Unmanned aerial vehicle track design method and device in multi-user wireless power transmission network

Abstract

The invention discloses an unmanned aerial vehicle track design method and device in a multi-user wireless power transmission network, and the method comprises the steps: constructing a nonlinear energy collection model, obtaining the receiving power of a user, and calculating the average receiving total energy of all users; constructing a joint optimization model of the track and the transmitting power of the unmanned aerial vehicle by taking maximization of the average received total energy as a target; through a Lagrange duality method, the average receiving total energy is maximized under the condition that the unmanned aerial vehicle meets the energy budget and the power upper limit, and an optimal transmitting power control scheme is obtained; for a given Lagrange multiplier, solving an optimal unmanned aerial vehicle track under the corresponding Lagrange multiplier; and designing an optimal Lagrange multiplier, and solving the joint optimization model to obtain an optimal unmanned aerial vehicle trajectory and transmitting power scheme. According to the method, the average total energy collection of all users is maximized by jointly optimizing the transmitting power and trajectory design of the unmanned aerial vehicle, and the limitation of a nonlinear energy collection process and the energy budget of the unmanned aerial vehicle is considered.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and device for designing a trajectory of an unmanned aerial vehicle in a multi-user wireless power transmission network. Background Art

[0002] As a strategic emerging industry, the low-altitude economy has been formally written into the national development strategy. Unmanned and intelligent operations have become a trend. Among them, low-altitude production services supported by unmanned aerial vehicles (UAVs) are widely used in logistics distribution, agricultural plant protection, emergency rescue and other fields. Based on the advantages of high mobility and high controllability of drones, by optimizing the design of the continuous trajectory of drones, additional performance increment space can be obtained compared to ground communication networks. In addition, drones generally experience time-varying communication environments when flying in the air. Therefore, it is very beneficial to jointly optimize drone trajectories and resource allocation for different channel states.

[0003] It is worth noting that integrating drones into wireless power transmission networks has become a popular trend. Wireless power transmission networks are usually composed of a group of sensor nodes with limited battery capacity. Through wireless power transfer technology (Wireless Power Transfer, WPT), the life of network nodes can be effectively extended and the stability of the network can be maintained. By integrating WPT technology on drones and deploying drones as mobile power stations, the performance of drone-assisted WPT networks can be improved. However, for drone-assisted multi-user WPT networks, there is still a lack of effective design of the optimal continuous trajectory and transmission power of drones, especially considering the use of a nonlinear energy harvesting model to simulate real-world scenarios. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art, and to provide a method and device for designing UAV trajectories in a multi-user wireless power transmission network, which maximizes the average total collected energy of all ground users by jointly optimizing the UAV transmission power and trajectory design, and takes into account the limitations of the nonlinear energy collection process and the UAV energy budget.

[0005] To achieve the above objectives, the technical solution of the present invention is: a method for designing a trajectory of a UAV in a multi-user wireless power transmission network, comprising:

[0006] The drone starts from a given starting point and transmits energy to multiple users on the ground at the same time to obtain relevant channel parameters;

[0007] Construct a nonlinear energy harvesting model to obtain the received power at the user and calculate the average total received energy of all users;

[0008] With the goal of maximizing the average total received energy, a joint optimization model of UAV trajectory and transmission power is constructed with maximum speed constraint, start and end point constraint, maximum power constraint and total available energy constraint as constraints;

[0009] Through the Lagrangian duality method, the average received total energy is maximized when the UAV meets the energy budget and power limit, and the optimal transmission power control scheme is obtained;

[0010] For a given Lagrangian multiplier, solve the optimal UAV trajectory under the corresponding Lagrangian multiplier;

[0011] Design the optimal Lagrange multiplier and solve the joint optimization model to obtain the optimal UAV trajectory and transmission power solution.

[0012] The nonlinear energy harvesting model is:

[0013]

[0014] Where P ch，k (t) is the received power at user k at time t; is the RF received signal power P rf A nonlinear function of out (P rf，k (t)) is the output current on the diode; P rf，k (t) is the RF received signal power at user k at time t; R l is the load resistance; x(t) and y(t) are the horizontal positions of the drone at a height of H at time t; P(t) is the transmission power of the drone at time t; β0 is the channel gain when the reference distance is 1m; (w k，x , w k，y ) is the horizontal position of user k; H is the minimum flight altitude of the UAV.

[0015] The joint optimization model of the UAV trajectory and transmission power is:

[0016]

[0017] Where K is the total number of users; E sum，k ({x(t), y(t)}, {P(t)}) is the total received energy at user k at time t; (x(t), y(t)) is the first-order derivative of the horizontal position of the drone; V max is the maximum speed of the UAV; (x1, y1) and (x2, y2) are the horizontal positions of the starting point and the end point set for the UAV respectively; E is the energy budget.

[0018] The total received energy at user k at time t is:

[0019]

[0020] Where T is the UAV mission cycle.

[0021] The Lagrange duality method is used to maximize the average total received energy when the UAV meets the energy budget and power upper limit, and obtain the optimal transmission power control scheme, including:

[0022] Construct the following Lagrangian function:

[0023]

[0024] Where λ is the Lagrange multiplier;

[0025] Lagrange dual function L D (λ) is:

[0026] L D (λ)=min {x(t)y(t)，P(t)} L({x(t),y(t),P(t)},λ);

[0027] The optimal transmit power is solved according to the following formula:

[0028]

[0029] Define the objective function as The first-order derivative is:

[0030]

[0031] Where, d k (t) is the distance from the drone to user k at time t;

[0032] The point where the first-order derivative is zero is set to P critial ,satisfy:

[0033]

[0034] For a given Lagrange multiplier λ, the optimal transmit power is calculated as:

[0035]

[0036] In the formula, is the optimal transmission power; P max is the maximum transmission power of the drone.

[0037] For a given Lagrangian multiplier, solving the optimal UAV trajectory under the corresponding Lagrangian multiplier includes: using a mechanical equivalent method to equate the Lagrangian dual function to:

[0038]

[0039] In the formula, is the potential energy field; is the rope shape; ρ(s) is the rope density; ρ min is the minimum line density constraint; m is the rope mass; S is the total length of the rope;

[0040]

[0041] In the formula, and is the equivalent corresponding received power P ch，k (t) and the optimal transmission power P λ * (t);d k (x, y) is the distance from point (x, y) to the horizontal position of user k;

[0042] The negative gradient of the scalar potential function is used to describe the force field in the potential energy field:

[0043]

[0044] In the formula, g(x, y) is the force field in the potential energy field;

[0045] Under the optimal rope shape, from the starting point of the drone (x1, y1) to The resultant forces on the x and y axes are 0, and the expressions are:

[0046]

[0047] In the formula, is the optimal initial rope tension; α * is the optimal solution of the initial rope tension angle; is the component of the force field in the x-axis direction; is the component of the force field in the y-axis direction; The optimal rope shape; is the sum of the gravitational force and the projection of the initial rope tension in the x-axis direction; is the sum of the gravitational force and the projection of the initial rope tension in the y-axis direction; Q(s) is the internal tension of the rope;

[0048] Optimal rope shape for:

[0049]

[0050] In the formula, (w 1，x , w 1，y ) is the starting point of the UAV flight;

[0051] According to s=Vt, the optimal UAV trajectory {x*(t), y*(t)} is given by the optimal rope shape It is expressed as:

[0052]

[0053] Optimal Lagrange multiplier λ * The design method is:

[0054] When E>P max When T, λ * =0;

[0055] When E <P max At T, starting from the feasible interval of λ, the optimal transmission power P is repeatedly solved with λ as the midpoint. λ * (t), and in accordance with Repeat shortening the interval until The optimal Lagrange multiplier λ is obtained when * .

[0056] A device for designing a trajectory of a drone in a multi-user wireless power transmission network, the device being applied to the method described above, the device comprising:

[0057] The parameter acquisition module is used for the UAV to start from a given starting point and transmit energy to multiple users on the ground at the same time to obtain relevant channel parameters;

[0058] The average received total energy calculation module is used to build a nonlinear energy collection model, obtain the received power at the user, and calculate the average received total energy of all users;

[0059] A joint optimization model building module is used to build a joint optimization model of UAV trajectory and transmission power with the goal of maximizing the average total received energy and the maximum speed constraint, the start and end point constraint, the maximum power constraint and the total available energy constraint as constraints;

[0060] The optimal transmission power acquisition module is used to maximize the average received total energy and obtain the optimal transmission power control solution under the condition that the UAV meets the energy budget and power upper limit through the Lagrange duality method;

[0061] The optimal UAV trajectory acquisition module is used to solve the optimal UAV trajectory under the corresponding Lagrangian multiplier for a given Lagrangian multiplier;

[0062] The joint optimization model solving module is used to design the optimal Lagrange multiplier and solve the joint optimization model to obtain the optimal UAV trajectory and transmission power solution.

[0063] A device for designing a trajectory of a drone in a multi-user wireless power transmission network, comprising a memory and a processor;

[0064] The memory is used to store computer program code and transmit the computer program code to the processor;

[0065] The processor is used to execute the method according to the instructions in the computer program code.

[0066] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] In a method and device for designing the trajectory of a drone in a multi-user wireless power transmission network of the present invention, the average total collected energy of all ground users is maximized by jointly optimizing the drone transmission power and trajectory design, and the limitations of the nonlinear energy collection process and the drone energy budget are taken into account. First, based on the nonlinear energy collection model, the expression of the charging power received at the user is derived, and the expression of the average total received energy of all users is further derived. Secondly, through the dual method, the characteristics of the optimal transmission power control scheme are constructed, and the dual problem is transformed into a pure trajectory design problem. Finally, through the mechanical equivalence method, the principles of physics are cleverly used to solve the non-convex trajectory problem of drones with continuous infinite variables, and the closed-form expression of the optimal trajectory solution is analytically constructed. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 The present invention is a flow chart of a method for designing a trajectory of a UAV in a multi-user wireless power transmission network.

[0070] Figure 2 1 is a model diagram of a drone system of a multi-user wireless power transmission network in an embodiment of the present invention.

[0071] Figure 3 It is a flow chart of finding the optimal solution to the equivalent mechanical problem in an embodiment of the present invention.

[0072] Figure 4 It is a flow chart of obtaining the optimal initial rope tension through binary search in an embodiment of the present invention.

[0073] Figure 5 It is a structural block diagram of a UAV trajectory design device in a multi-user wireless power transmission network of the present invention.

[0074] Figure 6 It is a structural block diagram of a UAV trajectory design device in a multi-user wireless power transmission network of the present invention. DETAILED DESCRIPTION

[0075] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0076] See also Figure 1 , a UAV trajectory design method in a multi-user wireless power transmission network, comprising:

[0077] S1, the UAV starts from a given starting point and transmits energy to multiple users on the ground at the same time to obtain relevant channel parameters;

[0078] S2. Construct a nonlinear energy harvesting model to obtain the received power at the user and calculate the average total received energy of all users;

[0079] S3, with the goal of maximizing the average total received energy, and taking the maximum speed constraint, the starting and end point constraints, the maximum power constraint and the total available energy constraint as constraints, a joint optimization model of the UAV trajectory and the transmission power is constructed;

[0080] S4. Through the Lagrangian duality method, the average received total energy is maximized when the UAV meets the energy budget and power limit, and the optimal transmission power control scheme is obtained;

[0081] S5. For a given Lagrangian multiplier, solve the optimal UAV trajectory under the corresponding Lagrangian multiplier;

[0082] S6. Design the optimal Lagrange multiplier and solve the joint optimization model to obtain the optimal UAV trajectory and launch power solution.

[0083] The present invention utilizes the mobility of the UAV to jointly design the trajectory and power of the UAV to maximize the average total energy collected by the UAV in a multi-user wireless power transmission network. The UAV flies at a fixed altitude and is responsible for providing energy supply to multiple user nodes on the ground. The starting point and end point of the UAV are preset so that it can be charged at a designated landing location to prepare for subsequent energy supply tasks. The present invention adopts a nonlinear energy collection model to maximize the average energy collection of all user nodes in a fixed time by jointly optimizing the trajectory and transmission power of the UAV. However, the nonlinear energy collection function and the infinite variables in the continuous trajectory bring great challenges to the non-convex problem of joint design. To solve this problem, the present invention utilizes the convex properties in the energy collection model to construct a compact convex approximation for the reconstruction problem. Due to the limited energy of wireless transmission, the closed-form expression of the optimal transmission power of the UAV is obtained through dual analysis, and the dual joint optimization problem is transformed into the UAV trajectory design problem. The mechanical equivalent method is used to cleverly equivalent the pure trajectory design problem after variable dimension reduction to the variable density rope shape design problem, thereby constructing a closed-form solution of the optimal continuous trajectory with the help of mechanical mechanics principles. This method has low complexity and shows strong competitiveness in low-latency scenarios.

[0084] The present invention takes a multi-user wireless power transmission network as an example. Figure 2 As shown in the figure. The drone is the mobile transmitter, starting from a known starting point, and the ground user node is the receiver. The drone flies at a fixed altitude H>0, starting from the starting point, and simultaneously transmits energy to multiple users on the ground to reach a given destination. The acquired communication parameters include the maximum speed limit V of the drone flight. max , Maximum transmit power limit P max , the minimum flight altitude of the drone is H, the starting and ending position information of the drone are (x1, y1) and (x2, y2) respectively, and the total number of ground users is K, which are placed at (w k，x , w k，y ), k = 1, ..., K, are all located on the ground. The communication duration from the UAV to the ground user is T. Since the direct line of sight link between the UAV and the ground node is strong, the channel is modeled as an ideal free space model, and the channel power gain per unit distance is expressed as β0.

[0085] Furthermore, the present invention proposes to use a small signal model to characterize the nonlinearity of the rectifier (diode) during the DC conversion at the user. During the energy harvesting process, the RF signal transmitted by the drone is received by the ground node and converted into a DC signal for battery supply. The voltage V on the diode d and output current I out The relationship between can be modeled as:

[0086]

[0087] In the formula, I s is the reverse bias saturation current on the diode; n is the ideality factor; v t is the thermal voltage of the diode.

[0088] By Taylor expansion of the nonlinear current function of the voltage on the diode, the output current I can be obtained: out Relative to the received RF signal power P rf The nonlinear implicit function I out (P rf ), expressed as:

[0089]

[0090] In the formula, R l is the load resistance; n0 is the cutoff order, which indicates the accuracy of the simulation of the nonlinear current model; all factors α j are intermediate constants and can be obtained by Taylor expansion.

[0091] For any given RF signal power P rf , I out (P rf ) can be obtained accordingly. To further improve the calculation efficiency, with the help of the LambertW function properties, we can get I out (P rf ) is expressed as:

[0092]

[0093] Where W0(x) is the main branch of the Lambert W function, f(x) = xe x is the inverse function of . When x>0, W0(x) is a strictly increasing convex function, which is widely used in dealing with problems involving exponentials and logarithms. In addition, the charging power at the user satisfies P ch =(I out (P rf )) 2 R l , which means the charging power P at the user ch It is about the RF received signal power P rf The nonlinear function is expressed as Therefore, the received power P at user k at time t is ch，k (t) can be expressed as:

[0094]

[0095] Where P ch，k (t) is the received power at user k at time t; is the RF received signal power P rf A nonlinear function of out (P rf，k (t)) is the output current on the diode; P rf，k (t) is the RF received signal power at user k at time t; R l is the load resistance; x(t) and y(t) are the horizontal positions of the drone at a height of H at time t; P(t) is the transmission power of the drone at time t; β0 is the channel gain when the reference distance is 1m; (w k，x , w k，y ) is the horizontal position of user k; H is the minimum flight altitude of the UAV.

[0096] Convexity proof of nonlinear model: Although the received power P ch，k (t) cannot be expressed as an elementary function, but can be expressed as a nonlinear function The convexity of is designed accordingly. In particular, let and represents the horizontal distance from the current position of the drone to user k at time t, then we can get

[0097] If satisfied It can be proved that I out (P rf，k (u)) is a convex function with respect to u. Since P rf，k The first and second derivatives of (u) are expressed as follows:

[0098]

[0099] It can be further concluded that the following inequality satisfies the conditions:

[0100]

[0101] I out (P rf，k (u)) is a convex function with respect to u. Note that the output current of the diode must be non-negative, that is, I out >0 is always true. At the same time, the received power P ch，k (t) = P ch，k (u) is a convex function with respect to u, that is, about is a convex function. Further, we can get P ch，k (t) is about is a decreasing convex function.

[0102] Since the onboard battery capacity of the UAV is limited, the present invention considers that the UAV power P(t) at any time t satisfies the maximum transmission power constraint P max, that is, P(t)≤P max , t∈[0,T], where T is the pre-designed mission period of the UAV.

[0103] Similarly, the energy budget of the drone can also be pre-scheduled. Therefore, in the wireless power transmission network, the present invention considers that the total available energy for transmission is limited by E sum , that is, satisfy

[0104] In order to give full play to the collection performance of drones in wireless power transmission networks and improve the energy collection intensity of drones for multiple users on the ground, the present invention assumes that all ground users can receive unlimited collected energy during the entire drone mission cycle T. Therefore, the total received energy at user k at time t is:

[0105]

[0106] According to the above, P rf，k (t) is a function of the drone trajectory and power. The total received energy at user k at time t is also a function of the drone trajectory {x(t), y(t)} and the transmit power {P(t)}. By jointly optimizing the drone's continuous trajectory and transmit power, the average total received energy of all users within the mission period T is maximized, thereby further improving the performance of wireless power transmission, while satisfying the maximum speed constraint, start and end point constraints, maximum power constraint, and total available energy constraint. The joint optimization model of drone trajectory and transmit power is:

[0107]

[0108] Where K is the total number of users; E sum，k ({x(t), y(t)}, {(P(t)}) is the total received energy at user k at time t; is the first-order derivative of the horizontal position of the UAV; V max is the maximum speed of the UAV; (x1, y1) and (x2, y2) are the horizontal positions of the starting point and the end point set for the UAV respectively; E is the energy budget.

[0109] Furthermore, through the Lagrangian duality method, the average total received energy is maximized when the UAV meets the energy budget and power upper limit, and the optimal transmission power control scheme is obtained: In order to improve the efficiency of problem solving, the present invention uses the properties of constraints C4 and C5 in the joint optimization model (original problem (OP)) to decouple the problem, obtain the closed-form expression of the optimal transmission power, and convert the original problem into a pure trajectory design problem.

[0110] The original problem (OP) is characterized by the dual method. According to the constraint C4 and a Lagrangian multiplier λ≥0, the partial Lagrangian function of the original problem (OP) is constructed as follows:

[0111]

[0112] Where λ is the Lagrange multiplier;

[0113] Lagrange dual function L D (λ) is:

[0114]

[0115] The dual problem (DP) corresponding to the original problem (OP) is constructed as:

[0116]

[0117] For any given λ≥0 and drone trajectory {x(t), y(t)}, the optimal transmit power can be obtained as follows:

[0118]

[0119] In the above nonlinear energy harvesting model, P ch，i (t) is about The decreasing convex function, due to the symmetric nature of the concave and convex function, that is, -P ch，k (t) About In addition, λKP(t) is a linear increasing function of P(t), which is both a convex function and a concave function. In order to solve the above equation to find the optimal transmission power, it is necessary to solve the minimum value through the first-order derivative condition.

[0120] Define the objective function as The first-order derivative is:

[0121]

[0122] Where, d k (t) is the distance from the drone to user k at time t;

[0123] The point where the first-order derivative is zero is set to P critial , and the trajectory of the drone {x(t), y(t)} (or the distance d from the drone to each user k ) is related to the Lagrange multiplier λ, satisfying:

[0124]

[0125] Considering the boundary condition 0≤P(t)≤P max, for a given Lagrange multiplier λ, the optimal transmit power is calculated as:

[0126]

[0127] Where P λ * (t) is the optimal transmission power; P max is the maximum transmission power of the drone.

[0128] The present invention characterizes the original problem by a dual method and obtains the corresponding Lagrangian dual function. ch，k (t) and the derivative of the UAV transmission power The convexity of is taken into account, and a simplified optimization problem is solved. The optimal power is expressed as a function of the Lagrange multiplier λ ≥ 0 and the trajectory of the UAV {x(t), y(t)}, thereby obtaining the optimal power control scheme.

[0129] Furthermore, according to the optimal power control scheme, a new dual function can be obtained, which is a pure UAV trajectory design problem. The pure trajectory design problem can be solved smoothly by the mechanical equivalent method. The solution process is as follows: Figure 3 As shown, the optimal initial rope tension corresponding to the optimal solution of rope shape can be solved by binary search. The solution process is as follows Figure 4 shown.

[0130] For any given λ and drone trajectory {x(t), y(t)}, combining the Lagrangian dual function and the optimal transmit power yields the following new dual function:

[0131]

[0132] The new dual function becomes a pure UAV trajectory design problem. In the dual method, the pure trajectory design problem is optimally solved for any given λ, and the dual problem (DP) can be optimally solved. Due to the strong duality between the dual problem (DP) and the primal problem (OP), when λ = λ * When , the problem in the new dual function will produce the same optimal UAV trajectory as in the original problem (OP) and formulate the optimal transmission power control scheme.

[0133] For any given λ, -λE is always a constant. Therefore, the -λE term can be deleted in the optimization process for the dual problem, and the dual problem can be further equivalent to a mechanical problem (MP), which cleverly transforms the continuous trajectory design problem of the UAV into the variable density rope shape design.

[0134] The Lagrange dual function is equivalent to:

[0135]

[0136] In the formula, is the potential energy field, which is used to characterize the objective function of the dual problem; is the rope shape; ρ(s) is the rope density; ρ min is the minimum line density constraint; m is the rope mass; S is the total length of the rope;

[0137]

[0138] In the formula, and is the receiving power P in the equivalent UAV trajectory design ch，k (t) and optimal transmit power d k (x, y) is the distance from point (x, y) to the horizontal position of user k, which is equivalent to the horizontal distance d from the drone to the user in trajectory design. k (t); equivalent and By adding d k (t) is replaced by d k (x, y) is obtained. Potential energy field For about d k (x, y) and varies with the value of λ.

[0139] The negative gradient of the scalar potential function is used to describe the force field in the potential energy field:

[0140]

[0141] In the formula, g(x, y) is the force field in the potential energy field;

[0142] According to the principle of minimum total potential energy, the optimal rope solution must be kept in a balanced state, which greatly promotes the optimal rope solution. Under the optimal rope shape, from the starting point of the drone (x1, y1) to The resultant forces on the x and y axes are 0, and the expressions are:

[0143]

[0144]

[0145] In the formula, is the optimal initial rope tension; α * is the optimal solution of the initial rope tension angle; is the component of the force field in the x-axis direction; is the component of the force field in the y-axis direction; The optimal rope shape; is the sum of the gravitational force and the projection of the initial rope tension in the x-axis direction; is the sum of the gravitational force and the projection of the initial rope tension in the y-axis direction; Q(s) is the internal tension of the rope;

[0146] exist and α * Known cases, combined Initial value conditions of the equivalent problem, the optimal rope shape when the rope mass is insufficient for:

[0147]

[0148] In the formula, (w 1，x , w 1，y ) is the starting point of the UAV flight;

[0149] According to s=Vt, the optimal UAV trajectory {x*(t), y*(t)} is given by the optimal rope shape It is expressed as:

[0150]

[0151] Therefore, for any given λ, the present invention can successfully construct the optimal closed-form solution of the UAV trajectory, and further solve the corresponding optimal transmission power of the UAV.

[0152] Furthermore, for the optimal solution to the original problem (OP), And according to the P ch，k (t) is about is a convex function. We can get Monotonic with respect to λ. Therefore, there exists λ=λ * Make At this point, due to the strong duality between the original problem (OP) and the dual problem (DP), the same drone trajectory as the original problem (OP) will be generated, and the final task will be to find the corresponding optimal λ * Optimal Lagrange multiplier λ * The design method is:

[0153] When E>P max When T, there may be multiple applicable λ * , define λ at this time * =0;

[0154] When E <P max At T, starting from the feasible interval of λ, the optimal transmission power P is repeatedly solved with λ as the midpoint. λ * (t), and in accordance with Repeat shortening the interval until The optimal Lagrange multiplier λ is obtained when * .

[0155] When the drone reaches the given destination, the energy transfer ends and the drone is charged.

[0156] See also Figure 5 The present invention also provides a UAV trajectory design device in a multi-user wireless power transmission network, which is applied to the UAV trajectory design method in a multi-user wireless power transmission network described above, and the device comprises:

[0157] The parameter acquisition module is used for the UAV to start from a given starting point and transmit energy to multiple users on the ground at the same time to obtain relevant channel parameters;

[0158] The average received total energy calculation module is used to build a nonlinear energy collection model, obtain the received power at the user, and calculate the average received total energy of all users;

[0159] A joint optimization model building module is used to build a joint optimization model of UAV trajectory and transmission power with the goal of maximizing the average total received energy and the maximum speed constraint, the start and end point constraint, the maximum power constraint and the total available energy constraint as constraints;

[0160] The optimal transmission power acquisition module is used to maximize the average received total energy and obtain the optimal transmission power control solution under the condition that the UAV meets the energy budget and power upper limit through the Lagrange duality method;

[0161] The optimal UAV trajectory acquisition module is used to solve the optimal UAV trajectory under the corresponding Lagrangian multiplier for a given Lagrangian multiplier;

[0162] The joint optimization model solving module is used to design the optimal Lagrange multiplier and solve the joint optimization model to obtain the optimal UAV trajectory and transmission power solution.

[0163] See also Figure 6 ,The present invention also provides a UAV trajectory design device in a multi-user wireless power transmission network, including a memory and a processor;

[0164] The memory is used to store computer program code and transmit the computer program code to the processor;

[0165] The processor is used to execute the above-mentioned method for designing drone trajectories in a multi-user wireless power transmission network according to the instructions in the computer program code.

[0166] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for designing a trajectory of a drone in a multi-user wireless power transmission network described above is implemented.

[0167] Generally speaking, the computer instructions for implementing the method of the present invention may be carried in any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media, except for the signal itself that is temporarily propagating.

[0168] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0169] Computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer or to an external computer (for example, using an Internet service provider to connect via the Internet) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0170] The above-mentioned device and non-temporary computer-readable storage medium can be found in the detailed description of a method for designing drone trajectories in a multi-user wireless power transmission network and its beneficial effects, which will not be repeated here.

[0171] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for designing UAV trajectories in a multi-user wireless power transmission network, characterized in that: include: The drone starts from a given starting point and transmits energy to multiple users on the ground at the same time to obtain relevant channel parameters; Construct a nonlinear energy harvesting model to obtain the received power at the user and calculate the average total received energy of all users; With the goal of maximizing the average total received energy, a joint optimization model of UAV trajectory and transmission power is constructed with maximum speed constraint, start and end point constraint, maximum power constraint and total available energy constraint as constraints; Through the Lagrangian duality method, the average received total energy is maximized when the UAV meets the energy budget and power limit, and the optimal transmission power control scheme is obtained; For a given Lagrangian multiplier, solve the optimal UAV trajectory under the corresponding Lagrangian multiplier; Design the optimal Lagrange multiplier and solve the joint optimization model to obtain the optimal UAV trajectory and transmission power solution.

2. The method for designing a trajectory of a drone in a multi-user wireless power transmission network according to claim 1, characterized in that: The nonlinear energy harvesting model is: Where P ch，k (t) is the received power at user k at time t; is the RF received signal power P rf A nonlinear function of out (P rf，k (t)) is the output current on the diode; P rf，k (t) is the RF received signal power at user k at time t; R l is the load resistance; x(t) and y(t) are the horizontal positions of the drone at a height of H at time t; P(t) is the transmission power of the drone at time t; β0 is the channel gain when the reference distance is 1m; (w k，x , w k，y ) is the horizontal position of user k; H is the minimum flight altitude of the UAV.

3. The method for designing a trajectory of a drone in a multi-user wireless power transmission network according to claim 2, characterized in that: The joint optimization model of the UAV trajectory and transmission power is: Where K is the total number of users; E sum，k ({x(t), y(t)}, {P(t)}) is the total received energy at user k at time t; is the first-order derivative of the horizontal position of the UAV; V max is the maximum speed of the UAV; (x1, y1) and (x2, y2) are the horizontal positions of the starting point and the end point set for the UAV respectively; E is the energy budget.

4. The method for designing a trajectory of a drone in a multi-user wireless power transmission network according to claim 3, characterized in that: The total received energy at user k at time t is: Where T is the UAV mission cycle.

5. The method for designing a trajectory of a drone in a multi-user wireless power transmission network according to claim 4, characterized in that: The Lagrange duality method is used to maximize the average total received energy when the UAV meets the energy budget and power upper limit, and obtain the optimal transmission power control scheme, including: Construct the following Lagrangian function: Where λ is the Lagrange multiplier; Lagrange dual function L D (λ) is: L D (λ)=min {x(t)y(t),P(t)} L({x(t),y(t),P(t)},λ); The optimal transmit power is solved according to the following formula: Define the objective function as The first-order derivative is: Where, d k (t) is the distance from the drone to user k at time t; The point where the first-order derivative is zero is set to P critial ,satisfy: For a given Lagrange multiplier λ, the optimal transmit power is calculated as: In the formula, is the optimal transmission power; P max is the maximum transmission power of the drone.

6. The method for designing a trajectory of a drone in a multi-user wireless power transmission network according to claim 5, characterized in that: For a given Lagrangian multiplier, solving the optimal UAV trajectory under the corresponding Lagrangian multiplier includes: The Lagrange dual function is equivalent to: In the formula, is the potential energy field; is the rope shape; ρ(s) is the rope density; ρ min is the minimum line density constraint; m is the rope mass; S is the total length of the rope; In the formula, and is the equivalent corresponding received power P ch，k (t) and optimal transmit power d k (x, y) is the distance from point (x, y) to the horizontal position of user k; The negative gradient of the scalar potential function is used to describe the force field in the potential energy field: In the formula, g(x, y) is the force field in the potential energy field; Under the optimal rope shape, from the starting point of the drone (x1, y1) to The resultant forces on the x and y axes are 0, and the expressions are: In the formula, is the optimal initial rope tension; α * is the optimal solution of the initial rope tension angle; is the component of the force field in the x-axis direction; is the component of the force field in the y-axis direction; The optimal rope shape; is the sum of the gravitational force and the projection of the initial rope tension in the x-axis direction; is the sum of the gravitational force and the projection of the initial rope tension in the y-axis direction; Q(s) is the internal tension of the rope; Optimal rope shape for: In the formula, (w 1，x , w 1，y ) is the starting point of the UAV flight; According to s = Vt, the optimal UAV trajectory {x * (t), y * (t)} by the optimal rope shape It is expressed as:

7. The method for designing a trajectory of a drone in a multi-user wireless power transmission network according to claim 5, characterized in that: Optimal Lagrange multiplier λ * The design method is: When E>P max When T, λ * =0; When E<P max At T, starting from the feasible interval of λ, the optimal transmission power is repeatedly solved with λ as the midpoint And according to Repeat shortening the interval until The optimal Lagrange multiplier λ is obtained when * .

8. A device for designing drone trajectories in a multi-user wireless power transmission network, characterized in that: The device is applied to the method described in any one of claims 1 to 7, and the device comprises: The parameter acquisition module is used for the UAV to start from a given starting point and transmit energy to multiple users on the ground at the same time to obtain relevant channel parameters; The average received total energy calculation module is used to build a nonlinear energy collection model, obtain the received power at the user, and calculate the average received total energy of all users; A joint optimization model building module is used to build a joint optimization model of UAV trajectory and transmission power with the goal of maximizing the average total received energy and the maximum speed constraint, the start and end point constraint, the maximum power constraint and the total available energy constraint as constraints; The optimal transmission power acquisition module is used to maximize the average received total energy and obtain the optimal transmission power control solution under the condition that the UAV meets the energy budget and power upper limit through the Lagrange duality method; The optimal UAV trajectory acquisition module is used to solve the optimal UAV trajectory under the corresponding Lagrangian multiplier for a given Lagrangian multiplier; The joint optimization model solving module is used to design the optimal Lagrange multiplier and solve the joint optimization model to obtain the optimal UAV trajectory and transmission power solution.

9. A device for designing drone trajectories in a multi-user wireless power transmission network, characterized in that: including memory and processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 7 according to instructions in the computer program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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