end users associated with the drone base station is defined as , , and the proportion share of the number of end users associated with the macro base station is defined as ; where satisfies the condition .

[0062] At time, the transmission capacity of the first access link of the drone base station is defined as:

[0063] ;

[0064] At At this moment, the transmission capacity of the first access link of the macro base station is defined as:

[0065] .

[0066] Step S12: Based on the access link transmission information, the backhaul link transmission information, and the flight position information, split the UAV base station resource allocation problem into an association relationship optimization problem corresponding to the user level and a transmission resource optimization problem corresponding to the resource level; the association relationship optimization problem is an optimization problem for the network association relationship established between the user terminal and the target base station for wireless information backhaul operations; the target base station includes the macro base station and the UAV base station.

[0067] In this embodiment, considering that the pricing strategies of the terminal user association and the bandwidth resource allocation interact with each other and have time-varying characteristics, therefore, this application proposes a dynamic hierarchical game method composed of a user level and a resource level to capture this characteristic. Among them, the association relationship optimization problem corresponding to the user level is an optimization problem for the network association relationship established between the user terminal and the target base station for wireless information backhaul operations.

[0068] Step S13: Model the receiving rate of the user terminal and the transmission resources of the target base station respectively to obtain a first game model corresponding to the association relationship optimization problem and a second game model corresponding to the transmission resource optimization problem.

[0069] In this embodiment, the modeling of the receiving rate of the user terminal and the transmission resources of the target base station respectively to obtain a first game model corresponding to the association relationship optimization problem and a second game model corresponding to the transmission resource optimization problem includes: Modeling the receiving rate of the user terminal based on a preset evolutionary game model to obtain a first game model corresponding to the association relationship optimization problem; Modeling the transmission resources of the UAV base station and the macro base station based on a preset Stackelberg game comprehensive research method to obtain a second game model corresponding to the transmission resource optimization problem.

[0070] Specifically, the method for modeling the receiving rate of the user terminal based on a preset evolutionary game model to obtain a first game model corresponding to the association relationship optimization problem includes: determining the user terminal during the wireless information backhaul operation as a game participant, and determining the current association number of the user terminals having a network association relationship with the target base station as a game strategy; constructing a participant utility model corresponding to the game participant based on the first access link transmission rate, the second access link transmission rate, and the game strategy; constructing a replicator dynamic model corresponding to the game participant, and determining a first game model corresponding to the association relationship optimization problem based on the participant utility model and the replicator dynamic model. That is, at the user level, evolutionary game is used to study the association strategy between the terminal user and the base station. The receiving rate of the terminal user is time-varying because parameters such as the second backhaul link transmission capacity, the first access link transmission capacity, and the number of terminal users associated with the base station have dynamic interactivity, resulting in the terminal user being unable to receive complete channel state information. Therefore, all terminal users in the network model gradually learn and adjust their own association strategies. Expressing the dynamic behavior of the terminal user as an evolutionary game model, all terminal users in the evolutionary game model will obtain the same personal utility in the game equilibrium state, and there is uniqueness.

[0071] Specifically, the method for modeling the transmission resources of the drone base station and the macro base station based on a preset Stackelberg game comprehensive research method to obtain a second game model corresponding to the transmission resource optimization problem includes: determining the wireless information backhaul profit of the drone base station during the wireless information backhaul operation based on the difference between the fee charged by the drone base station to the user terminal and the fee paid to the macro base station in the user bidding method; determining the wireless information backhaul cost corresponding to the first access link transmission rate, the second access link transmission rate, and the preset backhaul link transmission bandwidth allocation ratio, and modeling the utility of the drone base station based on the wireless information backhaul profit and the wireless information backhaul cost to obtain a first utility sub-model; modeling the utility of the macro base station based on the difference between the profit obtained by the macro base station from the drone base station and the user terminal and the cost incurred during the wireless information backhaul operation to obtain a second utility sub-model, and determining a second game model corresponding to the transmission resource optimization problem based on the first utility sub-model and the second utility sub-model. There are characteristics of limited second backhaul link transmission capacity and dynamic user association base station strategies at the user level , which prompt the drone base station to need to obtain from the first access link transmission rate , the second backhaul link transmission bandwidth allocation and the price strategy with the macro base station Make optimal decisions in three aspects. For example, increasing the transmission rate of the first access link of the drone base station can improve the utility of the associated end-users, which will prompt more end-users to access the drone base station. However, this result will cause the drone base station to lease more transmission bandwidth resources of the second backhaul link from the macro base station to increase the transmission capacity of the second backhaul link of the drone base station, further affecting the pricing of wireless bandwidth resources by the macro base station . To accurately analyze the dynamic process, the present invention uses the Stackelberg game to model the macro base station as the leader and the drone base station as the follower, and users obtain the open-loop Stackelberg equilibrium as the optimal strategic solution through calculation.

[0072] Step S14: Determine the target individual utility and the target resource backhaul utility corresponding to the first game model and the second game model in the game equilibrium state respectively, and determine the target resource allocation strategy for the drone base station to perform the wireless information backhaul operation based on the target individual utility and the target resource backhaul utility.

[0073] In this embodiment, the determining the target individual utility and the target resource backhaul utility corresponding to the first game model and the second game model in the game equilibrium state respectively includes: adjusting the game strategy in the participant utility model based on the flight position information of the drone base station, the preset backhaul link transmission bandwidth allocation ratio, and the replicator dynamic model to obtain the current individual utility of the participant utility model; determining the new current association quantity of the user terminals having a network association relationship with the target base station based on the current individual utility; determining the current resource backhaul utility corresponding to the second game model based on the current association quantity, and determining the new flight position information and the new preset backhaul link transmission bandwidth allocation ratio of the drone base station based on the current resource backhaul utility; jumping to the step of adjusting the game strategy in the participant utility model based on the flight position information of the drone base station, the preset backhaul link transmission bandwidth allocation ratio, and the replicator dynamic model to obtain the current individual utility of the participant utility model, until the preset game equilibrium state is satisfied, so as to obtain the current individual utility and the current resource backhaul utility as the target individual utility and the target resource backhaul utility respectively. Generally speaking, the user layer uses the optimized resource variables obtained from the resource layer to model the replicator dynamics to analyze the user association state. The resource layer uses the user association state evolved from the user layer to promote resource transactions between base stations to obtain the optimal values in terms of the transmission throughput of the first access link, the backhaul bandwidth pricing, and the allocation.

[0074] Among them, the evolutionary game model of the user layer is defined as: Participants: Each end-user in the network Define as the participants in the evolutionary game. Strategy: The strategy of each said participant is base station association, and the strategy set is defined as . Association quantity state: Define the association quantity state as , and the said association quantity state is expressed as the proportion of participants who choose the strategy at the moment . Define as the set of association quantity states. Utility: Given the set of said association quantity states , the second backhaul link transmission bandwidth allocation ratio and the first access link transmission rate , the utility model of the participant who chooses the strategy is defined as:

[0075] ; (1)

[0076] where represents a fixed mapping factor.

[0077] Replicator dynamics: The said replicator dynamics is expressed as the utility information of each participant choosing different strategies , and is defined as:

[0078] ;

[0079] where represents the learning rate that controls the frequency of participants adjusting their own strategies; is defined as the average utility of the entire network at the moment . Define as the initial setting state, and the participant adjusts its own association quantity state according to the participant utility model (1) to find a higher utility , for example, if the strategy provides a higher utility than the average utility, that is , more participants choose to associate with the base station , and this process will generate a higher said association quantity state .

[0080] At the moment , for the said strategy , if the first access link transmission rate and the second backhaul link transmission bandwidth allocation are measurable within the time interval , then the said replicator dynamics obtained by the present invention through the participant utility model under the condition of setting the initial value has a unique solution 。

[0081] Here, a proof is given for the statement that has a unique solution. At time, for the strategy , the formula is set. For the associated quantity state set , it is continuously differentiable. If the transmission rate of the first access link and the transmission bandwidth allocation of the second backhaul link are measurable within the time interval , and the strategy is fixed within the time interval , then the formula is also measurable within the time interval . Further, within any fixed region and closed bounded set , there always exists a positive number that can construct an integrable function , where . Obviously, for all , the inequalities and are satisfied. Define , and the inequality can be derived from the equation , which verifies the existence of a global Lipschitz condition in the formula . Through the above analysis, it is proved that the replicator dynamics has a globally unique solution. Among them, the utility of each UAV base station consists of economic profit and resource cost. Determining the current resource backhaul utility corresponding to the second game model based on the current associated quantity includes: fixing the utility of the macro base station in the second utility sub-model, and transforming the first utility sub-model based on the backhaul link transmission information and the current associated quantity to obtain a first constrained control problem; fixing the utility of the UAV base station in the first utility sub-model, and transforming the second utility sub-model based on the preset backhaul link transmission bandwidth allocation ratio and the current associated quantity to obtain a second constrained control problem; determining the optimal solutions corresponding to the first constrained control problem and the second constrained control problem based on the Pontryagin maximum principle to obtain the current resource backhaul utility corresponding to the second game model in the open-loop Stackelberg equilibrium state.

[0082] Specifically, the UAV base station

[0083] ​The profit comes from the difference between the fees charged to the associated end-users and the cost of renting the transmission capacity of the second backhaul link from the macro base station. The difference in the fees paid, which is defined as . In addition, the drone base station also takes into account the transmission rate of the first access link and the allocation rate of the transmission bandwidth of the second backhaul link to generate the cost. Thus far, the utility of the drone base station at time is modeled (the first utility sub-model) as:

[0084] ; (2)

[0085] where the scalar represents the transmission rate of the first access link for the end-user, represents the profit coefficient, represents the cost coefficient. Formula (2) reflects that the utility of the drone base station depends on the pricing of the transmission resources of the second backhaul link by the macro base station and the state of the number of associations in the user hierarchy . The purpose of the drone base station is to maximize its cumulative revenue, which is defined as maximizing its own utility while optimizing its own consumption. Under the condition that the pricing strategy of the macro base station is fixed, the cumulative revenue is an optimal control problem constrained by the transmission capacity of the second backhaul link and the evolution of the state of the number of associations, which can be expressed as:

[0086] ; (3)

[0087] where, is the discount rate representing the discounted value of future profits; the optimization objective C0 is expressed as the average profit of the drone base station within the time period ; the constraint condition C1 is expressed as the transmission capacity of the second backhaul link of the drone base station being greater than its first access link transmission capacity; the constraint condition C2 is expressed as the replicator dynamics of the selection strategy in the user hierarchy; the constraint condition C3 is expressed as setting the initial strategy value at the initial time 0.

[0088] The utility of the macro base station at time is expressed as the difference between the profit obtained from the associated end-users and the drone base station and the cost paid, which is expressed in the following form:

[0089] . (4)

[0090] The utility of the macro base station Resource allocation relying on the second feedback link and the associated quantity status in the user hierarchy , which is transformed into the following optimal control problem:

[0091] ; (5)

[0092] Among them, the optimization objective is expressed as the average profit of the macro base station during the transmission service time period of the first access link ; The constraint condition is expressed as the replicator dynamics of the selection strategy in the user hierarchy ; The constraint condition is expressed as setting the initial strategy value at the initial time 0 .

[0093] When the resource hierarchy reaches the open-loop Stackelberg equilibrium state, at time, the obtained and of the problem (3), as well as the obtained of the problem (4) are used as the optimal selection strategy of the resource hierarchy, where the necessary condition for setting the open-loop Stackelberg equilibrium state is the Pontryagin maximum principle. The drone base station The augmented Hamiltonian function is expressed as:

[0094] ; (6)

[0095] Among them, ; is defined as the cost function of the drone base station and the associated quantity status ; is expressed as the Lagrange multiplier of the constraint condition C1. Further, the maximized Hamiltonian function of the drone base station is expressed as: ; (7)

[0096] The optimal first access link transmission capacity of the drone base station is expressed as:

[0097] .

[0098] The optimal second feedback link transmission bandwidth allocation of the drone base station is expressed as:

[0099] .

[0100] Here, it is proved that for the Pontryagin maximum principle, maximizing the Hamiltonian equation (6) is a necessary condition for the optimization problem (5) to obtain an optimal control strategy. The Pontryagin maximum principle states that maximizing the Hamiltonian equation (6) is a necessary condition for the optimization problem (5) to obtain an optimal control strategy, while satisfying the following necessary optimality conditions:

[0101] ; (8)

[0102] ; (9)

[0103] .

[0104] Because the Hamiltonian equation (6) is concave with respect to the first access link transmission rate and the second fronthaul link transmission bandwidth allocation the first access link transmission rate obtained by calculating through the formula (8) and the second fronthaul link transmission bandwidth allocation obtained by calculating through the formula (9) are the optimal values of the unmanned aerial vehicle (UAV) base station .

[0105] For the open-loop Stackelberg equilibrium of the macro base station, similarly, the maximized Hamiltonian function of the macro base station is expressed as:

[0106] ; (10)

[0107] where ; ; is defined as the cost variable of the cost function , where .

[0108] Furthermore, the maximized Hamiltonian function of the macro base station is expressed as:

[0109] ; (11)

[0110] ;

[0111] .

[0112] Similarly, according to the Pontryagin maximum principle, at the moment , substituting the optimal strategy obtained by the UAV base station into the Hamiltonian equation (10) of the macro base station for pricing ​is a convex function, which ensures that the pricing strategy obtained by the macro base station at the moment through the equation (11) is the optimal value.

[0113] The obtained optimal control strategy using the Pontryagin maximum principle satisfies the necessary conditions for the optimal value. In addition, the Hamiltonian formula (6) of the UAV base station and the Hamiltonian formula (10) of the macro base station are both concave, continuously differentiable for the set of associated quantity states These verification conditions all satisfy the sufficient conditions for the Stackelberg equilibrium, and can prove that the obtained optimal control strategy reaches the open-loop Stackelberg equilibrium state.

[0114] It can be seen that in this embodiment, as Figure 2 shown, the access link transmission information and the backhaul link transmission information during the wireless information backhaul operation of the macro base station, the UAV base station and the user terminal are obtained, as well as the flight position information of the UAV base station; based on the access link transmission information, the backhaul link transmission information, and the flight position information, the UAV base station resource allocation problem is split into an association relationship optimization problem corresponding to the user level and a transmission resource optimization problem corresponding to the resource level; the association relationship optimization problem is an optimization problem for the network association relationship established between the user terminal and the target base station for wireless information backhaul operation; the target base station includes the macro base station and the UAV base station; the receiving rate of the user terminal and the transmission resources of the target base station are respectively modeled to obtain a first game model corresponding to the association relationship optimization problem and a second game model corresponding to the transmission resource optimization problem; the target personal utility and the target resource backhaul utility corresponding to the first game model and the second game model in the game equilibrium state are determined, and the target resource allocation strategy for the UAV base station to perform the wireless information backhaul operation is determined based on the target personal utility and the target resource backhaul utility. That is, based on the user level of evolutionary game and the resource level of Stackelberg game. The user level uses the optimized resource variables obtained from the resource level to model the replicator dynamics to analyze the user association state. The resource level uses the user association state evolved from the user level to promote resource trading between base stations to obtain the optimal values in terms of the first access link transmission throughput, backhaul bandwidth pricing and allocation.

[0115] Referring to Figure 3 the above, the embodiment of the present application also correspondingly discloses a UAV base station resource allocation device based on hierarchical game, including:

[0116] An information acquisition module 11, configured to acquire access link transmission information and backhaul link transmission information during wireless information backhaul operations of a macro base station, a drone base station, and a user terminal, as well as flight position information of the drone base station;

[0117] A problem splitting module 12, configured to split the drone base station resource allocation problem into an associated relationship optimization problem corresponding to the user level and a transmission resource optimization problem corresponding to the resource level based on the access link transmission information, the backhaul link transmission information, and the flight position information; the associated relationship optimization problem is an optimization problem for the network associated relationship established between the user terminal and a target base station for wireless information backhaul operations; the target base station includes the macro base station and the drone base station;

[0118] A game model creation module 13, configured to model the reception rate of the user terminal and the transmission resources of the target base station respectively to obtain a first game model corresponding to the associated relationship optimization problem and a second game model corresponding to the transmission resource optimization problem; the target base station includes the macro base station and the drone base station;

[0119] An allocation strategy determination module 14, configured to determine a target personal utility and a target resource backhaul utility respectively corresponding to the first game model and the second game model in a game equilibrium state, and determine a target resource allocation strategy for the drone base station to perform the wireless information backhaul operation based on the target personal utility and the target resource backhaul utility.

[0120] It can be seen that in this embodiment, the drone base station resource allocation problem is split into an associated relationship optimization problem corresponding to the user level and a transmission resource optimization problem corresponding to the resource level based on a predetermined game rule. And a game model corresponding to the optimization problem is generated to analyze the user association state. The resource level uses the user association state evolved from the user level to promote resource trading between base stations to obtain the optimal allocation of drone base station resources.

[0121] In some specific embodiments, the information acquisition module 11 may specifically include:

[0122] A connection relationship establishment unit, configured to establish a network associated relationship between the user terminal and the macro base station and the drone base station based on a user bidding method, and establish a network connection between the macro base station, the drone base station, and the user terminal based on the network associated relationship to perform wireless information backhaul operations;

[0123] A position information acquisition unit, configured to determine the flight position information of the drone base station based on the horizontal deployment position, the vertical deployment position, and the corresponding flight period of the drone base station;

[0124] A first transmission rate determination unit, configured to determine a first access link transmission rate corresponding to the UAV base station according to the first transmission power of the UAV base station, the noise power spectral density, and a set of first channel gains of user terminals having a network association relationship with the UAV base station;

[0125] A second transmission rate determination unit, configured to determine a second access link transmission rate corresponding to the macro base station according to the second transmission power of the macro base station, the noise power spectral density, and a set of second channel gains of user terminals having a network association relationship with the macro base station;

[0126] A backhaul information determination unit, configured to determine backhaul link transmission information of the UAV base station based on a preset backhaul link transmission bandwidth allocation ratio.

[0127] In some specific embodiments, the game model creation module 13 may specifically include:

[0128] A first game model determination sub-module, configured to model the reception rate of the user terminal based on a preset evolutionary game model to obtain a first game model corresponding to the association relationship optimization problem;

[0129] A second game model determination sub-module, configured to model the transmission resources of the UAV base station and the macro base station based on a preset Stackelberg game comprehensive research method to obtain a second game model corresponding to the transmission resource optimization problem.

[0130] In some specific embodiments, the first game model determination sub-module may specifically include:

[0131] A game information determination unit, configured to determine the user terminal during wireless information backhaul operation as a game participant, and determine the current association quantity of user terminals having a network association relationship with the target base station as a game strategy;

[0132] A utility model determination unit, configured to construct a participant utility model corresponding to the game participant based on the first access link transmission rate, the second access link transmission rate, and the game strategy;

[0133] A first game model determination unit, configured to construct a replicator dynamics model corresponding to the game participant, and determine a first game model corresponding to the association relationship optimization problem based on the participant utility model and the replicator dynamics model.

[0134] In some specific embodiments, the second game model determination sub-module may specifically include:

[0135] A profit determination unit, configured to determine the wireless information backhaul profit of the drone base station during the wireless information backhaul operation based on the difference between the fees charged by the drone base station to the user terminal and the fees paid to the macro base station in the user bidding method;

[0136] A utility sub-model determination unit, configured to determine the wireless information backhaul cost corresponding to the first access link transmission rate, the second access link transmission rate, and the preset backhaul link transmission bandwidth allocation ratio, and model the utility of the drone base station based on the wireless information backhaul profit and the wireless information backhaul cost to obtain a first utility sub-model;

[0137] A second game model determination unit, configured to model the utility of the macro base station based on the difference between the profit obtained by the macro base station from the drone base station and the user terminal and the cost incurred during the wireless information backhaul operation to obtain a second utility sub-model, and determine a second game model corresponding to the transmission resource optimization problem based on the first utility sub-model and the second utility sub-model.

[0138] In some specific embodiments, the allocation strategy determination module 14 may specifically include:

[0139] A personal utility determination unit, configured to adjust the game strategy in the participant utility model based on the flight position information of the drone base station, the preset backhaul link transmission bandwidth allocation ratio, and the replicator dynamics model to obtain the current personal utility of the participant utility model;

[0140] A current association number determination unit, configured to determine a new current association number of the user terminal having a network association relationship with the target base station based on the current personal utility;

[0141] A resource backhaul utility determination sub-module, configured to determine the current resource backhaul utility corresponding to the second game model based on the current association number, and determine a new flight position information and a new preset backhaul link transmission bandwidth allocation ratio of the drone base station based on the current resource backhaul utility;

[0142] A step jump unit, configured to jump to the step of adjusting the game strategy in the participant utility model based on the flight position information of the drone base station, the preset backhaul link transmission bandwidth allocation ratio, and the replicator dynamics model to obtain the current personal utility of the participant utility model until a preset game equilibrium state is satisfied, so as to obtain the current personal utility and the current resource backhaul utility are respectively determined as the target personal utility and the target resource backhaul utility.

[0143] In some specific embodiments, the resource backhaul utility determination sub-module may specifically include:

[0144] A first constraint control problem determination unit, configured to fix the utility of the macro base station in the second utility sub-model, and transform the first utility sub-model based on the backhaul link transmission information and the current association quantity to obtain a first constraint control problem;

[0145] A second constraint control problem determination unit, configured to fix the utility of the drone base station in the first utility sub-model, and transform the second utility sub-model based on the preset backhaul link transmission bandwidth allocation ratio and the current association quantity to obtain a second constraint control problem;

[0146] A current resource backhaul utility determination unit, configured to determine optimal solutions corresponding to the first constraint control problem and the second constraint control problem based on the Pontryagin maximum principle, so as to obtain the current resource backhaul utility corresponding to the second game model in the open-loop Stackelberg equilibrium state.

[0147] Furthermore, an embodiment of the present application also discloses an electronic device, Figure 4 It is a structural diagram of an electronic device 20 shown according to an exemplary embodiment, and the content in the figure cannot be regarded as any limitation on the scope of use of the present application.

[0148] Figure 4 It is a schematic structural diagram of an electronic device 20 provided by an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the method for allocating drone base station resources based on hierarchical game disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0149] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows is any communication protocol applicable to the technical solution of the present application, and no specific limitation is imposed on it here; the input / output interface 25 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application requirements, and no specific limitation is made here.

[0150] In addition, as a carrier for resource storage, the memory 22 may be a read-only memory, a random access memory, a magnetic disk, or an optical disc, etc., and the resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage method may be temporary storage or permanent storage.

[0151] Among them, the operating system 221 is used to manage and control each hardware device and computer program 222 on the electronic device 20, and it can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to complete the method for allocating drone base station resources based on hierarchical game executed by the electronic device 20 disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs that can be used to complete other specific tasks.

[0152] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the method for allocating drone base station resources based on hierarchical game disclosed above. For the specific steps of this method, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details will not be repeated here.

[0153] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and reference can be made to the description of the method part for related parts.

[0154] Those skilled in the art can further realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0155] The steps of the method or algorithm described in combination with the embodiments disclosed in this article can be directly implemented by hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0156] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0157] The technical solutions provided in this application have been introduced in detail above. Specific examples are used in this text to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for allocating resources of drone base stations based on hierarchical game, characterized in that: include: Acquire access link transmission information and backhaul link transmission information of the macro base station, the drone base station and the user end when performing wireless information backhaul operations, as well as the flight position information of the drone base station; Based on the access link transmission information, the backhaul link transmission information, and the flight position information, the UAV base station resource allocation problem is split into an association relationship optimization problem corresponding to the user level and a transmission resource optimization problem corresponding to the resource level; The association relationship optimization problem is an optimization problem for a network association relationship established between the user terminal and the target base station for performing a wireless information backhaul operation; The target base station includes the macro base station and the drone base station; Modeling the receiving rate of the user terminal and the transmission resources of the target base station respectively to obtain a first game model corresponding to the association relationship optimization problem and a second game model corresponding to the transmission resource optimization problem; Determine the target personal utility and target resource return utility corresponding to the first game model and the second game model respectively in the game equilibrium state, and determine the target resource allocation strategy of the drone base station for performing the wireless information return operation based on the target personal utility and the target resource return utility.

2. The method for allocating resources of drone base stations based on hierarchical game according to claim 1 is characterized in that: The obtaining of access link transmission information and return link transmission information when the macro base station, the drone base station and the user end perform wireless information backhaul operations, as well as the flight position information of the drone base station, includes: Establishing a network association relationship between the user terminal and the macro base station and the drone base station based on a user auction method, and establishing a network connection between the macro base station, the drone base station and the user terminal based on the network association relationship to perform a wireless information backhaul operation; Determine the flight position information of the drone base station based on the horizontal deployment position, the vertical deployment position and the corresponding flight period of the drone base station; Determine a first access link transmission rate corresponding to the drone base station according to a first transmission power of the drone base station, a noise power spectrum density, and a first channel gain set of a user terminal having a network association relationship with the drone base station; Determine a second access link transmission rate corresponding to the macro base station according to a second transmission power of the macro base station, a noise power spectrum density, and a second channel gain set of a user terminal having a network association relationship with the macro base station; The backhaul link transmission information of the drone base station is determined based on a preset backhaul link transmission bandwidth allocation ratio.

3. The method for allocating resources of drone base stations based on hierarchical game according to claim 2 is characterized in that: The modeling of the receiving rate of the user terminal and the transmission resources of the target base station respectively to obtain a first game model corresponding to the association relationship optimization problem and a second game model corresponding to the transmission resource optimization problem includes: Modeling the receiving rate of the user terminal based on a preset evolutionary game model to obtain a first game model corresponding to the association relationship optimization problem; Based on the preset Stackelberg game comprehensive research method, the transmission resources of the UAV base station and the macro base station are modeled to obtain a second game model corresponding to the transmission resource optimization problem.

4. The method for allocating resources of drone base stations based on hierarchical game according to claim 3 is characterized in that: The modeling of the receiving rate of the user terminal based on the preset evolutionary game model to obtain a first game model corresponding to the association relationship optimization problem includes: Determine the user terminal when performing the wireless information backhaul operation as a game participant, and determine the current number of associations of the user terminals that have a network association relationship with the target base station as a game strategy; Constructing a participant utility model corresponding to the game participant based on the first access link transmission rate, the second access link transmission rate and the game strategy; A replica dynamic model corresponding to the game participant is constructed, and a first game model corresponding to the association relationship optimization problem is determined based on the participant utility model and the replica dynamic model.

5. The method for allocating resources of drone base stations based on hierarchical game according to claim 4 is characterized in that: The transmission resources of the UAV base station and the macro base station are modeled based on the preset Stackelberg game comprehensive research method to obtain a second game model corresponding to the transmission resource optimization problem, including: Determine the wireless information backhaul profit of the drone base station during the wireless information backhaul operation based on the difference between the fee charged by the drone base station to the user terminal and the fee paid to the macro base station in the user auction method; Determine the wireless information backhaul cost corresponding to the first access link transmission rate, the second access link transmission rate, and the preset backhaul link transmission bandwidth allocation ratio, and model the utility of the drone base station based on the wireless information backhaul profit and the wireless information backhaul cost to obtain a first utility sub-model; The utility of the macro base station is modeled based on the difference between the profit and the cost obtained by the macro base station from the drone base station and the user terminal during the wireless information backhaul operation to obtain a second utility sub-model, and the second game model corresponding to the transmission resource optimization problem is determined based on the first utility sub-model and the second utility sub-model.

6. The method for allocating resources of drone base stations based on hierarchical game according to claim 5 is characterized in that: The determining the target personal utility and the target resource return utility respectively corresponding to the first game model and the second game model in a game equilibrium state includes: Adjusting the game strategy in the participant utility model based on the flight position information of the drone base station, the preset backhaul link transmission bandwidth allocation ratio and the replicator dynamic model to obtain the current personal utility of the participant utility model; Determine a new current association number of a user terminal having a network association relationship with the target base station based on the current personal utility; Determine the current resource return utility corresponding to the second game model based on the current association quantity, and determine the new flight position information of the drone base station and the new preset return link transmission bandwidth allocation ratio based on the current resource return utility; Jump to the step of adjusting the game strategy in the participant utility model based on the flight position information of the drone base station, the preset return link transmission bandwidth allocation ratio and the replicator dynamic model to obtain the current personal utility of the participant utility model, until the preset game equilibrium state is met, so as to determine the current personal utility and the current resource return utility as the target personal utility and the target resource return utility respectively.

7. The method for allocating resources of drone base stations based on hierarchical game according to claim 6 is characterized in that: The determining, based on the current association quantity, the current resource return utility corresponding to the second game model includes: Fixing the utility of the macro base station in the second utility sub-model, and transforming the first utility sub-model based on the backhaul link transmission information and the current association quantity to obtain a first constraint control problem; Fixing the utility of the drone base station in the first utility sub-model, and transforming the second utility sub-model based on the preset backhaul link transmission bandwidth allocation ratio and the current association number to obtain a second constraint control problem; Based on the Pontryagin maximum principle, the optimal solutions corresponding to the first constraint control problem and the second constraint control problem are determined to obtain the current resource feedback utility corresponding to the second game model in the open-loop Stackelberg equilibrium state.

8. A UAV base station resource allocation device based on hierarchical game, characterized in that: include: An information acquisition module, used to acquire access link transmission information and return link transmission information of the macro base station, the drone base station and the user end when performing wireless information backhaul operations, as well as flight position information of the drone base station; A problem splitting module is used to split the UAV base station resource allocation problem into an association relationship optimization problem corresponding to the user level and a transmission resource optimization problem corresponding to the resource level based on the access link transmission information, the backhaul link transmission information, and the flight position information; The association relationship optimization problem is an optimization problem for a network association relationship established between the user terminal and the target base station for performing a wireless information backhaul operation; the target base station includes the macro base station and the drone base station; A game model creation module, used to model the receiving rate of the user terminal and the transmission resources of the target base station respectively, so as to obtain a first game model corresponding to the association relationship optimization problem and a second game model corresponding to the transmission resource optimization problem; the target base station includes the macro base station and the drone base station; An allocation strategy determination module is used to determine the target personal utility and target resource return utility corresponding to the first game model and the second game model respectively in the game equilibrium state, and determine the target resource allocation strategy of the drone base station for performing the wireless information return operation based on the target personal utility and the target resource return utility.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the method for allocating resources of a drone base station based on layered game as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, implements the drone base station resource allocation method based on layered game as described in any one of claims 1 to 7.

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