Unmanned aerial vehicle communication resource allocation network acquisition method and device, medium and equipment

Abstract

The invention provides an unmanned aerial vehicle communication resource allocation network acquisition method and device, a medium and equipment. The method comprises the following steps: acquiring current network communication states and motion actions of a plurality of unmanned aerial vehicles; acquiring the next network communication state of the unmanned aerial vehicle after each unmanned aerial vehicle executes the motion action; according to the equipment coverage rate, the communication resource throughput and the distance penalty coefficient with other unmanned aerial vehicles after each unmanned aerial vehicle executes the motion action, obtaining an action reward of each unmanned aerial vehicle; training an unmanned aerial vehicle communication resource allocation network according to the current network communication states of the multiple groups of unmanned aerial vehicles, the unmanned aerial vehicle motion actions, the action rewards and the next network communication states of the unmanned aerial vehicles to obtain a trained unmanned aerial vehicle communication resource allocation network; and inputting the current network communication state of the to-be-allocated unmanned aerial vehicle into the trained unmanned aerial vehicle communication resource allocation network to obtain an unmanned aerial vehicle communication resource allocation result. According to the invention, the utilization efficiency of unmanned aerial vehicle communication resources can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of unmanned aerial vehicle communication resource allocation, and specifically to a method, device, medium and equipment for obtaining a network for unmanned aerial vehicle communication resource allocation. Background Art

[0002] The integrated air-ground-space network is a network architecture collaboratively constructed by satellites, ground base stations and drones. Among them, the allocation of communication resources for drones will also seriously affect the communication efficiency of the integrated air-ground-space network. The existing methods for allocating communication resources for drones all manually control the movement of drones to change the coverage of drones. However, manual control will make the coverage of drones unstable, and manual control will not combine the throughput of human-machine communication resources, resulting in limited allocation of communication resources for drones and low utilization efficiency of drone communication resources. Summary of the invention

[0003] The purpose of this application is to overcome the shortcomings and deficiencies in the prior art and to provide a method, device, medium and equipment for obtaining a drone communication resource allocation network, which are used to control the communication resource allocation of the drone and improve the utilization efficiency of the drone communication resources.

[0004] A first aspect of an embodiment of the present application provides a method for acquiring a UAV communication resource allocation network, comprising:

[0005] Get the current network communication status and motion actions of several drones;

[0006] Obtaining the next network communication state of each drone after the drone performs the motion action;

[0007] Obtaining an action reward for each of the drones according to the device coverage rate, communication resource throughput, and distance penalty coefficient with other drones after each of the drones performs the motion action;

[0008] Training a drone communication resource allocation network according to the current network communication status of multiple groups of drones, the drone motion actions, the action rewards, and the next network communication status of the drones to obtain a trained drone communication resource allocation network;

[0009] The current network communication status of the UAV to be allocated is input into the trained UAV communication resource allocation network to obtain the UAV communication resource allocation result.

[0010] A second aspect of an embodiment of the present application provides a device for acquiring a drone communication resource allocation network, including:

[0011] The state action acquisition module is used to obtain the current network communication status and motion actions of several drones;

[0012] The state action execution module is used to obtain the next network communication state of each drone after the drone performs the motion action; according to the device coverage rate, communication resource throughput, and distance penalty coefficient of each drone after the drone performs the motion action, obtain the action reward of each drone;

[0013] A training module, used for training a UAV communication resource allocation network according to the current network communication status of multiple groups of UAVs, the UAV motion actions, the action rewards and the next network communication status of the UAVs, to obtain a trained UAV communication resource allocation network;

[0014] The resource allocation module is used to input the current network communication status of the drone to be allocated into the trained drone communication resource allocation network to obtain the drone communication resource allocation result.

[0015] A third aspect of an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for acquiring a drone communication resource allocation network are implemented as described above.

[0016] The fourth aspect of an embodiment of the present application provides a computer device, including a storage device, a processor, and a computer program stored in the storage device and executable by the processor. When the processor executes the computer program, the steps of the drone communication resource allocation network acquisition method as described above are implemented.

[0017] Compared with the related art, the present application trains a drone communication resource allocation network according to the current network communication status of multiple groups of drones, drone motion actions, action rewards after executing motion actions, and the next network communication status of the drones, and obtains the trained drone communication resource allocation network for communication resource allocation for the drones to be allocated. Among them, since the action reward is obtained based on the device coverage rate, communication resource throughput, and distance penalty coefficient with other drones after executing the motion action, the action reward is a comprehensive reflection of the device coverage rate, communication resource throughput, and distance penalty coefficient. The drone communication resource allocation network obtained by combining the current network communication status, drone motion actions, action rewards, and the next network communication status of the drone also predicts resource allocation by combining the device coverage rate, communication resource throughput, and distance penalty coefficient. Therefore, the drone communication resource allocation result outputted by it can achieve the effect of improving the utilization efficiency of drone communication resources.

[0018] In order to provide a clearer understanding of the present application, the specific implementation of the present application will be described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of an integrated air-ground-space network according to an embodiment of the present application.

[0020] Figure 2 This is a flowchart of a method for allocating UAV communication resources according to an embodiment of the present application.

[0021] Figure 3 This is a schematic diagram of the module connections of a drone communication resource allocation network acquisition device according to one embodiment of the present application.

[0022] 100. UAV communication resource allocation network acquisition device; 101. State action acquisition module; 102. State action execution module; 103. Training module; 104. Resource allocation module. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0024] It should be clear that the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the embodiments of the present application.

[0025] When the following description relates to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. The singular forms of "a", "said" and "the" used in the present application and the appended claims are also intended to include the majority form, unless the context clearly indicates other meanings. The words "if" / "if" used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0026] In addition, in the description of this application, unless otherwise specified, "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0027] See also Figure 1, which is a schematic diagram of an air-ground integrated network of an embodiment of the present application, the air-ground integrated network is collaboratively constructed by satellites, ground base stations and drones, Figure 1 SAT is a satellite, UAV1, UAV2 and UAV3 are drones, and GBS is a base station. The drone communication resource allocation is mostly achieved by controlling the movement of the drone, because the movement of the drone will affect the coverage of the drone, the throughput of the drone communication resources, and the distance between the current drone and other drones. Therefore, this application will train the drone communication resource allocation network in combination with the movement of the drone, so that the drone communication resource allocation result output by the trained drone communication resource allocation network can achieve the effect of improving the utilization efficiency of drone communication resources.

[0028] See also Figure 2 , which is a flow chart of a method for allocating communication resources of a drone according to an embodiment of the present application, comprising:

[0029] S1: Get the current network communication status and motion actions of several drones.

[0030] Among them, several drones can be all drones in the same network communication area. The current network communication status includes the current location of the drone, the current coverage of the drone, whether the user equipment in the current coverage can communicate smoothly through the drone, the communication resource throughput in the current coverage, etc.

[0031] S2: Obtain the next network communication state of each drone after the drone performs the motion action, and obtain the action reward of each drone according to the device coverage rate, communication resource throughput, and distance penalty coefficient with other drones after the drone performs the motion action.

[0032] The next network communication state of the drone refers to the network communication state of the drone after it performs the corresponding motion action. The device coverage rate and communication resource throughput of the next network communication state of the drone may change relative to the current network communication state of the drone before the motion action is performed. In addition, the distance between the location of the drone after the motion action and other drones will also affect the score of the motion action. Therefore, the action reward of the drone for performing the motion action can be accurately judged based on the device coverage rate, communication resource throughput, and distance penalty coefficient with other drones after the motion action.

[0033] S3: Training a drone communication resource allocation network according to the current network communication status of multiple groups of drones, the drone motion actions, the action rewards and the next network communication status of the drones to obtain a trained drone communication resource allocation network.

[0034] Wherein, step S3 comprises:

[0035] S31: Input the drone motion action, the action reward and the next network communication state of the drone into the drone communication resource allocation network to obtain an action estimation score.

[0036] The action prediction score refers to the score given by the UAV communication resource allocation network to the UAV action based on its network parameters, combined with the action reward and the next network communication state of the UAV.

[0037] Specifically, the action estimation score can be obtained by the following formula:

[0038] y i =r i +γmax a Q ω (s i+1 ,a), i=1,2,...N

[0039] Among them, y i Score the motion estimation of the UAV motion action, r i is the action reward, γ is the discount factor, max a Q ω (s i+1 ,a) is the action value function of the next network communication state of the drone and the drone motion action, N is the number of sampling training times. Each action estimation score corresponds to a drone motion action.

[0040] S32: Obtaining the action target score of the current network communication status.

[0041] The action target score of the current network communication state may be a target score input by a user, indicating a target score corresponding to executing the motion action in the current network communication state.

[0042] S33: Adjusting the network parameters of the UAV communication resource allocation network according to the action prediction score and the action target score to obtain a trained UAV communication resource allocation network.

[0043] Among them, step S33 can also be understood as training the network parameters of the drone communication resource allocation network according to the action prediction score and the action target score, so that after adjustment or training, the drone communication resource allocation network outputs an action prediction score close to or equal to the action target score, thereby obtaining a drone communication resource allocation network that can output an action prediction score that meets user requirements.

[0044] S4: Input the current network communication status of the drone to be allocated into the trained drone communication resource allocation network to obtain the drone communication resource allocation result.

[0045] Among them, the trained UAV communication resource allocation network will combine the current network communication status of the UAV to predict the action estimation score after the UAV performs various different motion actions, and select the action estimation score with the highest action estimation score as the motion action to be driven by the UAV. The motion action and the corresponding action estimation score are the UAV communication resource allocation result output by the UAV communication resource allocation network. After the UAV performs the motion action, it can take into account multiple factors such as device coverage, communication resource throughput, and distance from other UAVs to play a communication role, so as to improve the utilization efficiency of UAV communication resources.

[0046] Compared with the related art, the present application trains a drone communication resource allocation network according to the current network communication status of multiple groups of drones, drone motion actions, action rewards after executing motion actions, and the next network communication status of the drones, and obtains the trained drone communication resource allocation network for communication resource allocation for the drones to be allocated. Among them, since the action reward is obtained based on the device coverage rate, communication resource throughput, and distance penalty coefficient with other drones after executing the motion action, the action reward is a comprehensive reflection of the device coverage rate, communication resource throughput, and distance penalty coefficient. The drone communication resource allocation network obtained by combining the current network communication status, drone motion actions, action rewards, and the next network communication status of the drone also predicts resource allocation by combining the device coverage rate, communication resource throughput, and distance penalty coefficient. Therefore, the drone communication resource allocation result outputted by it can achieve the effect of improving the utilization efficiency of drone communication resources.

[0047] In a feasible embodiment, the step of S33: adjusting the network parameters of the UAV communication resource allocation network according to the action estimation score and the action target score to obtain a trained UAV communication resource allocation network includes:

[0048] S331: Obtain an error value between the action estimation score and the action target score.

[0049] The error value can be obtained by the following formula:

[0050]

[0051] Where L is the error value, N is the number of sampling training times, and y i Scoring the motion estimation of drone motion, Q ω (s i ,a i ) is used to score the action target.

[0052] S332: Adjust the network parameters of the UAV communication resource allocation network according to the error value to obtain a trained UAV communication resource allocation network.

[0053] Among them, the gradient descent method can be used to adjust the network parameters of the UAV communication resource allocation network. Its goal is to reduce the error value so that the action estimation score predicted by the UAV communication resource allocation network after adjusting the network parameters is close to or equal to the action target score.

[0054] In this embodiment, an error value is obtained based on the action prediction score and the action target score, and the network parameters of the drone communication resource allocation network are adjusted based on the error value, which is conducive to obtaining a trained drone communication resource allocation network that can accurately output an action prediction score that meets user requirements.

[0055] In a feasible embodiment, the device coverage rate after each of the drones performs the motion action is obtained by the following steps, including:

[0056] S21: Determine the terminal devices covered by the communication range of the drones according to the distance between each drone and the terminal device after each drone performs the motion action.

[0057] Among them, the terminal devices covered by the drone’s communication range can be determined by the following formula:

[0058]

[0059] Among them, C s,u (t) = 0 means the terminal device is not within the communication range of the drone, C s,u (t) = 1 means the terminal device is within the communication range of the drone; d s,u (t) is the distance between the terminal device and the ground center point of the drone, d s,m (t) is the distance between the terminal device and the base station or server, r u is the radius of the drone’s communication range. s,u The number of (t)=1 is the number of terminal devices covered by the communication range of the drone.

[0060] S22: Obtain the device coverage rate according to the total number of terminal devices and the number of terminal devices covered by the communication range of the drone.

[0061] The device coverage rate can be calculated by the following formula:

[0062]

[0063] Where Cov(t) is the equipment coverage rate.

[0064] Optionally, the device coverage rate can also be obtained according to the number of terminal devices covered by the base station, the number of terminal devices covered by the communication range of the drone, and the total number of terminal devices. In this case, the device coverage rate can be calculated by the following formula:

[0065]

[0066] The terminal devices covered by the base station can be obtained by the following formula:

[0067]

[0068] Among them, C s,m (t) = 0 means the terminal device is not within the communication range of the base station. s,m (t) = 1 means the terminal device is within the communication range of the base station; d s,m (t) is the distance between the terminal device and the base station or server. s,m The number of (t)=1 is the number of terminal devices covered by the base station.

[0069] In this embodiment, the device coverage rate for influencing the action reward can be accurately obtained based on the number of terminal devices located within the communication range and the total number of terminal devices, so that the drone communication resource allocation result output by the trained drone communication resource allocation network can take into account the factor of device coverage rate to improve the utilization efficiency of drone communication resources.

[0070] See also Figure 3 The second embodiment of the present application provides a drone communication resource allocation network acquisition device 100, comprising:

[0071] The state action acquisition module 101 is used to acquire the current network communication state and motion action of several drones;

[0072] The state action execution module 102 is used to obtain the next network communication state of each drone after the drone performs the motion action; obtain the action reward of each drone according to the device coverage rate, communication resource throughput, and distance penalty coefficient of each drone after the drone performs the motion action;

[0073] The training module 103 is used to train the drone communication resource allocation network according to the current network communication status of multiple groups of drones, the drone motion actions, the action rewards and the next network communication status of the drones to obtain a trained drone communication resource allocation network;

[0074] The resource allocation module 104 is used to input the current network communication status of the drone to be allocated into the trained drone communication resource allocation network to obtain the drone communication resource allocation result.

[0075] It should be noted that the drone communication resource allocation network acquisition device 100 provided in the second embodiment of the present application only uses the division of the above-mentioned functional modules as an example when executing the drone communication resource allocation network acquisition method. In actual applications, the above-mentioned functional allocation can be completed by different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the drone communication resource allocation network acquisition device 100 provided in the second embodiment of the present application and the drone communication resource allocation network acquisition method of the first embodiment of the present application belong to the same concept. The implementation process thereof is detailed in the method embodiment and will not be repeated here.

[0076] A third aspect of an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for acquiring a drone communication resource allocation network are implemented as described above.

[0077] The fourth aspect of an embodiment of the present application provides a computer device, including a storage device, a processor, and a computer program stored in the storage device and executable by the processor. When the processor executes the computer program, the steps of the drone communication resource allocation network acquisition method as described above are implemented.

[0078] The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application. Ordinary technicians in this field can understand and implement it without creative work.

[0079] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0080] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the function selected in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 function selected in a box or multiple boxes.

[0081] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 steps for the function selected in a box or multiple boxes.

[0082] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0083] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0084] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0085] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0086] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for allocating communication resources of unmanned aerial vehicles, characterized in that: include: Get the current network communication status and motion actions of several drones; Obtaining the next network communication state of each drone after the drone performs the motion action; Obtaining an action reward for each of the drones according to the device coverage rate, communication resource throughput, and distance penalty coefficient with other drones after each of the drones performs the motion action; Training a drone communication resource allocation network according to the current network communication status of multiple groups of drones, the drone motion actions, the action rewards, and the next network communication status of the drones to obtain a trained drone communication resource allocation network; The current network communication status of the UAV to be allocated is input into the trained UAV communication resource allocation network to obtain the UAV communication resource allocation result.

2. The method for acquiring a UAV communication resource allocation network according to claim 1, characterized in that: The step of training the drone communication resource allocation network according to the current network communication status of the multiple groups of drones, the drone motion actions, the action rewards and the next network communication status of the drones to obtain the trained drone communication resource allocation network includes: Inputting the drone motion action, the action reward and the next network communication state of the drone into the drone communication resource allocation network to obtain an action estimation score; Obtaining an action target score of the current network communication state; The network parameters of the UAV communication resource allocation network are adjusted according to the action estimation score and the action target score to obtain a trained UAV communication resource allocation network.

3. The method for acquiring a UAV communication resource allocation network according to claim 2, characterized in that: The step of inputting the drone motion action, the action reward and the next network communication state of the drone into the drone communication resource allocation network to obtain the action estimation score includes: The action estimation score is obtained by the following formula: y i =r i +γmax a Q ω (s i+1 ,a),i=1,2,...N Among them, y i Score the motion estimation of the UAV motion action, r i is the action reward, γ is the discount factor, max a Q ω (s i+1 ,a) is the action value function of the next network communication state of the UAV and the UAV motion action, and N is the number of sampling training times.

4. The method for acquiring a UAV communication resource allocation network according to claim 2, characterized in that: The step of adjusting the network parameters of the UAV communication resource allocation network according to the action estimation score and the action target score to obtain a trained UAV communication resource allocation network includes: Obtaining an error value between the action estimation score and the action target score; The network parameters of the UAV communication resource allocation network are adjusted according to the error value to obtain a trained UAV communication resource allocation network.

5. The method for acquiring a UAV communication resource allocation network according to claim 4, characterized in that: The step of obtaining the error value between the action estimation score and the action target score comprises: The error value is obtained by the following formula: Where L is the error value, N is the number of sampling training times, and y i Scoring the motion estimation of drone motion, Q ω (s i ,a i ) is used to score the action target.

6. The method for acquiring a UAV communication resource allocation network according to claim 1, characterized in that: The device coverage rate after each of the drones performs the motion action is obtained by the following steps, including: Determine the terminal devices covered by the communication range of the drones according to the distance between each drone and the terminal device after each drone performs the motion action; The device coverage rate is obtained according to the total number of terminal devices and the number of terminal devices covered by the communication range of the drone.

7. The method for acquiring a UAV communication resource allocation network according to claim 6, characterized in that: The step of determining the terminal devices covered by the communication range of the drones according to the distance between each drone and the terminal device after each drone performs the motion action comprises: The terminal devices covered by the drone’s communication range are determined by the following formula: Among them, C s,u (t) = 0 means the terminal device is not within the communication range of the drone, C s,u (t) = 1 means the terminal device is within the communication range of the drone; d s,u (t) is the distance between the terminal device and the ground center point of the drone, d s,m (t) is the distance between the terminal device and the base station or server, r u is the radius of the drone’s communication range.

8. A device for acquiring a drone communication resource allocation network, characterized in that: include: The state action acquisition module is used to obtain the current network communication status and motion actions of several drones; The state action execution module is used to obtain the next network communication state of each drone after the drone performs the motion action; according to the device coverage rate, communication resource throughput, and distance penalty coefficient of each drone after the drone performs the motion action, obtain the action reward of each drone; A training module, used for training a UAV communication resource allocation network according to the current network communication status of multiple groups of UAVs, the UAV motion actions, the action rewards and the next network communication status of the UAVs, to obtain a trained UAV communication resource allocation network; The resource allocation module is used to input the current network communication status of the drone to be allocated into the trained drone communication resource allocation network to obtain the drone communication resource allocation result.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for acquiring a drone communication resource allocation network are implemented as described in any one of claims 1 to 7.

10. A computer device, characterized in that: It includes a storage device, a processor, and a computer program stored in the storage device and executable by the processor. When the processor executes the computer program, the steps of the method for acquiring a drone communication resource allocation network as described in any one of claims 1 to 7 are implemented.

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