Hybrid offline-online unmanned aerial vehicle path design and subchannel allocation in unmanned aerial vehicle relay orthogonal frequency division multiple access network

Abstract

Disclosed is a technique for operating an unmanned aerial vehicle support network In an embodiment, a method for determining a radio resource allocation and a flight path of an unmanned aerial vehicle by an electronic device in a system including the unmanned aerial vehicle and multiple device pairs, in which a source device and a destination device included in the corresponding device pair perform OFDMA-based communication through the relaying of the unmanned aerial vehicle, may comprise the steps of: configuring requirement conditions including a minimum required end-to-end throughput condition for each of the multiple device pairs; and determining the radio resource allocation and the flight path of the unmanned aerial vehicle to maximize the sum of end-to-end throughputs of the multiple device pairs while satisfying the configured requirement conditions.

Description

Hybrid Offline-Online UAV Path Design and Subchannel Allocation in Orthogonal Frequency Division Multiple Access Networks for UAV Relay

[0001] The present disclosure relates to techniques for operating a drone support network, and more particularly, to techniques for optimizing flight paths and OFDMA-based radio resource allocation of drones performing relay functions.

[0002] Unmanned Aerial Vehicles (UAVs) have many advantages, including flexible deployment in three-dimensional space, rapid mobility, small size, and low cost, and are widely used in various fields. Wireless networks are also one area where UAVs can be used to improve network performance. Based on this potential, active research is being conducted on integrating UAVs into wireless networks, which are called "UAV-enabled networks." For example, UAVs can act as aerial base stations (ABSs) to relay data between ground base stations (GBSs) and ground users (GUs), or relay communications between ground users (GUs).

[0003] Despite these research findings, UAV-supported networks still require improvements to function as practical communication systems. For example, optimization of UAV flight paths, radio resource allocation (e.g., OFDMA-based radio resource allocation), and algorithms capable of performing these optimizations under real-world conditions (e.g., limitations in channel information, computational constraints, etc.) may be necessary.

[0004] Therefore, we aim to provide a technology for efficiently operating a drone support network (e.g., a technology for optimizing the flight path of a drone performing a relay function and OFDMA-based radio resource allocation).

[0005] One aspect of the present disclosure provides a method for determining a flight path and wireless resource allocation of an unmanned aerial vehicle in a system including a plurality of device pairs, wherein a source device and a destination device included in the device pairs perform OFDMA-based communication through a relay of an unmanned aerial vehicle, the method comprising: setting a requirement condition, wherein the requirement condition includes a minimum required end-to-end throughput condition of each of the plurality of device pairs; and determining a flight path and wireless resource allocation of the unmanned aerial vehicle so as to maximize a sum of the end-to-end throughputs of the plurality of device pairs while satisfying the set requirement condition.

[0006] In some embodiments, the wireless resources of the system may be configured based on N (a natural number greater than or equal to 2) time slots, each of the time slots including an uplink subslot and a downlink subslot, and K (a natural number greater than or equal to 2) subchannels, each of which includes at least one subcarrier. In some embodiments, the step of determining the wireless resource allocation may include the step of allocating an uplink wireless resource, which is formed by a combination of the N uplink subslots and the K subchannels, to transmission of a source device included in the device pairs, and a downlink wireless resource, which is formed by a combination of the N downlink subslots and the K subchannels, to reception of a destination device included in the device pairs.

[0007] In some embodiments, each of the K subchannels may be composed of a preset number of subcarriers.

[0008] In some embodiments, the determining step may include an offline step of determining a flight path and wireless resource allocation of the unmanned aerial vehicle by approximating an end-to-end throughput of each of the plurality of device pairs to an expected end-to-end throughput; and an online step of performing flight and wireless resource allocation according to the flight path and wireless resource allocation determined in the offline step, but updating the wireless resource allocation based on instantaneous channel state information of each link, thereby performing updated wireless resource allocation.

[0009] In some embodiments, the offline step may include calculating the expected end-to-end throughput based on a probabilistic line of sight channel model.

[0010] In some embodiments, the offline step may include determining the flight path and wireless resource allocation of the unmanned aerial vehicle by approximating the expected end-to-end throughput to the end-to-end throughput when each link is in a line-of-sight channel state.

[0011] In some embodiments, the offline step may include: dividing the method into a first subproblem for allocating subchannels when a flight path is given and a second subproblem for optimizing the flight path when radio resource allocation is given; obtaining a solution of the first subproblem and a solution of the second subproblem using a block coordinate descent (BCD) technique; and determining the flight path and radio resource allocation of the unmanned aerial vehicle based on the obtained solution of the first subproblem and the obtained solution of the second subproblem.

[0012] In some embodiments, the online step may include allocating wireless resources for at least some of the plurality of device pairs for the current time slot based on wireless resource allocations performed prior to the current time slot, channel state information of each link in the current time slot, and wireless resource allocations determined in the offline step.

[0013] In some embodiments, the online step may include determining a radio resource allocation for the current time slot that maximizes the sum of an online expected end-to-end throughput for each device pair, the sum of the radio resource allocations executed up to the current time slot, the radio resource allocations for the current time slot, and the radio resource allocations after the current time slot determined in the offline step, while satisfying preset QoS requirements.

[0014] In some embodiments, the online step may include: modifying a QoS requirement in the current time slot if the QoS requirement is not met; and determining a radio resource allocation for the current time slot that maximizes the sum of the online expected end-to-end throughput for each device pair while satisfying the modified QoS requirement.

[0015] In some embodiments, the determining step comprises the following mathematical expression:

[0016]

[0017] A step of determining the flight path and radio resource allocation of the unmanned aerial vehicle based on the solution of the problem expressed as

[0018] The above requirements may include (1a), (1b), (1c), (1d), (1e), (1f), (1g) in the above mathematical formula. In the above mathematical formula and are the indexes of the source devices, respectively. represents a set of represents a destination device corresponding to the source device, and the system operates based on time slots obtained by dividing the unit operating time (T) of the drone into N, each of the time slots has a time length δ and is composed of an uplink subslot and a downlink subslot, and n is an index of a time slot and has a natural number value from 1 to N, represents the set of n, and represents the end-to-end throughput of the device pair in time slot n and the minimum average required throughput of the device pair, is the position of the drone at time slot n, corresponds to time slots 1 to N. represents a set of (i.e. flight paths), and represent the initial and final positions of the drone, respectively. represents the maximum speed of the above drone, represents the height of the drone at time slot n, and represent the minimum and maximum heights at which the drone can be positioned, respectively, and k is the index of the subchannel in the OFDMA technique and has a natural number value from 1 to K. is a value indicating whether a radio resource identified by time slot n and subchannel k has been allocated for data transmission from the source device s to the drone. is the set of values ​​that n and k can have. represents a set of is a destination device from the above drone A value indicating whether a radio resource identified by time slot n and subchannel k has been allocated for data transmission. is the set of values ​​that n and k can have. represents a set of .

[0019] In some embodiments, the determining step may include an offline step of determining a flight path and wireless resource allocation of the unmanned aerial vehicle to maximize a sum of end-to-end throughputs of the plurality of device pairs while satisfying the set requirement using statistical characteristics; and a step of checking whether a preset QoS requirement is satisfied in a current time slot; a step of modifying the set QoS requirement if it is determined that the QoS requirement cannot be satisfied; and a step of adjusting the wireless resource allocation for the current time slot determined in the offline step based on instantaneous channel state information according to the modified QoS requirement.

[0020] In some embodiments, the electronic device may be the unmanned aerial vehicle.

[0021] In some embodiments, the electronic device is a device physically separate from the airborne base station, and the method may further include a step of providing information about the determined flight path and radio resource allocation to the unmanned aerial vehicle.

[0022] Another aspect of the present disclosure provides a method for operating an unmanned aerial vehicle in a system comprising a plurality of device pairs, wherein a source device and a destination device included in the device pairs perform OFDMA-based communication through a relay of an unmanned aerial vehicle, the method comprising: setting a flight path and a radio resource allocation of the unmanned aerial vehicle; and performing a flight and the relay according to the set flight path and the radio resource allocation, wherein the method includes updating the radio resource allocation based on the set radio resource allocation and instantaneous channel state information of a link between each of the plurality of user device pairs and the unmanned aerial vehicle.

[0023] In some embodiments, the setting step may include determining the flight path and wireless resource allocation of the unmanned aerial vehicle by approximating the end-to-end throughput of each of the plurality of device pairs to an expected end-to-end throughput.

[0024] In some embodiments, the step of updating the wireless resource allocation may include the step of checking whether a preset QoS requirement is satisfied in the current time slot; if it is determined that the QoS requirement cannot be satisfied, the step of modifying the preset QoS requirement; and the step of adjusting the wireless resource allocation for the current time slot determined in the offline step based on instantaneous channel state information according to the modified QoS requirement.

[0025] Another aspect of the present disclosure provides an electronic device for determining a flight path and wireless resource allocation of an unmanned aerial vehicle in a system comprising a plurality of device pairs, a source device and a destination device included in the device pairs performing OFDMA-based communication via a relay of an unmanned aerial vehicle, the electronic device comprising: a processor; and a computer-readable storage medium comprising instructions, the instructions causing the electronic device to perform at least one of the embodiments of the present disclosure in response to execution by the processor.

[0026] Another aspect of the present disclosure provides an electronic device, comprising: a processor; one or more hardware-based transceivers; and a computer-readable storage medium comprising instructions, wherein the instructions, in response to execution by the processor, cause the electronic device to perform at least one of the embodiments of the present disclosure.

[0027] Another aspect of the present disclosure provides a non-transitory recording medium storing instructions readable by a processor of an electronic device, the instructions causing the processor to perform at least one of the embodiments of the present disclosure.

[0028] This Summary is provided to introduce a selection of concepts in a simplified form that are further explained in the Detailed Description below. This Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to implementations that solve any or all of the problems mentioned in any portion of this specification. In addition to the exemplary aspects, embodiments, and features described above, additional aspects, embodiments, and features will become apparent by reference to the following Detailed Description and Drawings.

[0029] Some embodiments of the present disclosure may have the following advantages. However, this does not mean that all embodiments must include all of the advantages, and the scope of the present invention should not be construed as being limited thereby.

[0030] According to some embodiments, a UAV-enabled network can be provided that has excellent system throughput while meeting requirements (e.g., minimum required end-to-end throughput requirements).

[0031] According to some embodiments, a technique for optimizing OFDMA-based radio resource allocation and flight path in a UAV-based relay communication system may be provided.

[0032] According to some embodiments, a technique may be provided for efficiently determining OFDMA-based radio resource allocation and flight paths (e.g., in terms of computational load and given channel information, etc.) in a UAV-based relay communication system.

[0033] Figure 1 illustrates a system for describing some embodiments of a UAV support network.

[0034] FIG. 2 is a flowchart illustrating some embodiments of flight path and wireless resource allocation.

[0035] Figure 3 is a flowchart illustrating some embodiments of decisions made in the offline phase.

[0036] Figure 4 is a flowchart illustrating some embodiments of decision making in the online stage.

[0037] Figure 5 is a flowchart illustrating some embodiments for a UAV.

[0038] FIG. 6 is a block diagram illustrating an electronic device performing a method according to embodiments of the present disclosure.

[0039] The description of the present invention is merely an example for structural and functional explanation, and therefore, the scope of the present invention should not be construed as being limited by the embodiments described in the text. That is, since the embodiments can be modified in various ways and can take various forms, the scope of the present invention should be understood to include equivalents that can realize the technical idea. In addition, the purposes or effects presented in the present invention do not mean that a specific embodiment must include all of them or only such effects, and therefore, the scope of the present invention should not be construed as being limited thereby.

[0040] Meanwhile, the meanings of the terms described in this disclosure should be understood as follows.

[0041] Terms such as "first" and "second" are intended to distinguish one component from another, and the scope of the rights should not be limited by these terms. For example, the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component.

[0042] When a component is said to be "connected" to another component, it should be understood that while it may be directly connected to that other component, there may also be other components intervening. Conversely, when a component is said to be "directly connected" to another component, it should be understood that there are no other intervening components. Similarly, other expressions describing relationships between components, such as "between" and "directly between," or "adjacent to" and "directly adjacent to," should be interpreted similarly.

[0043] Singular expressions should be understood to include plural expressions unless the context clearly indicates otherwise, and terms such as "comprises" or "have" should be understood to specify the presence of a feature, number, step, operation, component, part or combination thereof, but not to exclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

[0044] For each step, the identifiers (e.g., a, b, c, etc.) are used for convenience of explanation and do not describe the order of the steps. The steps may occur in a different order than stated unless the context clearly dictates a specific order. That is, the steps may occur in the same order as stated, may be performed substantially simultaneously, or may be performed in the opposite order.

[0045] Figure 1 illustrates a system for describing some embodiments of a UAV support network.

[0046] In some embodiments, as illustrated in FIG. 1, the system (100) may include a UAV (110) and a plurality of devices (120_1, 120_2, 130_1, 130_2, 140_1, 140_2). The plurality of devices may be terminals capable of performing wireless communication (e.g., user equipment of a cellular communication system).

[0047] In some embodiments, the devices (120_S, 120_D) included in the first device pair can perform OFDMA-based communication through the relay of the UAV (110). Similarly, the devices (130_S, 130_D) included in the second device pair and the devices (140_S, 140_D) included in the third device pair can also perform OFDMA-based communication through the relay of the UAV (110). For example, the UAV (110) can be operated as an aerial relay to enable communication between devices that cannot directly communicate with each other.

[0048] In some embodiments, the wireless resources of the system (100) may be OFDMA-based wireless resources. For example, the wireless resources of the system (100) may be configured based on N (a natural number greater than or equal to 2) time slots and K subchannels.

[0049] For example, N (a natural number greater than or equal to 2) time slots can be obtained by dividing the unit operation time (T) of the system (100) equally into N. In this case, each time slot is can have a length of time.

[0050] In some embodiments, each time slot may include an uplink subslot and a downlink subslot. For example, the uplink subslot and the downlink subslot may be obtained by dividing each time slot equally into two.

[0051] In some embodiments, each of the K subchannels may include at least one subcarrier. For example, each of the K subchannels may be composed of a preset number of subcarriers.

[0052] In some embodiments, the UAV (110) may perform a relay operation while flying from an initial location to a final location for a predetermined period of time (e.g., a unit operation time).

[0053] The right drawing of Fig. 1 illustrates a relay situation of a UAV (110) when several time slots have passed from the relay situation of the UAV (110) of the left drawing of Fig. 1. The channel situation of each link may change over time. For example, in the left drawing, the two links (uplink and downlink) of the first device pair were in a line-of-sight channel state, but in the right drawing, it can be seen that the two links of the first device pair have changed to a non-line-of-sight channel state. In another example, the two links (uplink and downlink) of the second device pair were in a non-line-of-sight channel state, but in the right drawing, it can be seen that the two links of the first device pair have changed to a line-of-sight channel state. On the other hand, it can be seen that the two links of the third device pair maintain a line-of-sight channel state.

[0054] In some embodiments, devices (120_S, 120_D) included in the first device pair may perform OFDMA-based communication through relaying of a UAV (110). For example, as illustrated in FIG. 1, device (120_S) may be allocated uplink radio resources (e.g., uplink subslots and subchannels) to transmit data, and the UAV (110) that receives the data may transmit the data to the device (120_D) corresponding to the destination device through downlink radio resources (e.g., downlink subslots and subchannels). This relaying may also be applied to the second and third device pairs.

[0055] In some embodiments, the UAV (110) may allocate uplink radio resources to each pair of source nodes (s) (e.g., devices 120_S, 130_S, 140_S in FIG. 1) for uplink transmission. For example, the uplink radio resources may be a combination of N uplink subslots and K subchannels. In this case, each source node (s) may transmit data to be sent to the corresponding destination node (d(s)) using the allocated uplink radio resources, and the UAV (100) may receive the data and transmit the data to the corresponding destination node (d(s)) (e.g., devices 120_D 130_D, 140_D in FIG. 1) via the downlink radio resources.

[0056] In some embodiments, the UAV (110) may allocate downlink radio resources to the destination node (d(s)) of each device pair for downlink reception. For example, the downlink radio resources may be comprised of a combination of N downlink subslots and K subchannels.

[0057] In some embodiments, the electronic device in the system (100) can determine the flight path and radio resource allocation of the UAV (110).

[0058] In some embodiments, the electronic device determining the flight path and wireless resource allocation may be the UAV (110). In other embodiments, the electronic device determining the flight path and wireless resource allocation may be a separate electronic device physically separated from the UAV (110). In this case, the electronic device may provide information about the determined flight path and wireless resource allocation to the UAV (110). In still other embodiments, the determination of the flight path and wireless resource allocation may be shared between a separate electronic device physically separated from the UAV (110) and the UAV (110). For example, the offline step described below may be performed by a separate electronic device physically separated from the UAV (110), and the online step described below may be performed by the UAV (110). In this case, the electronic device may provide information about the flight path and wireless resource allocation determined in the offline step to the UAV (110).

[0059] For convenience of explanation, below, several embodiments will be described assuming that the electronic device that determines the flight path and wireless resource allocation is a UAV (110).

[0060] FIG. 2 is a flowchart illustrating some embodiments of flight path and wireless resource allocation.

[0061] In some embodiments, as illustrated in FIG. 2, after setting requirements (S210), the UAV (110) can determine a flight path and wireless resource allocation to maximize the end-to-end throughput of multiple device pairs (hereinafter, system throughput) while satisfying the set requirements (S220).

[0062] In some embodiments, the requirement may include a minimum required end-to-end throughput condition for each of the plurality of device pairs. For example, the minimum required end-to-end throughput condition for each of the plurality of device pairs may be the condition of (1a) of the optimization problem P0 described below.

[0063] In some embodiments, the UAV (110) can obtain a solution to an optimization problem given by Equation 1 to determine a flight path and radio resource allocation to maximize system throughput while satisfying set requirements.

[0064]

[0065] In mathematical expression 1, and are the index and of the source device respectively. represents a set of is the source device Indicates the destination device corresponding to .

[0066] n is the index of the time slot and has a natural number value from 1 to N, represents the set of n.

[0067] represents the end-to-end throughput of the device pair in time slot n, and can be expressed by mathematical expression 2. represents the minimum average required throughput of the device pair.

[0068] is the position of the UAV (110) in time slot n, corresponds to time slots 1 to N. represents a set of (i.e. flight paths).

[0069] and represent the initial and final positions of the UAV (110), respectively. represents the maximum speed of the UAV (110).

[0070] represents the height position of the drone at time slot n, and represent the minimum and maximum heights at which the UAV (110) can be positioned, respectively.

[0071] k is the index of the subchannel in the OFDMA technique and has a natural number value from 1 to K.

[0072] is a value indicating whether a radio resource identified by time slot n and subchannel k has been allocated for data transmission from source device s to UAV (110). Is represents a set of .

[0073] is a destination device from the above drone A value indicating whether a radio resource identified by time slot n and subchannel k has been allocated for data transmission. Is represents a set of .

[0074]

[0075] Here, and represents the data transmission rate from the source device s to the UAV in time slot n and the data transmission rate from the UAV to the destination device d(s), respectively, and can be expressed by mathematical expressions 3 and 4, respectively.

[0076]

[0077]

[0078] The problem (P0) is and constraint (1a) decision variables Q, A U , andA DSince it is a non-deterministic function, it is not easy to find a solution. To find a solution, perfect channel state information (CSI) for all time slots must be provided before the flight of the UAV (110), but this may be realistically impossible.

[0079] Accordingly, in some embodiments, the UAV (110) may perform a hybrid offline-offline algorithm to maximize system throughput while improving QoS satisfaction of a device pair without perfect CSI. For example, the QoS satisfaction may be the end-to-end throughput of each device pair.

[0080] In some embodiments, as illustrated in FIG. 2, the UAV (110) may perform an offline operation (S222) of determining a flight path and wireless resource allocation of the UAV by approximating the end-to-end throughput of each of the plurality of device pairs to an expected end-to-end throughput to determine a flight path and wireless resource allocation for maximizing system throughput while satisfying set requirements, and an online operation (S224) of performing flight and wireless resource allocation according to the flight path and wireless resource allocation determined in the offline step, but updating the wireless resource allocation based on instantaneous channel state information of each link, thereby performing updated wireless resource allocation.

[0081] In some embodiments, for block S222, the UAV (110) may determine the flight path and radio resource allocation of the unmanned aerial vehicle to maximize the end-to-end aggregate throughput (hereinafter, system throughput) of the plurality of device pairs while satisfying the set requirements using statistical characteristics.

[0082] For example, in an offline phase (e.g., before a flight starts), the UAV (110) calculates the expected end-to-end throughput based on a probabilistic LoS (hereinafter, PL) channel model, and jointly optimizes the flight path and radio resource allocation to satisfy the expected QoS requirements of all device pairs while maximizing the sum of the expected end-to-end throughputs calculated for multiple device pairs (hereinafter, expected system throughput) (S222), and in an online phase (e.g., during the UAV flight), the UAV can dynamically update the radio resource allocation by utilizing the instantaneous CSI of each time slot to improve the system throughput and the QoS satisfaction of the device pairs as much as possible (S224).

[0083] In some embodiments, the UAV (110) may utilize Equation 5 for optimization of the offline phase.

[0084]

[0085] Here, is the expected end-to-end throughput of a device pair (s,d(s)) in time slot n, which can be expressed by Equation 6.

[0086]

[0087] Here, and represents the expected data transmission rate from the source device s to the UAV and the expected data transmission rate from the UAV to the destination device d(s) in time slot n, respectively, and can be expressed by mathematical expressions 7 and 8.

[0088]

[0089]

[0090] In equations 7 and 8 , In the offline phase, when the link is LoS, it represents the transmission rate of the device, and in Equations 7 and 8, , Indicates the transmission rate of the device when the link is NLoS in the offline phase.

[0091] In equations 7 and 8 represents the probability that the link between device m and UAV is LoS, and can be expressed by mathematical expression 9.

[0092]

[0093] In mathematical expression 9 represents the elevation angle between device m and UAV at time slot n, and can be expressed by mathematical expression 10.

[0094]

[0095] Solving the deterministic optimization problem (P1) allows designing a 3D trajectory (i.e., flight path) using statistical CSI, but since problem (P1) is a mixed integer non-convex programming (MINCP) problem, it may be difficult to solve optimally.

[0096] Therefore, in some embodiments, the UAV (110) can approximate the problem (P1) into a more solvable problem.

[0097] In most real-world scenarios, the A2G channel gain in the LoS state is much larger than that in the NLoS state, and therefore the expected data transmission rate in the LoS state is much higher than that in the NLoS state. Therefore, Equations 7 and 8 can be approximated by Equations 11 and 12, respectively.

[0098]

[0099]

[0100] Approximate expected data transfer rate and Using , the expected end-to-end throughput of Equation 6 can be approximated by Equation 13.

[0101]

[0102] In some embodiments, the UAV (110) may be configured to provide an expected end-to-end throughput (e.g., ) is the expected end-to-end throughput when each link is in line-of-sight channel state (e.g., ) and determine the flight path and radio resource allocation based on the sum of the approximated data processing amount.

[0103] For example, using the approximated expected data rate and the approximated expected end-to-end throughput, problem (P1) can be approximated and expressed as problem (P2) as in Equation 14.

[0104]

[0105] Although problem (P2) is more traceable than problem (P1), it is still a MINCP problem. To address this difficulty, in some embodiments, the system may utilize the block coordinate descent (BCD) method. The BCD method is a widely used heuristic method for iteratively solving multivariable optimization problems by updating subsets of variables (blocks).

[0106] Figure 3 is a flowchart illustrating some embodiments of decisions made in the offline phase.

[0107] In some embodiments, as illustrated in FIG. 3, the UAV (110) may divide the problem (P2) in the offline stage into a first subproblem of allocating subchannels when a flight path is given and a second subproblem of optimizing a flight path when radio resource allocation is given (S310), and then obtain a solution to the first subproblem and a solution to the second subproblem using a block coordinate descent (BCD) technique (S320). In this case, the UAV (110) may determine a flight path and radio resource allocation based on the obtained solution to the first subproblem and the obtained solution to the second subproblem (S330).

[0108] The first subproblem described above (i.e., the radio resource allocation problem given a feasible UAV trajectory (Q)) can be expressed by mathematical expression (15).

[0109]

[0110] Problem (P3) is a mixed integer non-linear programming (MINLP) problem, but with slack variables By introducing , it can be reconstructed as a mixed integer linear programming (MILP) problem given by Equation 16.

[0111]

[0112] This problem (P4) can be solved with existing MILP solvers (e.g., CVX-Gurobi).

[0113] The second subproblem described above (i.e., feasible radio resource allocation) ) is given, the UAV trajectory optimization problem can be expressed by mathematical expression 17.

[0114]

[0115] Problem (P5) is difficult to solve because it is a non-convex optimization problem due to the objective function (17) and constraint (14a). Therefore, in some embodiments, the system can utilize successive convex approximation (SCA), which is widely used in solving non-convex optimization problems, to heuristically solve problem (P5). This successive convex approximation is widely used to solve non-convex optimization problems by repeatedly solving an approximated convex optimization problem.

[0116] In some embodiments, in the online phase, the UAV (110) may allocate radio resources for at least some of the plurality of device pairs for the current time slot based on the radio resource allocations performed up to the current time slot, the channel state information of each link in the current time slot, and the radio resource allocations determined in the offline phase.

[0117] In some embodiments, in the online phase, the UAV (110) can determine a radio resource allocation for the current time slot that maximizes the sum of the online expected end-to-end throughput for each device pair, which is comprised of the sum of the radio resource allocations executed up to the current time slot, the radio resource allocations for the current time slot, and the radio resource allocations after the current time slot determined in the offline phase, while satisfying preset QoS requirements.

[0118] For example, the online expected end-to-end throughput is expressed by Equation 19. For example, the preset QoS requirement may be (18a) included in Equation 18. For example, the online wireless resource allocation problem for maximizing system throughput while satisfying the QoS requirement of a device pair may be expressed as Equation 18.

[0119]

[0120] Here is the problem included in (P8) is the expected end-to-end throughput included in the offline phase problem (e.g., ) or approximated expected end-to-end throughput (e.g., ) rather than the current n on The average throughput of a pair of devices (s, d(s)) in the online phase to reflect the actual CSI value up to the time slot can be expressed by Equation 19.

[0121]

[0122] The first, second, and third items on the right side of Equation 19 represent the sum of end-to-end throughput for past time slots, the sum of end-to-end throughput for the current time slot, and the sum of expected end-to-end throughput for future time slots.

[0123] That is, a set of present and future time slots. When expressed as , the problem (P8) is the allocation of radio resources for current and future time slots (i.e., ) by optimizing the current time slot n on It can be viewed as an online optimization problem for allocating subchannels.

[0124] Problem (P8) is an integer decision variable and Because it is an integer programming (IP) problem, it is difficult to solve in real time.

[0125] To make problem (P8) easier to solve, we relax the constraints in (18d). and If we set to have a real value between 0 and 1, the problem (P8) can be transformed into a convex optimization problem (P9) as in Equation 20.

[0126]

[0127] Problem (P9) can be easily solved by a well-known optimization algorithm, but the solution obtained from Problem (P9) is and can have fractional values ​​due to relaxed constraints (i.e., Equation 20d). Therefore, to handle this, Each included in For the given subchannel index k, the largest Only for source devices with values ​​(for convenience, let's call them index s*) Round to 1 and the remaining source devices The value is replaced with 0. Likewise Each included in For the given subchannel index k, the largest Only for source devices with values ​​(for convenience, let's call them index s*) Round to 1 and the remaining source devices The value is replaced with 0.

[0128] In some embodiments, the UAV (110) is in the corresponding time slot (i.e., current time slot n on ) Solve problem P9 for each and After obtaining it, and Each included in and By performing the rounding process described above, the wireless resource allocation for the corresponding time slot can be determined.

[0129] In some other embodiments, the UAV (110) is in the corresponding time slot (i.e., current time slot n on) can satisfy the QoS requirements (e.g., condition (18a) of P9), and if it is expected that the QoS requirements will not be satisfied, at least some of the requirements of the optimization problem (e.g., condition (18a) of P9) are modified, and then a solution to the optimization problem reflecting the modified requirements is obtained to determine the radio resource allocation for the corresponding time slot. This is because the problem (P9) may be an optimization problem that is not feasible in the online stage. For example, the end-to-end throughput of each device pair realized up to the current time slot may be low due to reasons such as low realized A2G channel gain. If this is too low, it may not be possible to meet the QoS requirements of problem (P9) by allocating radio resources for the remaining time slots.

[0130] Figure 4 is a flowchart illustrating some embodiments of decision making in the online stage.

[0131] In some embodiments, as illustrated in FIG. 4, the UAV (110) may check whether the QoS requirements of each device pair in the current time slot are met (S410), and if the QoS requirements are not met, modify the QoS requirements (S420), and allocate wireless resources for the current time slot by solving an optimization problem including the QoS requirements (S430). For example, the UAV (110) may check whether the QoS requirements of each device pair can be met by solving the artificial problem (P11) described below (S410), modify the QoS requirements that cannot be met (S420), and obtain a solution to the problem (P12) described below and round it to obtain the time slot n. on Wireless resources can be allocated opportunistically (S430).

[0132] In some embodiments, the UAV (110) is configured to have an optimal value of problem (P11) expressed by Equation 21 of time slot n if the optimal value is 0. onIt is determined that the QoS requirement can be satisfied, and if not, the QoS requirement modification formula expressed by Equation 22 can be applied.

[0133]

[0134] In mathematical expression 21 is a slack variable is expressed as, is an artificial variable can be expressed as

[0135]

[0136] Using Equation 22, the online wireless resource allocation problem (P9) can be reformulated as Equation 23.

[0137]

[0138] Problem (P12) is a convex optimization problem that can be easily solved.

[0139] Figure 5 is a flowchart illustrating some embodiments for a UAV.

[0140] In some embodiments, as illustrated in FIG. 5, the UAV sets a flight path and wireless resource allocation of the unmanned aerial vehicle (S510), and performs the flight and the relay according to the set flight path and wireless resource allocation, and may update the wireless resource allocation based on the set wireless resource allocation and the instantaneous CSI of the link (i.e., each uplink and each downlink) between each of the plurality of device pairs and the unmanned aerial vehicle (S520).

[0141] For example, the UAV may determine and set a flight path and radio resource allocation by executing the embodiments (algorithms, etc.) for flight path and radio resource allocation in the offline phase described above for block S510. In another example, the UAV may receive information from another electronic device that determines the flight path and radio resource allocation by executing the embodiments (algorithms, etc.) for flight path and radio resource allocation described above for block S510, and may set the flight path and radio resource allocation.

[0142] In some embodiments, for the S520 block, the flight and the relay are performed according to the established flight path and radio resource allocation, but the radio resource allocation can be updated by executing embodiments (algorithms, etc.) for radio resource allocation in the online stage described above.

[0143] FIG. 6 is a block diagram illustrating an electronic device that performs a method according to embodiments of the present disclosure. In FIG. 6, the electronic device (600) is described as a single physical device, but depending on the embodiment, the electronic device (600) may be implemented in a form in which multiple devices are linked together (e.g., distributed computing).

[0144] In some embodiments, the electronic device (600) may include a memory (610), a processor (620), and a communication module (630), as illustrated in FIG. 6 . In some other embodiments, the electronic device (600) may further include all or some of the input / output interface (640) and other units. For example, if the electronic device (600) performing the embodiments is an unmanned aerial vehicle, it may further include units such as a flight means.

[0145] The memory (610) is a recording medium readable by an electronic device (e.g., a computer), and may include a random access memory (RAM), a read only memory (ROM), and a permanent mass storage device such as a disk drive. Here, the ROM and the permanent mass storage device may be included as a separate permanent storage device separate from the memory (610). In addition, the memory (610) may store an operating system and at least one program code (for example, a program such as a computer program for controlling the electronic device (600) by being stored on a recording medium included in the electronic device (600) to perform a method according to embodiments of the present disclosure). These software components may be loaded from a recording medium readable by an electronic device separate from the memory (610). The recording medium readable by the separate electronic device may include a recording medium readable by an electronic device such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, a memory card, etc. In another embodiment, the software components may be loaded into the memory (610) via a communication module (630) rather than a readable recording medium in the electronic device.

[0146] The processor (620) may be configured to process instructions of a program, such as a computer program, by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to the processor (620) by the memory (610) or the communication module (630). For example, the processor (620) may be configured to execute instructions received according to program code loaded into the memory (610). More specifically, the processor (620) may sequentially execute instructions according to the code of the computer program loaded into the memory (610) to perform processing related to a UAV support network according to an embodiment of the present disclosure (e.g., UAV flight path, optimization of radio resource allocation, communication, and relay). The communication module (630) may provide a function for communicating with other physical devices through a communication network, such as a computer network. For example, the present disclosure may be implemented in a manner in which a processor (620) of an electronic device (600) performs a part of the process of the present embodiment, and another physical device (e.g., an electronic device such as another computer, not shown) of a communication network performs the remaining process, while exchanging the processing results with the communication network through a communication module (630).

[0147] The input / output interface (640) may be a means for interfacing with an input / output device (650). For example, in the input / output device (650), the input device may include a device such as a keyboard or a mouse, and the output device may include a device such as a display or a speaker. In FIG. 6, the input / output device (650) is represented as a separate device from the electronic device (600), but depending on the embodiment, the electronic device (600) may be implemented such that the input / output device (650) is included in the electronic device (600).

[0148] The devices described above may be implemented as hardware components, software components, and / or a combination of hardware components and software components. For example, the devices and components described in the embodiments may be implemented using one or more general-purpose computers or special-purpose computers, such as a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them. The electronic device (600) may execute an operating system (OS) and one or more software applications running on the operating system. In addition, the electronic device (600) may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, the electronic device (600) is sometimes described as being used alone; however, those skilled in the art will appreciate that the electronic device (600) may include multiple processing elements and / or multiple types of processing elements. For example, the electronic device (600) may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

[0149] The software may include a computer program, code, instructions, or a combination of one or more of these, and may configure the electronic device (600) to operate as desired or may independently or collectively command the electronic device (600). The software and / or data may be embodied in any type of machine, component, physical device, computer storage medium, or device for interpretation by the electronic device (600) or for providing instructions or data to the electronic device (600). The software may also be distributed across networked computer systems and stored or executed in a distributed manner. The software and data may be stored on one or more computer-readable recording media.

[0150] The method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. At this time, the medium may be one that continuously stores a computer-executable program or one that temporarily stores it for execution or download. In addition, the medium may be various recording means or storage means in the form of a single or multiple hardware combinations, and is not limited to a medium directly connected to a computer system, but may also be distributed on a network. Examples of the medium may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and those configured to store program commands, including ROM, RAM, and flash memory. In addition, examples of other media may include recording media or storage media managed by app stores that distribute applications, sites that supply or distribute various software, servers, etc.

[0151] Although the embodiments described above have been described by way of limited examples and drawings, those skilled in the art will appreciate that various modifications and variations can be made based on the above teachings. For example, appropriate results can still be achieved even if the described techniques are performed in a different order than described, and / or components of the described systems, structures, devices, circuits, etc. are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents.

[0152] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims described below.

Claims

1. A method for determining a flight path and wireless resource allocation of a drone in a system comprising a plurality of device pairs, wherein a source device and a destination device included in the device pair perform OFDMA-based communication through a relay of a drone, and an electronic device in a system comprising the drone, wherein a plurality of device pairs, wherein a source device and a destination device included in the device pair perform OFDMA-based communication through a relay of a drone, and an electronic device in a system comprising the drone, A step of setting requirements - including a minimum required end-to-end throughput condition of each of the plurality of device pairs; and A method comprising the step of determining a flight path and wireless resource allocation of the unmanned aerial vehicle to maximize the sum of end-to-end throughputs of the plurality of device pairs while satisfying the above-described set of requirements.

2. In paragraph 1, The wireless resources of the above system are configured based on N (a natural number greater than or equal to 2) time slots, each of which includes an uplink subslot and a downlink subslot, and K (a natural number greater than or equal to 2) subchannels, each of which includes at least one subcarrier. A method according to claim 1, wherein the step of determining the allocation of the wireless resources comprises the steps of allocating uplink wireless resources - consisting of a combination of the N uplink subslots and the K subchannels - to transmission of a source device included in the device pairs, and allocating downlink wireless resources - consisting of a combination of the N downlink subslots and the K subchannels - to reception of a destination device included in the device pairs.

3. In the second paragraph, each of the K subchannels A method consisting of a preset number of subcarriers.

4. In paragraph 1, the determining step comprises: An offline step of determining the flight path and wireless resource allocation of the unmanned aerial vehicle by approximating the end-to-end throughput of each of the plurality of device pairs to an expected end-to-end throughput; and A method comprising an online step of performing flight and radio resource allocation according to the flight path and radio resource allocation determined in the offline step, and updating the radio resource allocation based on the instantaneous channel state information of each link, thereby performing the updated radio resource allocation.

5. In the fourth paragraph, the offline step A method comprising a step of calculating the expected end-to-end throughput based on a probabilistic line of sight channel model.

6. In paragraph 4, the offline step is A method comprising a step of determining a flight path and wireless resource allocation of the unmanned aerial vehicle by approximating the expected end-to-end throughput to the end-to-end throughput when each link is in a line-of-sight channel state.

7. In the fourth paragraph, the offline step A step of dividing into a first subproblem of allocating subchannels when a flight path is given and a second subproblem of optimizing the flight path when radio resource allocation is given; A step of obtaining a solution to the first subproblem and a solution to the second subproblem using a block coordinate descent (BCD) technique; and A method comprising a step of determining a flight path and radio resource allocation of the unmanned aerial vehicle based on the solution of the first sub-problem obtained above and the solution of the second sub-problem obtained above.

8. In paragraph 4, the online step A method comprising the step of allocating wireless resources for at least some of the plurality of device pairs for the current time slot based on wireless resource allocations executed up to the current time slot, channel state information of each link in the current time slot, and wireless resource allocations determined in the offline step.

9. In paragraph 4, the online step A method comprising the step of determining a radio resource allocation for a current time slot that maximizes the sum of the online expected end-to-end throughput for each device pair, the sum of the radio resource allocations executed up to the current time slot, the radio resource allocations for the current time slot, and the radio resource allocations after the current time slot determined in the offline step, while satisfying preset QoS requirements.

10. In paragraph 9, the online step is: If the QoS requirement is not met in the current time slot, a step of modifying the QoS requirement; and A method comprising the step of determining a wireless resource allocation for a current time slot that maximizes the sum of the online expected end-to-end throughput for each pair of devices while satisfying the above modified QoS requirements.

11. In paragraph 1, The above decision step is the following mathematical formula (Here, and are the indexes of the source devices, respectively. represents a set of, represents a destination device corresponding to the source device, and the system operates based on time slots obtained by dividing the unit operating time (T) of the drone into N, each of the time slots has a time length δ and is composed of an uplink subslot and a downlink subslot, and n is an index of a time slot and has a natural number value from 1 to N, represents the set of n, and represents the end-to-end throughput of the device pair in time slot n and the minimum average required throughput of the device pair, is the position of the drone at time slot n, corresponds to time slots 1 to N. represents a set of (i.e. flight paths), and represent the initial and final positions of the drone, respectively. represents the maximum speed of the above drone, represents the height of the drone at time slot n, and represent the minimum and maximum heights at which the drone can be positioned, respectively, and k is the index of the subchannel in the OFDMA technique and has a natural number value from 1 to K. is a value indicating whether a radio resource identified by time slot n and subchannel k has been allocated for data transmission from the source device s to the drone. is the set of values ​​that n and k can have. represents a set of, is a destination device from the above drone A value indicating whether a radio resource identified by time slot n and subchannel k has been allocated for data transmission. is the set of values ​​that n and k can have. A step of determining the flight path and radio resource allocation of the unmanned aerial vehicle based on the solution of the problem expressed as a set of (representing a set of), The above requirement is a method including (1a), (1b), (1c), (1d), (1e), (1f), (1g) in the above mathematical formula.

12. In paragraph 1, the determining step is An offline step of determining the flight path and wireless resource allocation of the unmanned aerial vehicle to maximize the sum of the end-to-end throughputs of the plurality of device pairs while satisfying the set requirements by utilizing statistical characteristics; and A step of checking whether preset QoS requirements are met in the current time slot; If it is determined that the QoS requirement cannot be met in the current time slot, a step of modifying the QoS requirement; and A method comprising the step of adjusting the wireless resource allocation for the current time slot determined in the offline step based on the instantaneous channel state information according to the above modified QoS requirements.

13. In paragraph 1, The above electronic device is a method of the above unmanned aerial vehicle.

14. In paragraph 1, The above electronic device is a device physically separated from the above public base station, The method further comprises the step of providing information on the determined flight path and radio resource allocation to the unmanned aerial vehicle.

15. In a system including multiple device pairs - a source device and a destination device included in the device pairs perform OFDMA-based communication through a relay of the drone - and a method for operating the drone, A step of setting the flight path and radio resource allocation of the above unmanned aerial vehicle; and A method for performing a flight and the relay according to the above-described set flight path and radio resource allocation, comprising a step of updating the radio resource allocation based on the set radio resource allocation and instantaneous channel state information of the link between each of the plurality of user device pairs and the unmanned aerial vehicle.

16. In paragraph 15, the setting step is: A method comprising a step of determining a flight path and wireless resource allocation of the unmanned aerial vehicle by approximating the end-to-end throughput of each of the plurality of device pairs to an expected end-to-end throughput.

17. In the 15th paragraph, the step of updating the wireless resource allocation is A step of checking whether preset QoS requirements are met in the current time slot; If it is determined that the above QoS requirements cannot be met, a step of modifying the set QoS requirements; and A method comprising the step of adjusting the wireless resource allocation for the current time slot determined in the offline step based on the instantaneous channel state information according to the above modified QoS requirements.

18. A system comprising a plurality of device pairs, wherein a source device and a destination device included in the device pair perform OFDMA-based communication through a relay of an unmanned aerial vehicle, and an electronic device for determining a flight path and radio resource allocation of the unmanned aerial vehicle, processor; and A computer-readable storage medium comprising instructions, An electronic device, wherein the command, in response to execution by the processor, causes the electronic device to perform the method described in any one of claims 1 to 12.

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