Method and system for network service optimization considering energy of UAV

Abstract

The present invention relates to a method and system for network service optimization considering the energy of an unmanned aerial vehicle (UAV). A device for optimizing the operation of a UAV may: receive UAV energy information from each of a plurality of UAVs, each of which having a wireless access point module; receive user terminal location information from each of a plurality of user terminals; calculate a data rate for a specific UAV among the plurality of UAVs on the basis of the one or more pieces of user terminal location information; calculate a data service remaining time of the specific UAV on the basis of the UAV energy information about the specific UAV; and select, on the basis of the data rate for the specific UAV and the data service remaining time of the specific UAV, partitioning for adjusting the interval of valid cells while at least one UAV does not move, or positioning for adjusting the interval of the valid cells while the at least one UAV moves, and cause at least one UAV among the plurality of UAVs to perform the selection.

Description

Method and system for optimizing network services considering UAV energy

[0001] The present specification relates to a method and system for optimizing network services considering the energy of a UAV, and more specifically, to a method and system for adjusting the position of a UAV considering the energy usage of the UAV acting as an access point (AP) of a user terminal.

[0002]

[0003] Advances in unmanned aerial vehicle (UAV) manufacturing and flight control technology have made it possible to equip UAVs with high-performance communication devices. Operating multiple UAVs equipped with such specialized equipment can maximize the operational range of a multi-UAV system by utilizing the low-cost, high-efficiency UAVs themselves as mobile communication relays at specific locations. However, developing an effective multi-UAV operation plan that maximizes the collaborative synergy among multiple UAVs equipped with specific equipment presents challenges. In particular, UAV communications are limited by the distance between base stations and UAVs, as well as between UAVs. Therefore, operational planning must strictly consider these limitations to ensure a smooth communication link. Furthermore, multi-UAV operation plans typically require the time and expense of skilled UAV control personnel, a significant challenge.

[0004] In particular, when using a drone as an Access Point (AP), communication service planning must be based on limited resources. In such cases, the issue of drone battery life is an unavoidable factor for drone utilization. As the drone searches for and moves to the optimal location to provide seamless service to the user terminal, battery consumption inevitably occurs. If the drone were to search for and change its hovering position in real time to account for changes in the user's location, efficient use of the drone's limited resources would be impossible, and technical solutions are urgently needed to address this issue.

[0005]

[0006] The technical problem that the embodiments of the present specification seek to solve is to propose an algorithm for positioning of an unmanned aerial vehicle (UAV) and cell partitioning of an access point (AP) system based on an unmanned aerial vehicle (UAV), thereby selecting an appropriate location for the UAV and determining network coverage, thereby providing high-quality data services to a large number of user terminals. Furthermore, the objective is to enable the UAV-based access point system to provide maximum throughput in response to changes in the location of user terminals and changes in required data.

[0007]

[0008] In order to solve the above technical problem, in a method for streaming G-PCC-based content, a media pre-distribution (MPD) describing spatial information necessary for the user based on the user's location is provided. In one embodiment of the present invention, a method for optimizing a network service considering the energy of a UAV comprises: a step in which a UAV operation optimization device receives UAV energy information from each of a plurality of UAVs (Unmanned Aerial Vehicles) equipped with a wireless access point module; a step in which the UAV operation optimization device receives user terminal location information from each of a plurality of user terminals and calculates a data rate for a specific UAV among the plurality of UAVs based on at least one of the user terminal location information; a step in which the UAV operation optimization device calculates a data service remaining time of the specific UAV based on the UAV energy information of the specific UAV; and a step in which the UAV operation optimization device adjusts the spacing of effective cells based on the data rate for the specific UAV and the data service remaining time of the specific UAV while at least one UAV does not move its location, or adjusts the spacing of effective cells while at least one UAV moves its location. A step of selecting one of the positioning operations and performing the operation on at least one UAV among the plurality of UAVs; wherein the UAV energy information may include total energy information of the UAV for the total amount of energy when the UAV starts operating, hovering energy information of the UAV for the amount of energy used for hovering of the UAV, and movement energy information of the UAV for the amount of energy used for moving the location of the UAV.In addition, the step of calculating the data rate for the specific UAV may include calculating the data rate for the specific UAV based on a signal-to-interference-noise ratio (SINR) provided by the specific UAV based on a preset data service partition area for which the specific UAV provides data, a preset available bandwidth per UAV, the number of user terminals in a preset effective cell provided by the specific UAV, and the one or more user terminal location information. In addition, the step of calculating the data service remaining time of the specific UAV may include calculating energy remaining information of the specific UAV, which indicates the energy usage of the specific UAV over a certain period of time, based on the total energy information of the specific UAV, the hovering energy information of the specific UAV, and the movement energy information of the specific UAV among the UAV energy information for the specific UAV. And it may include a step of calculating a data service remaining time of the specific UAV, which indicates the data service provision time compared to the remaining energy of the specific UAV, based on the energy remaining information of the specific UAV and the hovering energy information of the specific UAV.In addition, the step of selecting any one of the above and performing the step for targeting at least one UAV among the plurality of UAVs may include: calculating a data throughput predicted for the at least one UAV when partitioned based on a data rate for the specific UAV and considering the number of the plurality of UAVs, and multiplying the data throughput predicted for the at least one UAV when partitioned by the data service remaining time of the specific UAV to produce a first multiplication value; and calculating a data throughput predicted for the at least one UAV when positioned based on a data rate for the specific UAV and considering the number of the plurality of UAVs, and multiplying the data throughput predicted for the at least one UAV when positioned by the data service remaining time of the specific UAV by additionally considering movement energy inference information of the UAV for movement energy information of the UAV among the UAV energy information for the specific UAV to produce a second multiplication value. In addition, the step of selecting one of the above and performing the partitioning on one or more UAVs among the plurality of UAVs may be performed by comparing the first multiplication value and the second multiplication value, such that if the first multiplication value is large, the partitioning may be performed on one or more UAVs among the plurality of UAVs, and if the second multiplication value is large, the positioning may be performed on one or more UAVs among the plurality of UAVs.

[0009] Furthermore, the following provides a computer-readable recording medium having recorded thereon a program for executing a network service optimization method considering the energy of the UAV described above on a computer.

[0010]

[0011] According to the embodiments of the present specification described above, by providing an algorithm for positioning of a UAV and cell partitioning of an access point (AP) system based on an unmanned aerial vehicle (UAV), it is possible to select an appropriate location for the UAV and determine network coverage, thereby alleviating the disadvantages of the energy operation time of the UAV for providing high-quality data services to a large number of user terminals. In addition, the role of a wireless access point-based repeater mounted on a means with restricted and limited movement can be supplemented in sections and areas where user terminals are densely packed, so that the UAV-based access point system can provide maximum throughput in response to movement of user terminals and changes in required data.

[0012]

[0013] FIG. 1 is a block diagram illustrating a network service optimization system considering the energy of a UAV according to one embodiment of the present invention.

[0014] FIG. 2 is a drawing for explaining a partitioning method of a UAV according to one embodiment of the present invention.

[0015] FIG. 3 is a drawing for explaining a method for positioning a UAV according to one embodiment of the present invention.

[0016] FIG. 4 is a flowchart illustrating a network service optimization method considering the energy of a UAV according to one embodiment of the present invention.

[0017] FIG. 5 is a block diagram illustrating a network service optimization device considering the energy of a UAV according to another embodiment of the present invention.

[0018]

[0019] Hereinafter, embodiments of the present specification will be described in detail with reference to the drawings. However, detailed descriptions of well-known functions or components that may obscure the gist of the embodiments in the following description and the attached drawings will be omitted. Additionally, throughout the specification, the term "including" a component does not exclude other components, unless specifically stated otherwise, but rather implies the inclusion of other components.

[0020] Additionally, while terms such as "first" and "second" may be used to describe various components, the components should not be limited by these terms. Terms may be used to distinguish one component from another. For example, without departing from the scope of this specification, a first component could be referred to as a second component, and similarly, a second component could also be referred to as a first component.

[0021] The terminology used herein is for the purpose of describing specific embodiments only and is not intended to be limiting of the present disclosure. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, it should be understood that the terms "comprise" or "have" indicate the presence of a described feature, number, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0022] Unless specifically defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which this specification pertains. Terms defined in commonly used dictionaries should be interpreted to have a meaning consistent with their meaning in the context of the relevant technology, and will not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0023] FIG. 1 is a block diagram illustrating a network service optimization system considering the energy of a UAV according to one embodiment of the present invention.

[0024] Referring to FIG. 1, a UAV operation optimization system (10) according to one embodiment of the present invention may include a UAV energy information acquisition device (100) and a UAV operation optimization device (200). In addition, the UAV operation optimization system (10) may be connected to a plurality of Unmanned Aerial Vehicles (UAVs) equipped with wireless access point modules that provide wireless communication services and a plurality of user terminals via a network.

[0025] The UAV energy information acquisition device (100) can acquire UAV energy information pre-stored in the UAV from each of a plurality of UAVs in a network connected to the UAV operation optimization system (10). At this time, the energy information of the UAV can include the total energy information of the UAV regarding the total amount of energy when the UAV starts operating, the hovering energy information of the UAV regarding the amount of energy used for hovering of the UAV, and the movement energy information of the UAV regarding the amount of energy used for moving the location of the UAV. The UAV energy information acquisition device (100) can transmit the acquired UAV energy information for each of the plurality of UAVs to the UAV operation optimization device.

[0026] The network referred to in the present invention may be a core network integrated with a wired public network, a wireless mobile communication network, or a mobile Internet, and may mean a global open computer network structure that provides various services existing in the TCP / IP protocol (Transmission Control Protocol), UDP (User Datagram Protocol) and its upper layer, namely, HTTP (Hyper Text Transfer Protocol), HTTPS (Hyper Text Transfer Protocol Secure), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), MQTT Protocol (Message Queueing Telemetry Transport), IPFS (Inter Planetary File System), and the like, and may comprehensively mean a data communication network that can transmit and receive data in various forms without being limited to these examples.

[0027] The UAV operation optimization device (200) can receive UAV energy information from the UAV energy information acquisition device (100) or acquire UAV energy information for each of a plurality of UAVs from a network connected to the UAV operation optimization system (10). In addition, the UAV operation optimization device (200) can acquire user terminal location information for each of a plurality of user terminals from the network connected to the UAV operation optimization system (10).

[0028] A UAV operation optimization device (200) receives UAV energy information from each of a plurality of UAVs (Unmanned Aerial Vehicles) equipped with a wireless access point module, receives user terminal location information from each of a plurality of user terminals, calculates a data rate for a specific UAV among the plurality of UAVs based on one or more user terminal location information, calculates the remaining data service time of the specific UAV based on the UAV energy information of the specific UAV, and selects one of partitioning, which adjusts the spacing between effective cells without one or more UAVs moving their locations based on the data rate for the specific UAV and the remaining data service time of the specific UAV, or positioning, which adjusts the spacing between effective cells while one or more UAVs move their locations, and performs the operation for one or more UAVs among the plurality of UAVs. A description of the operation of the UAV operation optimization device (200) of FIG. 1 will be supplemented with the drawings to be described later.

[0029] FIG. 2 is a diagram for explaining a partitioning method of a UAV according to one embodiment of the present invention. FIG. 3 is a diagram for explaining a positioning method of a UAV according to one embodiment of the present invention.

[0030] Referring to FIGS. 2 and 3, a plurality of UAVs (210, 220, 230, 240, and 250) are equipped with wireless access point modules, and thus can function as access points (APs). In order to provide high-quality data services to a plurality of user terminals, it is an important issue to determine the locations and coverage of the plurality of UAVs (210, 220, 230, 240, and 250). Accordingly, the plurality of UAVs (210, 220, 230, 240, and 250) can operate by selecting either partitioning or positioning through the UAV operation optimization device (200) of the present invention in order to provide communication services to the user terminals.

[0031] A UAV operation optimization device (200) receives UAV energy information from each of a plurality of UAVs (Unmanned Aerial Vehicles, 210, 220, 230, 240 and 250) equipped with a wireless access point module, receives user terminal location information from each of a plurality of user terminals, and can calculate a data rate for a specific UAV (e.g., 220) among the plurality of UAVs (210, 220, 230, 240 and 250) based on one or more pieces of user terminal location information.

[0032] In an environment where multiple UAVs (210, 220, 230, 240, and 250) provide communication services based on optimized locations determined by the Particle Swarm Optimization (PSO) algorithm, if the locations and data demands of multiple user terminals change, the optimal service cannot be provided at that location. Therefore, the multiple UAVs (210, 220, 230, 240, and 250) must re-enter the computational process and move to a new location.

[0033] However, if each UAV (210, 220, 230, 240, and 250) moves whenever the location and data demand of the user terminal change, a lot of energy will be consumed in moving the location of the UAV within the limited battery energy of the UAV. To overcome this problem, by minimizing the movement of the UAV by compensating for the effective cells provided by the UAV and thereby compensating for this, the total data throughput that each UAV (210, 220, 230, 240, and 250) can provide can be maximized.

[0034] The UAV operation optimization device (200) can calculate the data rate (Ri) for a specific UAV (e.g., UAVi, where 220 is the ith UAV among 5 UAVs) using the following mathematical expression 1 based on user terminal location information ((x, y)) received from multiple user terminals.

[0035]

[0036] Referring to mathematical expression 1, refers to a signal-to-interference-plus-noise ratio (SINR) value provided by the UAVi according to the user terminal location information (x, y), B refers to the available bandwidth per UAV, Ni refers to the number of user terminals in the effective cell provided by the UAVi, and Ai refers to the data service partition area of ​​the UAVi. That is, the UAV operation optimization device (200) can calculate a data rate (Ri) for a specific UAV based on a preset data service partition area where a specific UAV (UAVi) provides data services, a preset available bandwidth per UAV, the number of user terminals in the preset effective cell provided by the specific UAV (UAVi), and a signal-to-interference-plus-noise ratio (SINR) provided by a specific UAV (UAVi) based on one or more user terminal location information.

[0037] The UAV operation optimization device (200) can calculate the remaining data service time (St) of a specific UAV (UAVi) based on the UAV energy information of a specific UAV (for example, UAVi, where 220 is the ith UAV among five UAVs) among the UAV energy information received from a plurality of UAVs (210, 220, 230, 240, and 250) using the following mathematical expressions 2 and 3. At this time, the UAV energy information can include the total energy information (Eo) of the UAV regarding the total amount of energy when the UAV starts operating, the hovering energy information (Eh) of the UAV regarding the amount of energy used for hovering of the UAV, and the movement energy information (Em,t) of the UAV regarding the amount of energy used for position movement of the UAV. The hovering energy information (Eh) of a UAV is information assuming that the UAV consumes uniform energy under conditions where the weather environment does not change rapidly while hovering, and the UAV operation optimization device (200) can calculate the remaining energy information (Et) of a specific UAV (UAVi) at a certain time t through the following mathematical expression 2 based on the UAV energy information for the specific UAV (UAVi).

[0038]

[0039] Referring to mathematical expression 2, the UAV operation optimization device (200) can calculate energy remaining information (Et) of a specific UAV (UAVi) indicating energy usage of a specific UAV (UAVi) over a certain period of time based on the total energy information (Eo) of a specific UAV (UAVi), hovering energy information (Eh) of a specific UAV (UAVi), and movement energy information (Em,t) of a specific UAV (UAVi) among UAV energy information for a specific UAV (UAVi).

[0040] The UAV operation optimization device (200) can calculate the remaining data service time (St) of a specific UAV (UAVi) based on the remaining energy information (Et) of a specific UAV (UAVi) and the hovering energy information (Eh) of the specific UAV (UAVi) using the following mathematical expression 3.

[0041]

[0042] Referring to mathematical expression 3, the UAV operation optimization device (200) can calculate the data service remaining time (St) of a specific UAV (UAVi) indicating the data service provision time compared to the remaining energy of the specific UAV (UAVi) based on the energy remaining information of the specific UAV (UAVi) and the hovering energy information (Eh) of the specific UAV (UAVi).

[0043] The UAV operation optimization device (200) can select either partitioning, which adjusts the spacing of effective cells without one or more UAVs moving their positions, or positioning, which adjusts the spacing of effective cells while one or more UAVs move their positions, based on the data rate (Ri) for a specific UAV (UAVi) and the remaining data service time (St) of the specific UAV (UAVi), and perform the operation on one or more UAVs among a plurality of UAVs (210, 220, 230, 240, and 250).

[0044] The UAV operation optimization device (200) of the present invention can select a command to operate either partitioning, which adjusts the spacing between effective cells of the UAV, or positioning, which moves the location to adjust the effective cells of the UAV, as the locations of multiple user terminals change, based on the total amount of data rate that can be provided during the current remaining energy of the UAV. Accordingly, the UAV operation optimization device (200) can calculate the predicted data throughput and the remaining data service time when the UAV performs partitioning, which adjusts the spacing between effective cells, at the current time based on the variables (Ri, St) defined above, and can calculate the predicted data throughput and the remaining data service time when the UAV performs positioning, which moves the location to adjust the effective cells.

[0045] The UAV operation optimization device (200) can calculate the predicted data processing rate (TPpa) when one or more UAVs are partitioned by considering the data rate (Ri) for a specific UAV (UAVi) as the predicted data rate (Rpa,i) when the specific UAV (UAVi) is partitioned, using the following mathematical expression 4.

[0046]

[0047] Referring to mathematical expression 4, Nbs represents the number of multiple UAVs (for example, referring to FIGS. 2 and 3, the total number of UAVs may be 5, which is 210, 220, 230, 240, and 250).

[0048] The UAV operation optimization device (200) may regard the data service remaining time (St) for a specific UAV (UAVi) as the data service remaining time (Spa,t) of the UAV, which means the data service provision time according to partitioning compared to the remaining energy of the UAV for a certain time t. Thereafter, the UAV operation optimization device (200) may multiply the data processing rate (TPpa) predicted at the time of partitioning of one or more UAVs and the data service remaining time (Spa,t) of the UAVs at the time of partitioning, to produce a first multiplication value (TPpa×Spa,t) representing the data processing rate predicted at the time of partitioning of one or more UAVs compared to the data service remaining time. That is, the UAV operation optimization device (200) calculates a data processing rate (TPpa) predicted when one or more UAVs are partitioned by considering the number of multiple UAVs (e.g., 210, 220, 230, 240, and 250) based on a data rate (Ri) for a specific UAV (UAVi), and multiplies the predicted data processing rate (TPpa) when one or more UAVs are partitioned and the data service remaining time (St) of the specific UAV (UAVi) considered as the data service remaining time (Spa,t) according to partitioning against the remaining energy of the UAV to produce a first multiplication value (TPpa×Spa,t).

[0049] The UAV operation optimization device (200) can calculate the predicted data processing rate (TPpo) of one or more UAVs during positioning by considering the data rate (Ri) for a specific UAV (UAVi) as the predicted data rate (Rpo,i) when the specific UAV (UAVi) is positioning, using the following mathematical expression 5.

[0050]

[0051] Referring to mathematical expression 5, Nbs represents the number of multiple UAVs (for example, referring to FIGS. 2 and 3, it may be 5, which is the total number of UAVs, 210, 220, 230, 240, and 250).

[0052] The UAV operation optimization device (200) infers the remaining data service time (St) for a specific UAV (UAVi) and the movement energy information (Em,t) of the UAV among the energy information for the specific UAV (UAVi). ) based on the following mathematical expression 6, the remaining data service time of the UAV, which means the data service provision time during positioning compared to the remaining energy of the UAV for a certain time t, is calculated. ) can be calculated.

[0053]

[0054] Referring to mathematical expression 6, the UAV's movement energy inference information ( ) means information that infers the movement energy information (Em,t) of the UAV. In general, when a UAV moves a location, the energy consumed for the movement of the UAV may be affected by weather and environmental conditions including the distance between the departure point and the destination, the difference in altitude at which the UAV is currently hovering, and the speed at which the UAV moves according to the wind speed. In order to solve this problem, the UAV operation optimization device (200) of the present invention considers weather and environmental conditions and converts the movement energy information (Em,t) of the UAV input from a plurality of UAVs into the movement energy inference information ( The remaining data service time of the UAV, which means the data service provision time according to the positioning compared to the remaining energy of the UAV for a certain period of time t, is considered as ) can be calculated.

[0055] The UAV operation optimization device (200) is configured to optimize the predicted data processing rate (TPpo) of one or more UAVs when positioning and the remaining data service time when positioning the UAVs. A second multiplication value (TPpo × ) representing the predicted data processing rate when one or more UAVs are positioned relative to the remaining data service time. ) can be produced. That is, the UAV operation optimization device (200) calculates the predicted data processing rate (TPpo) when one or more UAVs are positioned by considering the number of multiple UAVs (e.g., 210, 220, 230, 240, and 250) based on the data rate (Ri) for a specific UAV (UAVi), and calculates the predicted data processing rate (TPpo) when one or more UAVs are positioned and the UAV's movement energy inference information (Em,t) for the UAV's movement energy information among the UAV energy information for the specific UAV (UAVi). By additionally considering the remaining data service time according to the positioning versus the remaining energy of the UAV ( ) is multiplied by the remaining data service time (St) of a specific UAV (UAVi) considered as a second multiplication value (TPpo × ) can be produced.

[0056] The UAV operation optimization device (200) is a first multiplication value (TPpa × ) and the second odds value (TPpo× ), by comparing the odds value (TPpa × ) is large, partitioning is performed on one or more UAVs among multiple UAVs, and the second multiplication value (TPpo × ) in this case, positioning can be performed targeting one or more UAVs among multiple UAVs.

[0057] The UAV operation optimization device (200) is a first multiplication value (TPpa × ) is the second odds value (TPpo × ) is greater than, as shown in FIG. 2, when multiple user terminals are concentrated in a specific area according to the movement of multiple users, and thus the location information of multiple user terminals changes, a command to perform positioning based on the Gradient Ascent Method for one or more UAVs (210 and 220 in FIG. 2) can be transmitted in consideration of the efficiency of data service and data processing rate. After this, one or more UAVs (210 and 220 in FIG. 2) can improve the maximum data rate by adjusting the spacing of effective cells without moving.

[0058] On the other hand, the UAV operation optimization device (200) has a second multiplication value (TPpo × ) is the first odds value (TPpa × ), referring to FIG. 3, when multiple user terminals are concentrated in a specific area according to the movement of multiple users, and thus the location information of multiple user terminals changes, a command to perform positioning based on Particle Swarm Optimization (PSO) targeting one or more UAVs (220 and 230 in FIG. 3) can be transmitted in consideration of the efficiency of data service and data processing rate. After that, one or more UAVs (220 and 230 in FIG. 3) can move directly to find a position that improves the maximum data rate, and set the spacing of effective cells at that position using a Voronoi diagram.

[0059] That is, the UAV operation optimization device (200) can adaptively respond to user movement (change in position of user terminal) and weather or environmental conditions by utilizing commands to change the position (positioning) of an access point module-based UAV or to transform an effective cell (partitioning) in a limited energy environment composed of an access point module-based UAV.

[0060] FIG. 4 is a flowchart illustrating a network service optimization method considering the energy of a UAV according to one embodiment of the present invention.

[0061] Referring to FIG. 4, a UAV operation optimization device can receive UAV energy information from each of a plurality of Unmanned Aerial Vehicles (UAVs) equipped with a wireless access point module (S410). The UAV operation optimization device (e.g., the UAV operation optimization (200) of FIG. 1) can receive UAV energy information for each of the plurality of UAVs from a UAV energy information acquisition device (e.g., the UAV energy information acquisition device (100) of FIG. 1) or can acquire UAV energy information for each of the plurality of UAVs from a network connected to the plurality of UAVs. At this time, the UAV energy information can include total energy information of the UAV for the total amount of energy when the UAV starts operating, hovering energy information of the UAV for the amount of energy used for hovering of the UAV, and movement energy information of the UAV for the amount of energy used for moving the location of the UAV.

[0062] A UAV operation optimization device can receive user terminal location information from each of a plurality of user terminals, and calculate a data rate for a specific UAV among the plurality of UAVs based on one or more pieces of user terminal location information (S430). The UAV operation optimization device can calculate the data rate for the specific UAV based on a preset data service partition area for which the specific UAV provides data services, a preset available bandwidth per UAV, the number of user terminals within a preset effective cell provided by the specific UAV, and a signal-to-interference-noise ratio (SINR) provided by the specific UAV based on the one or more pieces of user terminal location information.

[0063] The UAV operation optimization device can calculate the remaining data service time of the specific UAV based on the UAV energy information of the specific UAV (S450). The UAV operation optimization device can calculate the remaining energy information of the specific UAV, which indicates the energy usage of the specific UAV over a certain period of time, based on the total energy information of the specific UAV, the hovering energy information of the specific UAV, and the movement energy information of the specific UAV among the UAV energy information for the specific UAV, and can calculate the remaining data service time of the specific UAV, which indicates the data service provision time compared to the remaining energy of the specific UAV, based on the remaining energy information of the specific UAV and the hovering energy information of the specific UAV.

[0064] The UAV operation optimization device may select one of partitioning, which adjusts the spacing between effective cells without one or more UAVs moving their positions, or positioning, which adjusts the spacing between effective cells while one or more UAVs move their positions, based on the data rate for the specific UAV and the remaining data service time of the specific UAV, and perform the operation on one or more UAVs among the plurality of UAVs (S470). The UAV operation optimization device calculates a data throughput predicted when the one or more UAVs are partitioned based on a data rate for the specific UAV and considering the number of the plurality of UAVs, and calculates a first multiplication value by multiplying the data throughput predicted when the one or more UAVs are partitioned and the remaining data service time of the specific UAV, and calculates a second multiplication value by multiplying the data throughput predicted when the one or more UAVs are positioned based on a data rate for the specific UAV and considering the number of the plurality of UAVs, and calculating a second multiplication value by multiplying the data throughput predicted when the one or more UAVs are positioned and the remaining data service time of the specific UAV that additionally considers movement energy inference information of the UAV for movement energy information of the UAV among UAV energy information for the specific UAV. Furthermore, the UAV operation optimization device can perform the partitioning on one or more UAVs among the plurality of UAVs when the first multiplication value is large by comparing the first multiplication value and the second multiplication value, and can perform the positioning on one or more UAVs among the plurality of UAVs when the second multiplication value is large.

[0065] FIG. 5 is a block diagram illustrating a network service optimization device considering the energy of a UAV according to another embodiment of the present invention.

[0066] The UAV operation optimization device (300) of FIG. 5 may be the same as the UAV operation optimization device (200) of FIG. 1. The UAV operation optimization device (300) may include at least one processor (310), a memory (320), and a transmission / reception device (330) that is connected to a network and performs communication. In addition, the UAV operation optimization device (300) may further include an input interface device (340), an output interface device (350), a storage device (360), etc. Each component included in the UAV operation optimization device (300) may be connected by a bus (bus, 370) to communicate with each other. However, each component included in the UAV operation optimization device (300) may be connected through individual interfaces or individual buses centered around the processor (310), rather than a common bus (370). For example, the processor (310) may be connected to at least one of a memory (320), a transceiver (330), an input interface device (340), an output interface device (350), and a storage device (360) via a dedicated interface.

[0067] The processor (310) can execute program commands stored in at least one of the memory (320) and the storage device (360). The processor (310) may refer to a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor in which methods according to embodiments of the present invention are performed. Each of the memory (320) and the storage device (360) may be configured with at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory (320) may be configured with at least one of a read-only memory (ROM) and a random access memory (RAM).

[0068] According to the embodiments of the present specification described above, by providing an algorithm for positioning of a UAV and cell partitioning of an access point (AP) system based on an unmanned aerial vehicle (UAV), it is possible to select an appropriate location for the UAV and determine network coverage, thereby alleviating the disadvantages of the energy operation time of the UAV for providing high-quality data services to a large number of user terminals. In addition, the role of a wireless access point-based repeater mounted on a means with restricted and limited movement can be supplemented in sections and areas where user terminals are densely packed, so that the UAV-based access point system can provide maximum throughput in response to movement of user terminals and changes in required data.

[0069] Embodiments according to the present specification may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of hardware implementation, an embodiment of the present specification may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, etc. In the case of firmware or software implementation, an embodiment of the present specification may be implemented in the form of a module, procedure, function, etc. that performs the capabilities or operations described above. Software code may be stored in a memory and executed by a processor. The memory may be located inside or outside the processor and may exchange data with the processor by various means already known in the art.

[0070] Meanwhile, the embodiments of the present disclosure can be implemented as computer-readable codes on a computer-readable recording medium. Computer-readable recording media include all types of recording devices that store data that can be read by a computer system. Examples of computer-readable recording media include ROMs, RAMs, CD-ROMs, magnetic tapes, floppy disks, optical data storage devices, etc. Furthermore, the computer-readable recording media can be distributed across network-connected computer systems, so that the computer-readable codes can be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the embodiments can be easily inferred by programmers in the technical field to which the present disclosure pertains.

[0071] The present disclosure has been described above, focusing on various embodiments thereof. Those skilled in the art will appreciate that various embodiments may be modified without departing from the essential characteristics of the present disclosure. Therefore, the disclosed embodiments should be considered illustrative rather than restrictive. The scope of the present disclosure is set forth in the claims, not the foregoing description, and all differences within the scope equivalent thereto should be construed as being encompassed by the present disclosure.

[0072] While most terms used in this invention are commonly used in the field, some terms were arbitrarily selected by the applicant, and their meanings are described in detail in the following description, as needed. Therefore, the present invention should be understood based on the intended meaning of the terms, not simply their names or meanings.

[0073] It will be apparent to those skilled in the art that the present invention can be embodied in other specific forms without departing from the essential characteristics thereof. Therefore, the above detailed description should not be construed as limiting in any respect, but rather as illustrative. The scope of the present invention should be determined by a reasonable interpretation of the appended claims, and all modifications within the scope of equivalents of the present invention are intended to be included within the scope of the present invention.

Claims

1. A step in which a UAV operation optimization device receives UAV energy information from each of a plurality of UAVs (Unmanned Aerial Vehicles) equipped with a wireless access point module; A step of the UAV operation optimization device receiving user terminal location information from each of a plurality of user terminals and calculating a data rate for a specific UAV among the plurality of UAVs based on one or more of the user terminal location information; A step of the UAV operation optimization device calculating the remaining data service time of the specific UAV based on the UAV energy information of the specific UAV; and A step of selecting one of partitioning, which adjusts the spacing of effective cells without one or more UAVs moving their positions, or positioning, which adjusts the spacing of effective cells while one or more UAVs move their positions, based on the data rate for the specific UAV and the remaining data service time of the specific UAV, and performing the step on one or more UAVs among the plurality of UAVs; The above UAV energy information is, A method for optimizing UAV operation, comprising: total energy information of the UAV for the total amount of energy at the start of operation of the UAV; hovering energy information of the UAV for the amount of energy used for hovering of the UAV; and movement energy information of the UAV for the amount of energy used for moving the location of the UAV.

2. In paragraph 1, The step of calculating the data rate for the above specific UAV is: A method for optimizing UAV operation, wherein the data rate for the specific UAV is calculated based on a preset data service partition area provided by the specific UAV, a preset available bandwidth per UAV, a number of user terminals within a preset effective cell provided by the specific UAV, and a signal-to-interference-noise ratio (SINR) provided by the specific UAV based on information on the location of one or more user terminals.

3. In paragraph 1, The step of calculating the remaining data service time of the above specific UAV is: A step of calculating energy remaining information of the specific UAV, which indicates the energy usage of the specific UAV over a certain period of time, based on the total energy information of the specific UAV, the hovering energy information of the specific UAV, and the movement energy information of the specific UAV among the UAV energy information for the specific UAV; and A method for optimizing UAV operation, comprising: calculating a data service remaining time of the specific UAV, which indicates the data service provision time compared to the remaining energy of the specific UAV, based on energy remaining information of the specific UAV and hovering energy information of the specific UAV.

4. In paragraph 1, The step of selecting one of the above and performing the step of targeting one or more UAVs among the plurality of UAVs is as follows: A step of calculating a data throughput predicted when one or more UAVs are partitioned based on a data rate for the specific UAV and considering the number of the plurality of UAVs, and multiplying the predicted data throughput when the one or more UAVs are partitioned and the remaining data service time of the specific UAV to produce a first multiplication value; and A method for optimizing UAV operation, comprising: calculating a predicted data processing rate for positioning of one or more UAVs based on a data rate for the specific UAV and considering the number of the plurality of UAVs; calculating a second multiplication value by multiplying the predicted data processing rate for positioning of the one or more UAVs and the remaining data service time of the specific UAV by additionally considering the UAV's movement energy inference information for the UAV's movement energy information among the UAV energy information for the specific UAV; 5. In paragraph 4, The step of selecting one of the above and performing the step of targeting one or more UAVs among the plurality of UAVs is as follows: By comparing the first odds value and the second odds value, If the above first multiplication value is large, the partitioning is performed on one or more UAVs among the plurality of UAVs, A method for optimizing UAV operation that performs the positioning targeting at least one UAV among the plurality of UAVs when the second multiplication value is large.

Images