A method, device and equipment for allocating resources of a UAV base station based on hierarchical game, and a storage medium

Abstract

The application discloses a kind of unmanned aerial vehicle base station resource allocation method, device and equipment based on hierarchical game, storage medium, it is related to data communication technical field, including: obtaining the access link transmission information and backhaul link transmission information when macro base station, unmanned aerial vehicle base station and user end carry out wireless information backhaul operation, and the flight position information of unmanned aerial vehicle base station, and the unmanned aerial vehicle base station resource allocation problem is split into the associated relationship optimization problem corresponding to user level and the transmission resource optimization problem corresponding to resource level;The receiving rate of user end and the transmission resource of target base station are modeled respectively to obtain corresponding first game model and second game model;Determine the corresponding target personal utility and target resource backhaul utility of two models in game equilibrium state to obtain the target resource allocation strategy of unmanned aerial vehicle base station for wireless information backhaul operation.This way, the user association state can be accurately analyzed, and the optimal allocation of unmanned aerial vehicle base station resources is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data communication, in particular to a method and device for allocating resources of a UAV base station based on hierarchical game, and a storage medium. BACKGROUND

[0002] In 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, i.e., immediately reaching a network stable state rather than dynamically deriving, which deviates from reality. In actual implementation of wireless backhaul, both the terminal user and the UAV base station have time-varying decisions. Therefore, a static model cannot accurately capture the dynamics resulting therefrom.

[0003] Therefore, how to better model the association between the terminal user and the base station, analyze the user association state, and achieve optimal allocation of resources of the UAV base station is a problem to be solved. SUMMARY

[0004] Therefore, the present application aims to provide a method and device for allocating resources of a UAV base station based on hierarchical game, which can accurately analyze the user association state and achieve optimal allocation of resources of the UAV base station. The specific scheme is 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, comprising:

[0006] obtaining access link transmission information and backhaul link transmission information of a macro base station, a UAV base station and a user terminal when performing wireless information backhaul operation, and 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 resource allocation problem of the UAV base station into an association relationship optimization problem corresponding to a user level, and a transmission resource optimization problem corresponding to a resource level; the association relationship optimization problem is an optimization problem for a network association relationship established between the user terminal and a target base station for performing wireless information backhaul operation; the target base station includes the macro base station and the UAV base station;

[0008] respectively modeling a reception rate of the user terminal and a transmission resource 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] The target individual utility and target resource backhaul utility corresponding to the first game model and the second game model in the game equilibrium state are determined respectively, and the target resource allocation strategy for the UAV base station to perform the wireless information backhaul operation is determined based on the target individual utility and the target resource backhaul utility.

[0010] Optionally, obtaining the access link transmission information and backhaul link transmission information of the macro base station, the UAV base station, and the user terminal during wireless information backhaul operations, as well as the flight position information of the UAV base station, includes:

[0011] A network association relationship is established between the user terminal and the macro base station and the drone base station based on the user bidding method, and a network connection is established between the macro base station, the drone base station and the user terminal based on the network association relationship to perform wireless information backhaul operation.

[0012] The flight position information of the drone base station is determined based on its horizontal and vertical deployment positions and the corresponding flight cycle.

[0013] The first access link transmission rate corresponding to the drone base station is determined based on the first transmission power of the drone base station, the noise power spectral density, and the first channel gain set of the user terminals that have a network association with the drone base station.

[0014] The transmission rate of the second access link corresponding to the macro base station is determined based on the second transmission power of the macro base station, the noise power spectral density, and the second channel gain set of the user terminals that have a network association with the macro base station.

[0015] The backhaul link transmission information of the UAV base station is determined based on the preset backhaul link transmission bandwidth allocation ratio.

[0016] Optionally, the step of 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 correlation optimization problem and a second game model corresponding to the transmission resource optimization problem includes:

[0017] The receiving rate of the user terminal is modeled based on a preset evolutionary game model to obtain the first game model corresponding to the correlation optimization problem.

[0018] Based on the pre-defined Stackelberg game comprehensive research method, the transmission resources of the UAV base station and the macro base station are modeled to obtain the second game model corresponding to the transmission resource optimization problem.

[0019] Optionally, the step of modeling the receiving rate of the user terminal based on a preset evolutionary game model to obtain the first game model corresponding to the correlation optimization problem includes:

[0020] The user terminal performing the wireless information backhaul operation is identified as a game participant, and the current number of user terminals that have a network association relationship with the target base station is identified as the game strategy.

[0021] Construct a participant utility model for the game participants 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 participants, and determine the first game model corresponding to the correlation optimization problem based on the participant utility model and the replicator dynamic model.

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

[0024] 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, the wireless information backhaul profit of the drone base station in the wireless information backhaul operation is determined.

[0025] 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 is determined, and the utility of the UAV base station is modeled based on the wireless information backhaul profit and the wireless information backhaul cost to obtain a first utility sub-model.

[0026] The utility of the macro base station is modeled based on the difference between the profit and cost obtained by the macro base station from the UAV base station and the user terminal during the wireless information backhaul operation to obtain a second utility sub-model. Based on the first utility sub-model and the second utility sub-model, a second game model corresponding to the transmission resource optimization problem is determined.

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

[0028] Based on the flight location information of the UAV base station, the preset backhaul link transmission bandwidth allocation ratio, and the replicator dynamic model, the game strategy in the participant utility model is adjusted to obtain the current personal utility of the participant utility model.

[0029] Based on the current personal utility, determine the new current number of user terminals that have a network association with the target base station;

[0030] Based on the current number of associations, the current resource backhaul utility corresponding to the second game model is determined, and based on the current resource backhaul utility, the new flight position information of the UAV base station and the new preset backhaul link transmission bandwidth allocation ratio are determined.

[0031] The process jumps to the step of adjusting the game strategy in the participant utility model based on the flight location information of the UAV 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, until a preset game equilibrium state is satisfied, so as to determine the current personal utility and the current resource backhaul utility as the target personal utility and the target resource backhaul utility, respectively.

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

[0033] The utility of the macro base station in the second utility sub-model is fixed, and the first utility sub-model is transformed based on the backhaul link transmission information and the current number of associations to obtain the first constraint control problem;

[0034] The utility of the UAV base station in the first utility sub-model is fixed, and the second utility sub-model is transformed based on the preset backhaul link transmission bandwidth allocation ratio and the current number of associations to obtain the second constraint control problem;

[0035] Based on the Pontryagin maximum principle, the optimal solutions corresponding to the first and second constrained control problems are determined to obtain the current resource backhaul utility of the second game model under the open-loop Stackelberg equilibrium state.

[0036] Secondly, this application discloses a drone base station resource allocation device based on hierarchical game theory, comprising:

[0037] The information acquisition module is used to acquire access link transmission information and backhaul link transmission information when the macro base station, the UAV base station and the user terminal are performing wireless information backhaul operations, as well as the flight position information of the UAV base station.

[0038] The problem decomposition module is used to decompose the UAV base station resource allocation problem into a user-level correlation optimization problem and a resource-level transmission resource optimization problem based on the access link transmission information, the backhaul link transmission information, and the flight position information. The correlation optimization problem is an optimization problem of the network correlation 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] The game model creation module is 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 correlation 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] The allocation strategy determination module is used to determine the target individual utility and target resource backhaul utility corresponding to the first game model and the second game model in the game equilibrium state, respectively, and to 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] Thirdly, this application discloses an electronic device, including:

[0042] Memory, used to store computer programs;

[0043] A processor is used to execute the computer program to implement the aforementioned method for allocating unmanned aerial vehicle (UAV) base station resources based on hierarchical game theory.

[0044] Fourthly, this application discloses a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the aforementioned method for allocating unmanned aerial vehicle (UAV) base station resources based on hierarchical game theory.

[0045] As can be seen, in this application, access link transmission information and backhaul link transmission information are obtained when a macro base station, a drone base station, and a user terminal perform 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 drone base station resource allocation problem is decomposed into a user-level correlation optimization problem and a resource-level transmission resource optimization problem. The correlation optimization problem is an optimization problem of the network correlation 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 drone base station. The receiving rate of the user terminal and the transmission resources of the target base station are modeled to obtain a first game model corresponding to the correlation optimization problem and a second game model corresponding to the transmission resource optimization problem. The target personal utility and 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 drone 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 predetermined game rules, the UAV base station resource allocation problem is broken down into an association optimization problem corresponding to the user level and a transmission resource optimization problem corresponding to the resource level. A game model corresponding to the optimization problem is then generated to analyze the user association state. The resource level uses the user association states evolved from the user level to facilitate resource transactions between base stations to achieve the optimal allocation of UAV base station resources. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0047] Figure 1 This application discloses a flowchart of a method for allocating unmanned aerial vehicle (UAV) base station resources based on hierarchical game theory.

[0048] Figure 2 This application discloses a specific method for allocating unmanned aerial vehicle (UAV) base station resources based on hierarchical game theory.

[0049] Figure 3 This is a schematic diagram of a drone base station resource allocation device based on hierarchical game theory disclosed in this application;

[0050] Figure 4 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0052] Existing research typically models the relationship between end users and base stations as completely rational and static, meaning it immediately reaches a stable network state rather than being dynamically derived, which deviates from reality. Therefore, this application will specifically introduce a hierarchical game theory-based method for allocating UAV base station resources, which can analyze user relationship states to obtain the optimal allocation of UAV base station resources.

[0053] See Figure 1 As shown in the figure, this application discloses a method for allocating unmanned aerial vehicle (UAV) base station resources based on hierarchical game theory, including:

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

[0055] In this embodiment, obtaining the access link transmission information and backhaul link transmission information of the macro base station, the UAV base station, and the user terminal during wireless information backhaul operations, 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 a user bidding 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 wireless information backhaul operations; determining the flight position information of the UAV base station based on its horizontal deployment position, vertical deployment position, and corresponding flight cycle; determining the first access link transmission rate corresponding to the UAV base station based on the first transmission power, noise power spectral density, and the first channel gain set of the user terminal with which the UAV base station has a network association relationship; determining the second access link transmission rate corresponding to the macro base station based on the second transmission power, noise power spectral density, and the second channel gain set of the user terminal with which the macro base station has a network association relationship; and determining the backhaul link transmission information of the UAV base station based on a preset backhaul link transmission bandwidth allocation ratio.

[0056] Specifically, network backhaul involves a macro base station, which communicates with [other entities] via backhaul. A set of drone base stations is connected, and the set of drone base stations is defined as follows: Distribution in the network model A set of terminal users, defined as: The terminal user purchases the first access link transmission service of the base station through an auction, and pays the fee to the purchasing base station during the first access link transmission service period, whereby the first access link transmission service period is defined as... .definition For drone base stations The price for the first access link transmission service is charged to each associated end user per unit time, defined as follows: The macro base station charges each associated terminal user a first access link transmission service price per unit time. The drone base station is defined as follows. The available bandwidth of the macro base station is respectively Hz and Hz. The base station adjusts the frequency based on the number of associated terminal users. It is necessary to lease the second backhaul link transmission bandwidth from the macro base station. Used to bear the transmission service of its first access link, Defined as the second backhaul link transmission bandwidth allocation ratio. The transmission capacity of the second backhaul link of a drone base station directly affects the number of its associated terminal users; that is, the more associated users, the more second backhaul link transmission capacity is required, thus affecting the allocated second backhaul link transmission bandwidth ratio. The higher the value, the lower the value. The price paid by a drone base station to lease the second backhaul link transmission bandwidth from a macro base station is defined as... The payment price is dynamically determined based on the load of the macro base station and is therefore defined as a time function. .exist At any given time, the second backhaul link transmission bandwidth allocation state of the network model is defined as follows: The remaining bandwidth of a macro base station is defined as 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, macro base stations are deployed in horizontal positions. end users Deployed in a horizontal position Drone base station Internally deployed in the same vertical position and different horizontal positions drone base station At a fixed altitude during each flight cycle Flying above the ground, the flight period can be divided into... An equal time slot. Taking into account safety factors such as terrain or obstacle avoidance, It should be set as small as possible. Then, the drone base station. In the The horizontal position of each time slot can be represented as... .

[0058] In the drone base station within each time slot With end users Channel gain between Defined as:

[0059] ;

[0060] in, It is the reference channel power per unit distance.

[0061] exist At that time, the drone base station The formula for the transmission rate of the first access link is defined as follows: ,in Defined as a drone base station Transmission power, Defined as a drone base station The set of channel gains associated with the terminal users, Defined as noise power spectral density. Further, the set of first access link transmission rates for UAV base stations in the network is defined as... The transmission rate of the first access link of a macro base station is defined as... Furthermore, in At that time, the drone base station The number of associated end users is defined as The number of associated terminal users of the macro base station is defined as follows: Furthermore, in At that time, the drone base station The percentage of associated terminal users is defined as , The percentage of associated terminal users of a macro base station is defined as follows: ;in, Meet the conditions .

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

[0063] ;

[0064] exist At any given time, 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, the UAV base station resource allocation problem is broken down into a user-level correlation optimization problem and a resource-level transmission resource optimization problem; the correlation optimization problem is an optimization problem of the network correlation 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 for terminal user association and bandwidth resource allocation are interactive and have time-varying characteristics, this application proposes a dynamic hierarchical game method consisting of user level and resource level in order to capture this characteristic. 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 operation.

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

[0069] In this embodiment, the step of 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 correlation 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 correlation optimization problem; and 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 step of modeling the receiving rate of the user terminal based on a preset evolutionary game model to obtain the first game model corresponding to the association optimization problem includes: identifying the user terminal performing wireless information backhaul operation as a game participant, and determining the current number of user terminals with network association with the target base station as the 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 the first game model corresponding to the association optimization problem based on the participant utility model and the replicator dynamic model. That is, evolutionary game theory is used at the user level to study the association strategy between terminal users 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, causing the terminal user to be unable to receive complete channel state information. Therefore, all terminal users in the network model adopt a stepwise learning and adjustment of their association strategies. The dynamic end-user behavior described herein is expressed as an evolutionary game model, in which all end-users will obtain the same personal utility in the game equilibrium state, and there is uniqueness.

[0071] The method of modeling the transmission resources of the UAV base station and the macro base station based on a preset Stackelberg game theory approach to obtain a second game model corresponding to the transmission resource optimization problem includes: determining the wireless information backhaul profit of the UAV base station during the wireless information backhaul operation based on the difference between the fees charged by the UAV base station to the user terminal and the fees 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 UAV 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 UAV 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 the second game model corresponding to the transmission resource optimization problem based on the first utility sub-model and the second utility sub-model. The user level exhibits limited transmission capacity of the second backhaul link and dynamic user-associated base station strategies. These characteristics necessitate that drone base stations require a transmission rate from the first access link. Second backhaul link transmission bandwidth allocation Pricing strategies between macro base stations The optimal decision is made based on three factors. For example, increasing the transmission rate of the first access link of the drone base station. This can improve the utility of associated end users, which will encourage more end users to connect to the drone base station. However, this result will cause the drone base station to lease more second backhaul link transmission bandwidth resources from the macro base station. This increases the transmission capacity of the second backhaul link of the drone base station, further influencing the pricing of wireless bandwidth resources by the macro base station. To accurately analyze the dynamic process, this invention employs a Stackelberg game model, treating macro base stations as leaders and drone base stations as followers. Users calculate and obtain an open-loop Stackelberg equilibrium as the optimal strategic solution.

[0072] Step S14: Determine the target individual utility and 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 UAV 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, determining the target individual utility and 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 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; determining the new current association quantity of user terminals with network association with the target base station based on the current individual utility; and determining the current association quantity corresponding to the second game model based on the current association quantity. The process involves: 1) determining the current resource backhaul utility and, based on this current resource backhaul utility, determining the new flight location information of the UAV base station and a new preset backhaul link transmission bandwidth allocation ratio; 2) transitioning to the step of adjusting the game strategy in the participant utility model based on the UAV base station's flight location information, 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 a preset game equilibrium state is satisfied, thus determining the current individual utility and the current resource backhaul utility as the target individual utility and target resource backhaul utility, respectively. In summary, the user level uses optimized resource variables obtained from the resource level to dynamically model the replicator to analyze user association states. The resource level uses user association states evolved from the user level to facilitate resource transactions between base stations to obtain optimal values ​​in terms of first access link transmission throughput, backhaul bandwidth pricing, and allocation.

[0074] The user-level evolutionary game model is defined as follows: Participants: Every end user in the network. Defined as participants in an evolutionary game. Strategy: Each participant's strategy is a base station association, and the strategy set is defined as follows: Association Quantity Status: Define the association quantity status as... The associated quantity status is represented as in Timing Selection Strategy The percentage of participants. Definition This is a set of associated quantity states. Utility: Given the set of associated quantity states... Second backhaul link transmission bandwidth allocation ratio and the transmission rate of the first access link Choose a strategy The participant utility model is defined as follows:

[0075] (1)

[0076] in, It is represented as a fixed mapping factor.

[0077] Replicator Dynamics: The replicator dynamics refer to the different strategies chosen by each participant. Utility information is defined as:

[0078] ;

[0079] in, This is represented as the learning rate, which controls the frequency with which participants adjust their own strategies. Defined as in The average utility of the entire network at any given time. Definition As the initial setup state, participants Adjusting the state of one's own association quantity according to the participant utility model (1) To find more efficient use For example, if the strategy It provides higher utility than average utility, that is More participants' selection quotas are linked to base stations. This process will produce a higher number of associated states. .

[0080] exist At time, for the strategy If the first access link transmission rate Second backhaul link transmission bandwidth allocation In time interval If it is internally measurable, then the present invention sets an initial value. The reproducer dynamics obtained under the condition of participant utility model It has a unique solution .

[0081] Here, regarding the aforementioned copier dynamics It has a unique solution To prove this claim. At time, for the strategy Set the formula For the associated quantity state set It is continuously differentiable. If the transmission rate of the first access link... Second backhaul link transmission bandwidth allocation In time interval Internal measurability, within a time interval Internal fixation strategy Then the formula In time interval The interior is also measurable. Furthermore, within any fixed region... and Closed Boundary Set There is always a positive number inside. Can an integrable function be constructed? ,in Obviously, for all All satisfy the inequality Sum of inequalities .definition It can be derived from the equation The inequalities are derived from this. This verifies the stated formula. A global Lipschitz condition exists. The above analysis proves the dynamics of the replicator. There exists a globally unique solution.

[0082] 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 number of associations 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 number of associations to obtain a first constraint 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 number of associations to obtain a second constraint control problem; determining the optimal solutions corresponding to the first constraint control problem and the second constraint control problem based on the Pontryagin maximum principle to obtain the current resource backhaul utility of the second game model under the open-loop Stackelberg equilibrium state.

[0083] Specifically, drone base stations The profits come from charging associated end users and leasing second backhaul link transmission capacity from macro base stations. The difference between the payment fees, the difference being defined as In addition, drone base stations The transmission rate of the first access link was also taken into consideration. Second backhaul link transmission bandwidth allocation rate The costs incurred. Thus, drone base stations... exist The utility model at time (first utility sub-model) is as follows:

[0084] (2)

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

[0086] (3)

[0087] in, The loss rate represents the loss value of future profits; the optimization objective C0 represents the profit loss over the time period. Internal drone base station The average profit; constraint C1 represents the average profit of the drone base station. The transmission capacity of the second backhaul link is greater than that of its first access link; constraint C2 represents the selection strategy at the user level. The aforementioned replicator dynamics Constraint C3 means setting the initial strategy value at initial time 0. .

[0088] Macro base stations The utility of a given moment is represented as the difference between the profit gained from the associated end-users and the costs incurred by the drone base station, expressed in the following form:

[0089] (4)

[0090] The utility of macro base stations Relying on the second backhaul link for transmission resource allocation The associated quantity status in the user hierarchy This can be transformed into the following optimization control problem:

[0091] (5)

[0092] Among them, optimization objectives This refers to the transmission service time period of the first access link. Average profit of macro base stations; constraints This indicates that a strategy is selected at the user level. The aforementioned replicator dynamics Constraints This means setting the initial policy value at an initial time of 0. .

[0093] When the resource level reaches an open-loop Stackelberg equilibrium, At that time, the problem (3) was obtained and and the results obtained from the aforementioned problem (4) As the optimal selection strategy at the resource level, the necessary condition for setting the open-loop Stackelberg equilibrium state is the Pontryagin maximum principle. (Unmanned aerial vehicle base station) The augmented Hamiltonian function is expressed as:

[0094] (6)

[0095] in, ; Defined as a drone base station Related quantity status The cost function; Let this be represented as a Lagrange multiplier for constraint C1. Furthermore, unmanned aerial vehicle (UAV) base stations... The maximized Hamiltonian function is expressed as: (7)

[0096] The drone base station Optimal first access link transmission capacity Represented as:

[0097] .

[0098] The drone base station Optimal second backhaul link transmission bandwidth allocation Represented as:

[0099] .

[0100] Here, we prove that maximizing the Hamiltonian equation (6) is a necessary condition for obtaining the optimal control strategy in the optimization problem (5) according to Pontryagin's maximization principle. Pontryagin's maximization principle states that maximizing the Hamiltonian equation (6) is a necessary condition for obtaining the optimal control strategy in the optimization problem (5), and simultaneously satisfies the following necessary optimality conditions:

[0101] (8)

[0102] (9)

[0103] .

[0104] Because the Hamiltonian equation (6) is relevant to the transmission rate of the first access link. and the second backhaul link transmission bandwidth allocation It is concave, so the first access link transmission rate obtained by formula (8) is... and the second backhaul link transmission bandwidth allocation obtained by formula (9) It is a drone base station The optimal value.

[0105] Similarly, for the open-loop Stackelberg equilibrium of a macro base station, the maximum Hamiltonian function of the macro base station can be expressed as:

[0106] (10)

[0107] in, ; ; Defined as cost function The cost variable, where .

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

[0109] (11)

[0110] ;

[0111] .

[0112] Similarly, according to Pontryagin's maximal principle, in At any time, the drone base station Optimal strategy obtained Substituting the Hamiltonian equation (10) of the macro base station into the pricing... It is a convex function, which ensures that macro base stations are... The pricing strategy obtained through equation (11) at any time It is the optimal value.

[0113] The optimal control strategy obtained using the Pontryagin maximum principle The necessary conditions for satisfying the optimal value. Furthermore, the drone base station... The Hamiltonian formula (6) and the Hamiltonian formula (10) of the macro base station are related to the set of associated quantity states. All are concave, continuously differentiable. These verification conditions all satisfy the sufficient conditions for Stackelberg equilibrium, and can be used in conjunction with the proven optimal control strategy. It reaches an open-loop Stackelberg equilibrium.

[0114] As can be seen, in this embodiment, as Figure 2 As shown, the system acquires access link transmission information and backhaul link transmission information of the macro base station, UAV base station, and user terminal during wireless information backhaul operations, 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 decomposed into a user-level correlation optimization problem and a resource-level transmission resource optimization problem. The correlation optimization problem is an optimization problem of the network correlation 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. The system models 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 correlation optimization problem and a second game model corresponding to the transmission resource optimization problem. The system determines the target personal utility and target resource backhaul utility corresponding to the first game model and the second game model in the game equilibrium state, respectively, and determines the target resource allocation strategy for the UAV base station to perform the wireless information backhaul operation based on the target personal utility and the target resource backhaul utility. That is, a user tier based on evolutionary game theory and a resource tier based on Stackelberg game theory. The user tier uses optimized resource variables obtained from the resource tier to dynamically model the replicator in order to analyze user association states. The resource tier uses user association states evolved from the user tier to facilitate resource transactions between base stations to obtain optimal values ​​in terms of first access link transmission throughput, backhaul bandwidth pricing, and allocation.

[0115] refer to Figure 3 The present application also discloses a drone base station resource allocation device based on hierarchical game theory, comprising:

[0116] The information acquisition module 11 is used to acquire access link transmission information and backhaul link transmission information when the macro base station, the UAV base station and the user terminal are performing wireless information backhaul operations, as well as the flight position information of the UAV base station.

[0117] The problem decomposition module 12 is used to decompose the UAV base station resource allocation problem into a user-level correlation optimization problem and a resource-level transmission resource optimization problem based on the access link transmission information, the backhaul link transmission information, and the flight position information. The correlation optimization problem is an optimization problem of the network correlation 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.

[0118] The game model creation module 13 is 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 correlation 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;

[0119] The allocation strategy determination module 14 is used to determine the target individual utility and target resource backhaul utility corresponding to the first game model and the second game model respectively in the game equilibrium state, and to 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.

[0120] As can be seen, in this embodiment, the UAV base station resource allocation problem is decomposed into an association optimization problem corresponding to the user level and a transmission resource optimization problem corresponding to the resource level, based on predetermined game rules. A game model corresponding to the optimization problem is generated to analyze the user association state. The resource level uses the user association states evolved from the user level to promote resource transactions between base stations to obtain the optimal allocation of UAV base station resources.

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

[0122] The connection relationship establishment unit is used to establish a network association relationship between the user terminal and the macro base station and the drone base station based on the user bidding method, and to establish a network connection between the macro base station, the drone base station and the user terminal based on the network association relationship, so as to perform wireless information backhaul operation.

[0123] The location information acquisition unit is used to determine the flight location information of the UAV base station based on the horizontal deployment location, vertical deployment location and corresponding flight cycle of the UAV base station;

[0124] The first transmission rate determination unit is used to determine the first access link transmission rate corresponding to the UAV base station based on the first transmission power of the UAV base station, the noise power spectral density, and the first channel gain set of user terminals that have a network association with the UAV base station.

[0125] The second transmission rate determination unit is used to determine the second access link transmission rate corresponding to the macro base station based on the second transmission power of the macro base station, the noise power spectral density, and the second channel gain set of user terminals that have a network association with the macro base station.

[0126] The backhaul information determination unit is used to determine the 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] The first game model determination submodule is used to model the receiving rate of the user terminal based on a preset evolutionary game model in order to obtain the first game model corresponding to the correlation optimization problem.

[0129] The second game model determination submodule is used to model the transmission resources of the UAV base station and the macro base station based on the preset Stackelberg game comprehensive research method, so as to obtain the second game model corresponding to the transmission resource optimization problem.

[0130] In some specific embodiments, the first game model determining submodule may specifically include:

[0131] The game information determination unit is used to determine the user terminal when performing wireless information backhaul operation as a game participant, and to determine the current number of user terminals that have network association with the target base station as the game strategy.

[0132] The utility model determination unit is used 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] The first game model determination unit is used to construct the replicator dynamic model corresponding to the game participants, and determine the first game model corresponding to the correlation optimization problem based on the participant utility model and the replicator dynamic model.

[0134] In some specific embodiments, the second game model determining submodule may specifically include:

[0135] The profit determination unit is used to 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 bidding method.

[0136] The utility sub-model determination unit is used 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 to model the utility of the UAV base station based on the wireless information backhaul profit and the wireless information backhaul cost to obtain the first utility sub-model.

[0137] The second game model determination unit is used to model the utility of the macro base station based on the difference between the profit and cost obtained by the macro base station from the UAV base station and the user terminal during the wireless information backhaul operation to obtain a second utility sub-model, and to determine the 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] The individual utility determination unit is used to adjust the game strategy in the participant utility model based on the flight location information of the UAV base station, the preset backhaul link transmission bandwidth allocation ratio, and the replicator dynamic model, so as to obtain the current individual utility of the participant utility model.

[0140] The current association quantity determination unit is used to determine a new current association quantity of user terminals that have a network association relationship with the target base station based on the current personal utility;

[0141] The resource backhaul utility determination submodule is used to determine the current resource backhaul utility corresponding to the second game model based on the current association quantity, and to determine the new flight position information of the UAV base station and the new preset backhaul link transmission bandwidth allocation ratio based on the current resource backhaul utility.

[0142] The step jump unit is used to 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 backhaul 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 backhaul utility as the target personal utility and the target resource backhaul utility, respectively.

[0143] In some specific embodiments, the resource return utility determination submodule may specifically include:

[0144] The first constraint control problem determination unit is used 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 the first constraint control problem;

[0145] The second constraint control problem determination unit is used to 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 backhaul link transmission bandwidth allocation ratio and the current number of associations to obtain the second constraint control problem;

[0146] The current resource backhaul utility determination unit is used to determine the optimal solutions corresponding to the first and second constrained control problems 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, embodiments of this application also disclose an electronic device, Figure 4 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0148] Figure 4 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this 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. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the hierarchical game-based UAV base station resource allocation method disclosed in any of the foregoing embodiments. Alternatively, 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 can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

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

[0151] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the hierarchical game-based UAV base station resource allocation method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0152] Furthermore, this 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 aforementioned disclosed method for allocating UAV base station resources based on hierarchical game theory. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0153] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0154] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software 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 beyond the scope of this application.

[0155] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

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

[0157] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for allocating unmanned aerial vehicle (UAV) base station resources based on hierarchical game theory, characterized in that, include: The system acquires access link transmission information and backhaul link transmission information when macro base stations, UAV base stations, and user terminals perform wireless information backhaul operations, 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 location information, the UAV base station resource allocation problem is broken down into a user-level correlation optimization problem and a resource-level transmission resource optimization problem. The aforementioned relationship optimization problem is an optimization problem of the network 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; The receiving rate of the user terminal and the transmission resources of the target base station are modeled respectively to obtain the first game model corresponding to the correlation optimization problem and the second game model corresponding to the transmission resource optimization problem; The target individual utility and target resource backhaul utility corresponding to the first game model and the second game model in the game equilibrium state are determined respectively, and the target resource allocation strategy for the UAV base station to perform the wireless information backhaul operation is determined based on the target individual utility and the target resource backhaul utility.

2. The method for allocating UAV base station resources based on hierarchical game theory according to claim 1, characterized in that, The acquisition of access link transmission information and backhaul link transmission information between the macro base station, the UAV base station, and the user terminal during wireless information backhaul operations, as well as the flight position information of the UAV base station, includes: A network association relationship is established between the user terminal and the macro base station and the drone base station based on the user bidding method, and a network connection is established between the macro base station, the drone base station and the user terminal based on the network association relationship to perform wireless information backhaul operation. The flight position information of the drone base station is determined based on its horizontal and vertical deployment positions and the corresponding flight cycle. The first access link transmission rate corresponding to the drone base station is determined based on the first transmission power of the drone base station, the noise power spectral density, and the first channel gain set of the user terminals that have a network association with the drone base station. The transmission rate of the second access link corresponding to the macro base station is determined based on the second transmission power of the macro base station, the noise power spectral density, and the second channel gain set of the user terminals that have a network association with the macro base station. The backhaul link transmission information of the UAV base station is determined based on the preset backhaul link transmission bandwidth allocation ratio.

3. The method for allocating UAV base station resources based on hierarchical game theory according to claim 2, characterized in that, The step of 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 correlation optimization problem and a second game model corresponding to the transmission resource optimization problem includes: The receiving rate of the user terminal is modeled based on a preset evolutionary game model to obtain the first game model corresponding to the correlation optimization problem. Based on the pre-defined Stackelberg game comprehensive research method, the transmission resources of the UAV base station and the macro base station are modeled to obtain the second game model corresponding to the transmission resource optimization problem.

4. The method for allocating UAV base station resources based on hierarchical game theory according to claim 3, characterized in that, The step of modeling the receiving rate of the user terminal based on a preset evolutionary game model to obtain the first game model corresponding to the correlation optimization problem includes: The user terminal performing the wireless information backhaul operation is identified as a game participant, and the current number of user terminals that have a network association relationship with the target base station is identified as the game strategy. Construct a participant utility model for the game participants based on the first access link transmission rate, the second access link transmission rate, and the game strategy. Construct a replicator dynamic model corresponding to the game participants, and determine the first game model corresponding to the correlation optimization problem based on the participant utility model and the replicator dynamic model.

5. The method for allocating UAV base station resources based on hierarchical game theory according to claim 4, characterized in that, The method based on a pre-defined Stackelberg game theory approach models the transmission resources of the UAV base station and the macro base station to obtain a second game model corresponding to the transmission resource optimization problem, including: 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, the wireless information backhaul profit of the drone base station in the wireless information backhaul operation is determined. 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 is determined, and the utility of the UAV base station is modeled 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 cost obtained by the macro base station from the UAV base station and the user terminal during the wireless information backhaul operation to obtain a second utility sub-model. Based on the first utility sub-model and the second utility sub-model, a second game model corresponding to the transmission resource optimization problem is determined.

6. The method for allocating UAV base station resources based on hierarchical game theory according to claim 5, characterized in that, Determining the target individual utility and target resource return utility corresponding to the first game model and the second game model in the game equilibrium state, respectively, includes: Based on the flight location information of the UAV base station, the preset backhaul link transmission bandwidth allocation ratio, and the replicator dynamic model, the game strategy in the participant utility model is adjusted to obtain the current personal utility of the participant utility model. Based on the current personal utility, determine the new current number of user terminals that have a network association with the target base station; Based on the current number of associations, the current resource backhaul utility corresponding to the second game model is determined, and based on the current resource backhaul utility, the new flight position information of the UAV base station and the new preset backhaul link transmission bandwidth allocation ratio are determined. The process jumps to the step of adjusting the game strategy in the participant utility model based on the flight location information of the UAV 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, until a preset game equilibrium state is satisfied, so as to determine the current personal utility and the current resource backhaul utility as the target personal utility and the target resource backhaul utility, respectively.

7. The method for allocating UAV base station resources based on hierarchical game theory according to claim 6, characterized in that, The step of determining the current resource feedback utility corresponding to the second game model based on the current number of associations includes: The utility of the macro base station in the second utility sub-model is fixed, and the first utility sub-model is transformed based on the backhaul link transmission information and the current number of associations to obtain the first constraint control problem; The utility of the UAV base station in the first utility sub-model is fixed, and the second utility sub-model is transformed based on the preset backhaul link transmission bandwidth allocation ratio and the current number of associations to obtain the second constraint control problem; Based on the Pontryagin maximum principle, the optimal solutions corresponding to the first and second constrained control problems are determined to obtain the current resource backhaul utility of the second game model under the open-loop Stackelberg equilibrium state.

8. A drone base station resource allocation device based on hierarchical game theory, characterized in that, include: The information acquisition module is used to acquire access link transmission information and backhaul link transmission information when the macro base station, the UAV base station and the user terminal are performing wireless information backhaul operations, as well as the flight position information of the UAV base station. The problem decomposition module is used to decompose the UAV base station resource allocation problem into a user-level correlation optimization problem and a resource-level transmission resource optimization problem based on the access link transmission information, the backhaul link transmission information, and the flight position information. The aforementioned correlation optimization problem is an optimization problem of the network correlation 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; The game model creation module is 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 correlation 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; The allocation strategy determination module is used to determine the target individual utility and target resource backhaul utility corresponding to the first game model and the second game model in the game equilibrium state, respectively, and to 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.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the unmanned aerial vehicle (UAV) base station resource allocation method based on hierarchical game theory as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer programs, which, when executed by a processor, implement the unmanned aerial vehicle (UAV) base station resource allocation method based on hierarchical game theory as described in any one of claims 1 to 7.

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