Resource allocation method, device and equipment for wireless power supply of unmanned aerial vehicle

Abstract

The present invention provides a resource allocation method, device and equipment for wireless power supply of unmanned aerial vehicles, and relates to the technical field of unmanned aerial vehicles. The method comprises: collecting parameter information of a D2D transmitter and a D2D network position in a mission scenario; based on the parameter information, respectively constructing a transmitter energy collection model and an information transmission and leakage model of a communication network; based on the transmitter energy collection model and the information transmission and leakage model of the communication network, obtaining an optimization model of the transmitter energy collection time ratio and the transmission power; solving the optimization model of the transmitter energy collection time ratio and the transmission power, and obtaining an optimal transmission power solution for the D2D transmitter, so as to reduce the technical problem of the risk of communication content being leaked in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a resource allocation method, device and equipment for wireless power supply of unmanned aerial vehicles. Background Art

[0002] With the development of the Internet of Things and wireless communication technologies, drone-assisted wireless communication networks have been widely used in many fields. These networks use drones as relay nodes to provide wireless communication services for ground equipment. However, in practical applications, drones face the problem of limited energy and need to be frequently charged or replaced with batteries, which limits their use time and convenience. At the same time, in some application scenarios, it is necessary to protect the communication content from being eavesdropped or interfered with to ensure the security and privacy of the data. Summary of the invention

[0003] The purpose of the present invention is to provide a resource allocation method, device and equipment for wireless power supply of unmanned aerial vehicles, so as to reduce the technical problem of the risk of communication content leakage existing in the prior art.

[0004] In a first aspect, the present invention provides a method for allocating resources for wireless power supply of an unmanned aerial vehicle, comprising:

[0005] Collect parameter information of D2D transmitter and D2D network location in the mission scenario;

[0006] Based on the parameter information, a transmitter energy collection model and an information transmission and leakage model of the communication network are respectively constructed;

[0007] Based on the transmitter energy collection model and the information transmission and leakage model of the communication network, an optimization model of the transmitter energy collection time ratio and the transmission power is obtained;

[0008] The optimization model of the energy collection time ratio and transmission power of the transmitter is solved to obtain the optimal transmission power solution of the D2D transmitter.

[0009] In an optional embodiment, the parameter information includes: energy collection time proportion and transmission power of the D2D transmitter, as well as D2D network location information and small-scale fading channel power gain data.

[0010] In an optional embodiment, based on the parameter information, a transmitter energy collection model and an information transmission and leakage model of the communication network are respectively constructed, including:

[0011] Based on the energy collection time ratio and transmission power of the D2D transmitter, a transmitter energy collection model is constructed, and the model is:

[0012]

[0013] In the formula, represents the information transmission energy consumption of the i-th D2D transmitter, τ represents the energy collection time proportion of all D2D transmitters, P i represents the information transmission power of the i-th D2D transmitter;

[0014]

[0015] In the formula, represents the energy collected by the i-th D2D transmitter from the UAV, η represents the energy collection efficiency of all D2D transmitters, P u Energy transmission power of drone, g i represents the energy harvesting link channel gain of the i-th D2D transmitter.

[0016] In an optional embodiment, based on the parameter information, a transmitter energy collection model and an information transmission and leakage model of the communication network are respectively constructed, including:

[0017] Based on the D2D network location information and small-scale fading channel power gain data, an information transmission and leakage model of the communication network is constructed. The model is:

[0018] f i (τ,P|x E ,y E )=(1-τ)Wlog2(1+γ i )

[0019] In the formula, f i represents the information rate of the i-th D2D transmission pair, and P represents the vector composed of the information transmission power of all D2D transmitters, that is, represents the location of the eavesdropper, W represents the bandwidth, γ i represents the signal-to-interference-noise ratio of the i-th D2D transmission pair;

[0020] g i (τ,P|x E ,y E )=(1-τ)Wlog2(1+γ ie )

[0021] In the formula, g i represents the information rate between the i-th D2D transmitter and the eavesdropper, γ ie represents the signal-to-interference-noise ratio between the i-th D2D transmitter and the eavesdropper.

[0022] In an optional embodiment, based on the transmitter energy collection model and the information transmission and leakage model of the communication network, an optimization model of the transmitter energy collection time ratio and the transmission power is obtained, including:

[0023] With the goal of maximizing the expected confidentiality energy efficiency, an optimization model is established with the energy collection time ratio and transmission power of the D2D transmitter as decision variables. The model is:

[0024]

[0025] In the formula, φ represents the expected confidentiality energy efficiency, V(τ,P) represents the total energy consumption, and l i (τ,P|x E ,y E ) represents the confidential information of the i-th D2D transmission pair, and E represents the expected operation.

[0026] In an optional embodiment, with the goal of maximizing the expected confidentiality energy efficiency, an optimization model is established with the energy collection time ratio and transmission power of the D2D transmitter as decision variables, including:

[0027] Using the following constraint rules, with the goal of maximizing the expected confidentiality energy efficiency, an optimization model for the transmitter energy collection time ratio and transmission power is constructed:

[0028] The value constraints of the decision variables of the D2D transmitter energy collection time ratio are:

[0029] 0≤τ≤1

[0030] QoS constraints for information transmission:

[0031] f i (τ,P|x E ,y E )≥r min

[0032] In the formula, r min Indicates the minimum threshold of information rate;

[0033] Energy sufficiency constraint for the i-th D2D transmitter:

[0034]

[0035] In an optional embodiment, solving the optimization model of the energy collection time ratio and the transmission power of the transmitter to obtain the optimal transmission power solution of the D2D transmitter includes:

[0036] The genetic algorithm is used to iteratively optimize the optimization model of the energy collection time ratio and the transmission power of the transmitter to obtain the optimal transmission power solution of the D2D transmitter.

[0037] In a second aspect, an embodiment of the present application provides a resource allocation device for wireless power supply of a drone, characterized in that it includes:

[0038] A collection module, used to collect parameter information of D2D transmitters and D2D network locations in the mission scenario;

[0039] A construction module is provided to respectively construct a transmitter energy collection model and an information transmission and leakage model of a communication network based on the parameter information; and based on the transmitter energy collection model and the information transmission and leakage model of the communication network, an optimization model of transmitter energy collection time proportion and transmission power is constructed with the goal of maximizing the expected confidentiality energy efficiency;

[0040] The solution module is used to solve the optimization model of the energy collection time ratio and the transmission power of the transmitter to obtain the optimal transmission power solution of the D2D transmitter.

[0041] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0042] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to execute the method described in the first aspect above.

[0043] The present invention provides a resource allocation method, device and equipment for wireless power supply of unmanned aerial vehicles. The method, device and equipment can respectively construct a transmitter energy collection model and an information transmission and leakage model of a communication network based on the parameter information by collecting parameter information of a D2D transmitter and a D2D network position in a mission scenario, obtain an optimization model of transmitter energy collection time ratio and transmission power based on the transmitter energy collection model and the information transmission and leakage model of the communication network, solve the optimization model of transmitter energy collection time ratio and transmission power, and obtain an optimal transmission power solution for the D2D transmitter, so as to reduce the risk of information leakage when using an unmanned aerial vehicle to wirelessly power a D2D communication network. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1One of the flow charts of a method for allocating resources for wireless power supply of a drone provided by an embodiment of the present invention;

[0046] Figure 2 A second flow chart of a method for allocating resources for wireless power supply of a drone provided by an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of a task scenario provided by an embodiment of the present invention;

[0048] Figure 4 A schematic diagram of the optimal solution for energy collection time ratio and transmission power of a D2D transmitter provided in an embodiment of the present invention;

[0049] Figure 5 A schematic diagram of the structure of a resource allocation device for wireless power supply of a drone provided by an embodiment of the present invention;

[0050] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] The deployment of drone-assisted D2D networks is an application of energy collection. Drones act as capability providers to enable energy-constrained D2D devices to continue working. Current research on drone energy collection networks has not considered the information leakage problem of drone-assisted D2D networks. Based on this, an embodiment of the present invention provides a resource allocation method, device, and equipment for wireless power supply of drones. Based on the parameter information of the D2D transmitter and the D2D communication network, an optimization model of the energy collection time ratio and transmission power of the D2D transmitter is constructed with the goal of maximizing the expected confidentiality energy efficiency. The optimization model of the energy collection time ratio and transmission power of the D2D transmitter is solved to obtain the optimal transmission power solution of the D2D transmitter. Therefore, the embodiment of the present invention can reduce the risk of information leakage when using drones to wirelessly power D2D communication networks.

[0053] To facilitate understanding of this embodiment, a resource allocation method for wireless power supply of a drone disclosed in an embodiment of the present invention is first introduced in detail.

[0054] The embodiment of the present invention provides a method for allocating resources for wireless power supply of a drone, which can be executed by a control device of a resource allocation system for wireless power supply of a drone. Figure 1 The flowchart of a method for allocating resources for wireless power supply of a drone is shown, and the method mainly includes the following steps S110 to S140:

[0055] like Figure 2 As shown, step S110, collecting parameter information of the D2D transmitter and the D2D network position in the task scenario.

[0056] The parameter information of the D2D transmitter and D2D network location in the mission scenario is collected through the energy collection device and communication device carried by the drone.

[0057] Among them, the D2D transmitter is used for data transmission and energy collection. The transmitter has energy collection time proportion data and transmission power data. The energy collection time proportion data represents the time proportion of the D2D transmitter used for energy collection within a specific time, and the transmission power data represents the transmission power of the D2D transmitter.

[0058] The D2D network location is used to determine the location information and small-scale fading channel power gain data of the D2D transmitter, where the location information represents the geographical location coordinates of the D2D transmitter; the small-scale fading channel power gain data represents the small-scale fading characteristics of the wireless communication link between the D2D transmitter and the drone.

[0059] The parameter information of the D2D transmitter and D2D network location in the mission scenario is collected through the energy collection device and communication device carried by the drone.

[0060] Step S120: Based on the parameter information, construct a transmitter energy collection model and a communication network information transmission and leakage model respectively.

[0061] In step S120, a transmitter energy collection model is constructed based on the energy collection time ratio and transmission power of the D2D transmitter;

[0062] Based on the D2D network location information and small-scale fading channel power gain data, an information transmission and leakage model of the communication network is constructed.

[0063] In some possible embodiments, based on the energy collection time ratio and transmission power of the D2D transmitter, a transmitter energy collection model is constructed, which can be specifically carried out through the following steps:

[0064] Step 1. Number each pair of D2D transmitters and receivers to form a D2D transmission pair set:

[0065] Ω={1,…,i,…N}

[0066] Where, Ω represents the set of N D2D transmission pairs;

[0067] In this application, N=3, and the D2D device consists of Figure 3 As shown in the circle in the figure, the circle indicates the serial number of the D2D transmission pair, and each pair of D2D transmission pairs is connected by a solid arrow, which points from the D2D transmitter to the D2D receiver;

[0068] Step 2: Define the information transmission power of the i-th D2D transmitter in N D2D transmission pairs as P i ;

[0069] Step 3. Define the energy harvesting link channel gain of the i-th D2D transmitter in N D2D transmission pairs as g i ;

[0070] In this application, i =1, Figure 3 The triangle in the figure represents a UAV, and the dotted arrow pointing from the triangle to the circle represents the energy transmission from the UAV to the D2D transmitter;

[0071] Step 4. The energy collection time proportion of all D2D transmitters is τ;

[0072] Step 5. The energy collection efficiency of all D2D transmitters is η; in this application, η = 0.8;

[0073] Step 6. The energy transmission power of the drone is P u ; In this application, P u =5;

[0074] Step 7. Calculate the information transmission energy consumption of the i-th D2D transmitter:

[0075]

[0076] Step 8. Calculate the energy collected by the i-th D2D transmitter from the drone:

[0077]

[0078] In some possible embodiments, based on the D2D network location information and the small-scale fading channel power gain data, an information transmission and leakage model of the communication network is constructed, wherein the information rate between the D2D transmission pairs and the information rate between the D2D transmission pairs and the eavesdropper are calculated according to the locations of the D2D transmitter and receiver and the location of the eavesdropper and the small-scale fading channel power gain data, and the specific process is as follows:

[0079] 1. The position of the i-th D2D transmitter is

[0080] In this application, and

[0081] 2. The location of the i-th D2D receiver is

[0082] In this application, and

[0083] 3. The location of the bug is (x E ,y E );

[0084] Since the location of the eavesdropper is unknown under normal circumstances, (x E ,y E ) is a two-dimensional random vector. In this application, Figure 3 The squares in the figure represent eavesdroppers, and the locations of the eavesdroppers are randomly and uniformly distributed in a circular area with (0,0) as the center and a radius of 90 dam.

[0085] 4. The position of the drone is (x U ,y U ,z U ); In this application, (x U ,y U ,z U )=(0,0,15);

[0086] 5. Calculate the Euclidean distance between the i-th D2D transmitter and the j-th D2D receiver:

[0087]

[0088] 6. Calculate the Euclidean distance between the i-th D2D transmitter and the eavesdropper:

[0089]

[0090] 7. The information channel power gain at the reference distance is β; in this application, β = -30;

[0091] 8. The power gain of the i-th D2D transmitter in the small-scale fading channel is f i 2 ; In this application, f i 2 =1;

[0092] 9. The path loss index is α; in this application, α=3;

[0093] 10. Calculate the information channel power gain h between the i-th D2D transmitter and the j-th D2D receiver ij :

[0094]

[0095] 11. Calculate the information channel power gain h between the i-th D2D transmitter and the eavesdropper ie :

[0096]

[0097] 12. The variance of additive Gaussian white noise is σ 2 ; In this application, σ 2 =0.1;

[0098] 13. Calculate the signal-to-interference-to-noise ratio γ of the i-th D2D transmission pair i :

[0099]

[0100] 14. Calculate the signal-to-interference-to-noise ratio γ between the i-th D2D transmitter and the eavesdropper ie :

[0101]

[0102] 15. The bandwidth is W; in this application, W=1;

[0103] 16. Calculate the information rate f of the i-th D2D transmission pair i :

[0104] f i (τ,P|x E ,y E )=(1-τ)Wlog2(1+γ i )

[0105] Where P is the vector of information transmission power of all D2D transmitters, that is

[0106] 17. Calculate the information rate g between the i-th D2D transmitter and the eavesdropper i :

[0107] g i (τ,P|x E ,y E )=(1-τ)Wlog2(1+γ ie )

[0108] Step S130: Based on the transmitter energy collection model and the information transmission and leakage model of the communication network, an optimization model of the transmitter energy collection time ratio and the transmission power is obtained.

[0109] In order to reduce the risk of information leakage when using drones to wirelessly power the D2D communication network, the optimization model is established with the energy collection time ratio and transmission power of the D2D transmitter as decision variables, with the goal of maximizing the expected confidentiality energy efficiency. The establishment process is as follows:

[0110] 11. Constraints on the value of the decision variable for the energy collection time ratio of the D2D transmitter:

[0111] 0≤τ≤1

[0112] 12. QoS (Quality of Service) constraints for information transmission:

[0113] f i (τ,P|x E ,y E )≥r min

[0114] In the formula, r min represents the minimum threshold of information rate. In this application, r min =0.01;

[0115] 13. Energy sufficiency constraint for the i-th D2D transmitter:

[0116]

[0117] 14. Calculate the confidential information of the i-th D2D transmission pair:

[0118] l i (τ,P|x E ,y E )=max{f i (τ,P|x E ,y E )-g i (τ,P|x E ,y E ),0}

[0119] 15. Calculate total energy consumption:

[0120]

[0121] Where P cir In this application, P cir =3;

[0122] 16. Set the objective function to maximize the expected confidentiality energy efficiency:

[0123]

[0124] Where φ represents the expected confidentiality efficiency.

[0125] Step S140: Solve the optimization model of the energy collection time ratio and the transmission power of the transmitter to obtain the optimal transmission power solution of the D2D transmitter.

[0126] Since the optimization model of the transmitter energy collection time ratio and transmission power belongs to a nonlinear non-convex programming model, a genetic algorithm is used in this application to solve the optimization model of the transmitter energy collection time ratio and transmission power to obtain the optimal transmission power solution of the D2D transmitter.

[0127] The genetic algorithm solution process can gradually approach the optimal solution through iterative search, and finally obtain the optimal solution for the transmission power of the D2D transmitter. The solution process depends on the setting of parameters such as the initial population, fitness function, selection operation, crossover operation and mutation operation. The specific process is as follows:

[0128] The first step is initialization: using real number coding, the energy collection time ratio and transmission power of the D2D transmitter are expressed in binary coding form, and the solution space of the problem is mapped to the binary coding space;

[0129] The second step is to generate the initial population: randomly generate 100 feasible solutions as the initial population. These solutions should meet the constraints of energy collection and transmission power to ensure the feasibility of the population.

[0130] The third step is selection operation: using the exponential sorting selection method, select individuals in the current population for replication according to the size of the fitness value. Individuals with high fitness have a greater probability of being selected, thereby retaining excellent genes and gradually evolving towards the optimal solution. In this application, the expected maximum confidentiality energy efficiency is used as the fitness value;

[0131] The fourth step is crossover operation: randomly pair the individuals generated by the selection-copy operation, perform crossover operation according to a certain crossover probability, and generate new individuals;

[0132] Step 5: Mutation operation: For each individual generated by the crossover operation, a mutation operation is performed with a mutation probability of 0.05 to prevent the algorithm from falling into a local optimal solution;

[0133] Step 6, iterative process: repeat the above selection, crossover, and mutation operations until the set number of iterations is reached. In each generation, the individuals in the population are updated according to the fitness value. In this application, the iteration is up to 30 generations;

[0134] Step 7: Output the optimal solution: Output the solution with the highest fitness. After decoding, the optimal solution of the energy collection time ratio and transmission power of the D2D transmitter is obtained. The optimal solution obtained by genetic algorithm search can meet the QoS constraints.

[0135] In this example, the genetic algorithm is used to solve the optimization model of the transmitter energy collection time ratio and transmission power. The maximum value of the expected confidentiality energy efficiency is 0.046, the optimal energy collection time ratio of the D2D transmitter is 0.37, and the following can be solved: Figure 4 The optimal transmission power solution is shown, where the number next to the solid arrow represents the optimal transmission power of the D2D transmitter.

[0136] The embodiments of the present application are aimed at the scenario where a D2D communication network is wirelessly powered by a drone and there is a risk of information leakage. An energy collection model and an information transmission and leakage model of the D2D communication network are established using a D2D transmitter, and an optimization model of the energy collection time ratio and transmission power of the D2D transmitter is established with the goal of maximizing the expected confidentiality energy efficiency. The optimal energy collection time ratio and transmission power of the D2D transmitter are solved to maximize the expected confidentiality energy efficiency, thereby reducing the risk of information leakage when a drone is used to wirelessly power a D2D communication network.

[0137] Corresponding to the above-mentioned resource allocation method for wireless power supply of a drone, an embodiment of the present invention further provides a resource allocation device for wireless power supply of a drone. Figure 5 The schematic diagram of the structure of a resource allocation device for wireless power supply of a drone is shown, and the device comprises:

[0138] A collection module 510, used to collect parameter information of D2D transmitters and D2D network locations in a mission scenario;

[0139] A construction module 520 constructs a transmitter energy collection model and an information transmission and leakage model of a communication network, respectively, based on the parameter information; and based on the transmitter energy collection model and the information transmission and leakage model of the communication network, constructs an optimization model of transmitter energy collection time proportion and transmission power with the goal of maximizing the expected confidentiality energy efficiency;

[0140] The solution module 530 is used to solve the optimization model of the energy collection time ratio and the transmission power of the transmitter to obtain the optimal transmission power solution of the D2D transmitter.

[0141] Furthermore, the acquisition module 510 is used to:

[0142] The energy collection time ratio and transmission power of the D2D transmitter in the collection mission scenario are collected, as well as the D2D network location information and small-scale fading channel power gain data.

[0143] Furthermore, the building block 520 is used to:

[0144] Based on the energy collection time ratio and transmission power of the D2D transmitter, a transmitter energy collection model is constructed, and the model is:

[0145]

[0146] In the formula, represents the information transmission energy consumption of the i-th D2D transmitter, τ represents the energy collection time proportion of all D2D transmitters, P i represents the information transmission power of the i-th D2D transmitter;

[0147]

[0148] In the formula, represents the energy collected by the i-th D2D transmitter from the UAV, η represents the energy collection efficiency of all D2D transmitters, P u The energy transmission power of the UAV, g1 represents the energy harvesting link channel gain of the i-th D2D transmitter.

[0149] Furthermore, the building block 520 is used to:

[0150] Based on the D2D network location information and small-scale fading channel power gain data, an information transmission and leakage model of the communication network is constructed. The model is:

[0151] Based on the D2D network location information and small-scale fading channel power gain data, an information transmission and leakage model of the communication network is constructed. The model is:

[0152] f i (τ,P|x E ,y E )=(1-τ)Wlog2(1+γ i )

[0153] In the formula, f i represents the information rate of the i-th D2D transmission pair, and P represents the vector composed of the information transmission power of all D2D transmitters, that is, (x E ,y E ) represents the location of the eavesdropper, W represents the bandwidth, γ i represents the signal-to-interference-noise ratio of the i-th D2D transmission pair;

[0154] g i (τ,P|x E ,y E )=(1-τ)Wlog2(1+γ ie )

[0155] In the formula, g irepresents the information rate between the i-th D2D transmitter and the eavesdropper, γ ie represents the signal-to-interference-noise ratio between the i-th D2D transmitter and the eavesdropper.

[0156] Furthermore, the building block 520 is used to:

[0157] With the goal of maximizing the expected confidentiality energy efficiency, an optimization model is established with the energy collection time ratio and transmission power of the D2D transmitter as decision variables. The model is:

[0158]

[0159] In the formula, φ represents the expected confidentiality energy efficiency, V(τ,P) represents the total energy consumption, and l i (τ,P|x E ,y E ) represents the confidential information of the i-th D2D transmission pair, and E represents the expected operation.

[0160] Furthermore, the building block 520 is used to:

[0161] Using the following constraint rules, with the goal of maximizing the expected confidentiality energy efficiency, an optimization model for the transmitter energy collection time ratio and transmission power is constructed:

[0162] The value constraints of the decision variables of the D2D transmitter energy collection time ratio are:

[0163] 0≤τ≤1

[0164] QoS constraints for information transmission:

[0165] f i (τ,P|x E ,y E )≥r min

[0166] In the formula, r min Indicates the minimum threshold of information rate;

[0167] Energy sufficiency constraint for the i-th D2D transmitter:

[0168]

[0169] Furthermore, the solution module 530 is used to:

[0170] The genetic algorithm is used to iteratively search and calculate the optimization model of the energy collection time ratio and the transmission power of the transmitter to obtain the optimal transmission power solution of the D2D transmitter.

[0171] The implementation principle and technical effects of a resource allocation device for wireless power supply for unmanned aerial vehicles provided in this embodiment are the same as those of the aforementioned resource allocation method for wireless power supply for unmanned aerial vehicles. For the sake of brief description, for parts not mentioned in the embodiment of a resource allocation device for wireless power supply for unmanned aerial vehicles, reference may be made to the corresponding contents in the aforementioned embodiment of a resource allocation method for wireless power supply for unmanned aerial vehicles.

[0172] like Figure 6 As shown, Figure 6 As shown, an electronic device 600 provided in an embodiment of the present application includes: a processor 601, a memory 602 and a bus, the memory 602 stores machine-readable instructions executable by the processor 601, and when the electronic device is running, the processor 601 communicates with the memory 602 through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the resource allocation method for wireless power supply of a drone as described above.

[0173] Specifically, the above-mentioned memory 602 and processor 601 can be general-purpose memories and processors, which are not specifically limited here. When the processor 601 runs the computer program stored in the memory 602, it can execute the above-mentioned resource allocation method for wireless power supply of a drone.

[0174] The processor 601 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 601. The above processor 601 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 602, and the processor 601 reads the information in the memory 602 and completes the steps of the above method in combination with its hardware.

[0175] Corresponding to the above-mentioned resource allocation method for wireless power supply of an unmanned aerial vehicle, an embodiment of the present application also provides a computer-readable storage medium, which stores machine-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to execute the steps of the above-mentioned resource allocation method for wireless power supply of an unmanned aerial vehicle.

[0176] A resource allocation device for wireless power supply of a drone provided in an embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in the embodiment of the present application are the same as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding contents in the aforementioned method embodiment. Technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0177] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0178] For another example, the flowchart and block diagram in the accompanying drawings show the possible architecture, functions and operations of the device, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and a part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0179] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0180] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0181] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for an electronic device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the vehicle marking method of each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0182] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0183] Finally, it should be noted that the above embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application. They should all be covered within the protection scope of the present application.

Claims

1. A resource allocation method for wireless power supply of unmanned aerial vehicles, characterized in that: include: Collect parameter information of the D2D transmitter and D2D network location in the mission scenario, wherein the parameter information includes: the energy collection time proportion and transmission power of the D2D transmitter, as well as the D2D network location information and small-scale fading channel power gain data; Based on the parameter information, a transmitter energy collection model and an information transmission and leakage model of the communication network are respectively constructed, wherein the transmitter energy collection model is constructed based on the D2D transmitter energy collection time ratio and transmission power, and the transmitter energy collection model is: In the formula, represents the information transmission energy consumption of the i-th D2D transmitter, represents the energy collection time proportion of all D2D transmitters, represents the information transmission power of the i-th D2D transmitter; In the formula, represents the energy collected by the i-th D2D transmitter from the UAV, represents the energy harvesting efficiency of all D2D transmitters, Energy transmission power of drones, represents the energy harvesting link channel gain of the i-th D2D transmitter; Based on the D2D network location information and the small-scale fading channel power gain data, an information transmission and leakage model of the communication network is constructed. The information transmission and leakage model of the communication network is: In the formula, represents the information rate of the i-th D2D transmission pair, and P represents the vector composed of the information transmission power of all D2D transmitters, that is, , N represents the number of D2D transmission pairs; ( ) represents the location of the eavesdropper, W represents the bandwidth, represents the signal-to-interference-noise ratio of the i-th D2D transmission pair; In the formula, represents the information rate between the i-th D2D transmitter and the eavesdropper, represents the signal-to-interference-noise ratio between the i-th D2D transmitter and the eavesdropper; Based on the transmitter energy collection model and the information transmission and leakage model of the communication network, an optimization model of the transmitter energy collection time ratio and transmission power is obtained, wherein the optimization model is established with the D2D transmitter energy collection time ratio and transmission power as decision variables with the goal of maximizing the expected confidentiality energy efficiency, and the model is: In the formula, Expressing the expectation of confidentiality efficiency, represents the total energy consumption, represents the confidential information of the i-th D2D transmission pair, and E represents the expected operation; Using the following constraint rules, with the goal of maximizing the expected confidentiality energy efficiency, an optimization model for the transmitter energy collection time ratio and transmission power is constructed: The value constraints of the decision variables of the D2D transmitter energy collection time ratio are: QoS constraints for information transmission: In the formula, Indicates the minimum threshold of information rate; is the energy sufficiency constraint of the i-th D2D transmitter; The optimization model of the energy collection time ratio and transmission power of the transmitter is solved to obtain the optimal transmission power solution of the D2D transmitter.

2. The resource allocation method for wireless power supply of unmanned aerial vehicle according to claim 1, characterized in that: The optimization model of the energy collection time ratio and transmission power of the transmitter is solved to obtain the optimal transmission power solution of the D2D transmitter, including: The genetic algorithm is used to iteratively optimize the optimization model of the energy collection time ratio and the transmission power of the transmitter to obtain the optimal transmission power solution of the D2D transmitter.

3. A resource allocation device for wireless power supply of unmanned aerial vehicles, characterized in that: include: A collection module, used to collect parameter information of the D2D transmitter and the D2D network location in the mission scenario, wherein the parameter information includes: the energy collection time proportion and transmission power of the D2D transmitter, as well as the D2D network location information and small-scale fading channel power gain data; A construction module is used to construct a transmitter energy collection model and an information transmission and leakage model of a communication network based on the parameter information. The transmitter energy collection model is constructed based on the energy collection time ratio and transmission power of the D2D transmitter. The transmitter energy collection model is: In the formula, represents the information transmission energy consumption of the i-th D2D transmitter, represents the energy collection time proportion of all D2D transmitters, represents the information transmission power of the i-th D2D transmitter; In the formula, represents the energy collected by the i-th D2D transmitter from the UAV, represents the energy harvesting efficiency of all D2D transmitters, Energy transmission power of drones, represents the energy harvesting link channel gain of the i-th D2D transmitter; Based on the D2D network location information and the small-scale fading channel power gain data, an information transmission and leakage model of the communication network is constructed. The information transmission and leakage model of the communication network is: In the formula, represents the information rate of the i-th D2D transmission pair, and P represents the vector composed of the information transmission power of all D2D transmitters, that is, , N represents the number of D2D transmission pairs; ( ) represents the location of the eavesdropper, W represents the bandwidth, represents the signal-to-interference-noise ratio of the i-th D2D transmission pair; In the formula, represents the information rate between the i-th D2D transmitter and the eavesdropper, represents the signal-to-interference-noise ratio between the i-th D2D transmitter and the eavesdropper; And based on the transmitter energy collection model and the information transmission and leakage model of the communication network, with the goal of maximizing the expected confidentiality energy efficiency, an optimization model of the transmitter energy collection time ratio and the transmission power is constructed, wherein, with the goal of maximizing the expected confidentiality energy efficiency, the optimization model is established with the D2D transmitter energy collection time ratio and the transmission power as decision variables, and the model is: In the formula, Expressing the expectation of confidentiality efficiency, represents the total energy consumption, represents the confidential information of the i-th D2D transmission pair, and E represents the expected operation; Using the following constraint rules, with the goal of maximizing the expected confidentiality energy efficiency, an optimization model for the transmitter energy collection time ratio and transmission power is constructed: The value constraints of the decision variables of the D2D transmitter energy collection time ratio are: QoS constraints for information transmission: In the formula, Indicates the minimum threshold of information rate; is the energy sufficiency constraint of the i-th D2D transmitter; The solution module is used to solve the optimization model of the energy collection time ratio and the transmission power of the transmitter to obtain the optimal transmission power solution of the D2D transmitter.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions cause the processor to execute the method according to any one of claims 1 to 2.

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