Energy acquisition and resource allocation optimization method in communication and inductance integrated system

Abstract

The invention discloses an energy collection and resource allocation optimization method in a communication and inductance integrated system, and belongs to the technical field of wireless communication. The energy acquisition and resource allocation optimization method in the communication and sensing integrated system comprises the following steps: constructing an unmanned aerial vehicle optimization problem based on a wireless energy acquisition threshold value and a sensing threshold value of a user, and decomposing the unmanned aerial vehicle optimization problem into a user scheduling sub-problem, a time slot allocation sub-problem and an unmanned aerial vehicle trajectory optimization sub-problem; and iteratively solving the user scheduling sub-problem, the time slot allocation sub-problem and the unmanned aerial vehicle trajectory optimization sub-problem by utilizing a greedy method, a linear programming method and a continuous convex approximation method, and determining an optimal user scheduling strategy, an optimal time slot allocation strategy and an optimal unmanned aerial vehicle flight trajectory. According to the method, wireless energy transmission of the user can be ensured, and the communication and rate of the system can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to an energy collection and resource allocation optimization method in a synaesthesia integrated system. Background Art

[0002] Wireless signals not only carry information but also energy. For low-energy devices, wireless power transmission can provide energy via radio frequency signals. By integrating information and energy transmission, researchers have achieved simultaneous wireless information and power transmission.

[0003] In the field of wireless communications, drone communications, with their advantages of strong line-of-sight links and flexible deployment, have become a crucial component of the next-generation integrated air-ground-space network. The integrated synaesthesia system, through hardware sharing and waveform fusion design, achieves deep coupling of communication and perception functions, further improving spectrum efficiency and system energy efficiency. Furthermore, wireless power transmission technology can provide energy to low-energy devices via radio frequency signals. Researchers have integrated information and energy transmission to achieve simultaneous wireless information and power transmission.

[0004] However, current drone-assisted interawareness systems rarely consider providing users with continuous wireless energy harvesting, making it difficult for the system to maintain the coordinated operation of communication and perception functions for a long time. Specifically, without an effective energy supply mechanism, ground-based IoT users may experience interruptions in information reception due to insufficient energy. Furthermore, drones' fixed trajectories or static resource allocation methods cannot balance energy transmission efficiency with communication and perception performance, further exacerbating the contradiction between system energy efficiency and stability.

[0005] In view of the above-mentioned defects, the present invention proposes an energy harvesting and resource allocation optimization method in a synaesthesia integrated system. Summary of the Invention

[0006] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method for optimizing energy harvesting and resource allocation in a synaesthesia integrated system, which solves the problems in the prior art.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for optimizing energy collection and resource allocation in a synaesthesia integrated system comprises the following steps:

[0009] Based on the user's wireless energy harvesting threshold and perception threshold, a UAV optimization problem is constructed and decomposed into user scheduling sub-problem, time slot allocation sub-problem and UAV trajectory optimization sub-problem;

[0010] The greedy method, linear programming method and continuous convex approximation method are used to iteratively solve the user scheduling subproblem, time slot allocation subproblem and UAV trajectory optimization subproblem to determine the optimal user scheduling strategy, time slot allocation strategy and UAV flight trajectory.

[0011] Furthermore, the integrated synaesthesia system includes a drone and K IoT users; the drone flies at a constant speed from a starting point to an end point, providing services to the K IoT users on the ground during the flight. The drone senses the environmental information around the IoT users and sends it to the IoT users, while charging the IoT users to complete information reception; after receiving the charging and communication signals from the drone, the IoT users divide the power of the received signals and perform energy collection and information decoding respectively.

[0012] Furthermore, the UAV optimization problem is expressed as P1:

[0013]

[0014] C4:u(1)=u ini ,u(N)=u fin

[0015]

[0016] Among them, A represents the user scheduling strategy set, B represents the time slot allocation strategy set, U represents the UAV trajectory, R c represents the system and rate, N represents the number of time slots, K represents the number of users, and a k (n) represents the user scheduling policy, P t Indicates the transmit power, represents the communication channel, u ini Indicates the starting point of flight, V max represents the maximum flight speed of the UAV, ρ represents the power allocation factor, σ 2 represents the noise power, β(n) represents the time slot allocation factor, δ t Indicates the sub-slot length, represents the perceptual mutual information, represents the sensing channel, φ min represents the perception threshold, E k (n) represents wireless harvesting energy, E min represents the energy threshold, u fin Indicates the end of the flight;

[0017] Constraint C1 is a perception constraint, which means that the radar mutual information for any user k in the entire task must be greater than the perception threshold φ minConstraint C2 is the user selection scheduling; Constraint C3 is the wireless charging constraint, which means that in each time slot n, the energy charged by user k must be greater than or equal to the energy threshold E for information decoding. min ; C4 and C5 are the UAV flight constraints, indicating the initial and final positions of the UAV, and the UAV's flight speed must not exceed the UAV's maximum flight speed V max .

[0018] Furthermore, the user scheduling sub-problem is:

[0019]

[0020] Furthermore, the time slot scheduling sub-problem is:

[0021]

[0022] Furthermore, the UAV trajectory optimization sub-problem is:

[0023]

[0024] C4:u(1)=u ini ,u(N)=u fin

[0025]

[0026] An energy collection and resource allocation optimization device in a synaesthesia integrated system, comprising:

[0027] Optimization problem construction module: Based on the user's wireless energy harvesting threshold and perception threshold, the drone optimization problem is constructed and decomposed into the user scheduling sub-problem, the time slot allocation sub-problem, and the drone trajectory optimization sub-problem;

[0028] Optimization problem solving module: Utilizes greedy methods, linear programming methods, and continuous convex approximation methods to iteratively solve the user scheduling subproblem, time slot allocation subproblem, and UAV trajectory optimization subproblem to determine the optimal user scheduling strategy, time slot allocation strategy, and UAV flight trajectory.

[0029] A computer storage medium stores a readable program, which, when executed by a processor, can implement the above-mentioned energy collection and resource allocation optimization method in a synaesthesia integrated system.

[0030] An electronic device comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0031] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned energy harvesting and resource allocation optimization method in the synaesthesia integrated system.

[0032] A computer program product includes computer instructions, wherein the computer instructions instruct a computing device to execute operations corresponding to the above-mentioned method for optimizing energy harvesting and resource allocation in a synaesthesia integrated system.

[0033] Beneficial effects of the present invention:

[0034] The proposed energy harvesting and resource allocation optimization method for an integrated synaesthesia system constructs a drone optimization problem based on user wireless energy harvesting thresholds and perception thresholds, decomposing it into user scheduling, time slot allocation, and drone trajectory optimization subproblems. The problem is solved iteratively using a greedy approach, a linear programming approach, and a continuous convex approximation method to determine the optimal user scheduling strategy, time slot allocation strategy, and drone flight trajectory. While ensuring that user wireless energy transmission meets the information decoding energy threshold, the perception mutual information is no less than the minimum detection threshold, and the drone flight constraints, the method dynamically adjusts the drone flight path to approach the user to improve channel gain by jointly optimizing charging time, user scheduling, and drone flight trajectory. The method also rationally allocates time slots to balance perception and communication time, solving the non-convex optimization problem of coupling drone flight trajectory with scheduling strategy and significantly improving system communication and rate. Experimental verification shows that the method significantly improves system and rate compared to comparable methods, supports multi-user scenario expansion, and has good adaptability to parameters such as flight altitude and transmit power. This method provides an effective solution for improving the energy efficiency of drone-assisted integrated synaesthesia systems, and possesses flexibility and compatibility in engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0036] Figure 1 This is a schematic diagram of the structure of the synaesthesia integration system of the present invention;

[0037] Figure 2 This is a schematic diagram of the UAV flight time slot allocation of the present invention;

[0038] Figure 3 This is a schematic diagram of user power allocation according to the present invention;

[0039] Figure 4 is a flow chart of the optimization method of the present invention;

[0040] Figure 5 The trajectory diagram of the UAV of the present invention;

[0041] Figure 6 A graph showing the change of the optimized trajectory of the UAV of the present invention with the number of flight time slots;

[0042] Figure 7 This is the optimized trajectory diagram of the drone when the number of users is 4 in the present invention;

[0043] Figure 8 This is the optimized trajectory diagram of the drone when the number of users is 5 in the present invention;

[0044] Figure 9 The user scheduling strategy during the UAV flight process of the present invention;

[0045] Figure 10 The system throughput changes with the number of users;

[0046] Figure 11 This is a graph showing how the system and rate corresponding to the optimization method of the present invention change with the maximum transmission power of the UAV;

[0047] Figure 12 This is a graph showing how the system and speed corresponding to the optimization method of the present invention change with the flight time of the UAV;

[0048] Figure 13 This is a comparison chart of the system and speed corresponding to the optimization method of the present invention and the flight altitude of the UAV. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] Example 1

[0051] like Figure 1 As shown in the figure, a synaesthesia integrated system includes a drone and K IoT users. The drone flies at a constant speed from a starting point to an end point within a time T, providing services to the K IoT users on the ground during the flight. The drone needs to perceive the environmental information around the IoT users and send the information to the IoT users. At the same time, the IoT users are charged to complete the information reception.

[0052] The UAV adopts time division multiplexing working mode, and the whole mission working time slot is allocated as follows Figure 2As shown. The entire flight time T of the drone is divided into N time slots, each time slot is divided into two sub-time slots, and the sensing sub-time slot is β(n)δ t , the wireless charging and communication sub-slot is (1-β(n))δ t After receiving the charging and communication signals from the drone, the IoT user divides the power of the received signal, collects the energy of the received signal power (1-ρ), and decodes the signal of the received signal power ρ, such as Figure 3 As shown. Assume that the flying height of the UAV is fixed at H. At any time slot n, the horizontal coordinate of the UAV is denoted as u(n) = (x n ,y n ), the horizontal coordinate of the kth IoT user on the ground is b k , then the distance d between the drone and IoT user k is k (n) is:

[0053]

[0054] Considering the LOS channel, the communication channel gain for:

[0055]

[0056] The various model mechanisms involved in this embodiment are as follows:

[0057] 1) Communication model

[0058] The transmission power of the UAV is P t , the power received by the kth user is In this embodiment, it is assumed that the drone only senses, charges, and communicates with one ground IoT user in any time slot n. Assuming that in time slot n, the drone selects IoT user k, then the binary variable a k (n)=1, otherwise a k (n) = 0, and Therefore, in time slot n, the channel capacity between the drone and IoT user k is It can be expressed as:

[0059]

[0060] Among them, σ 2 is the noise power, β(n) is the time slot allocation factor, δ t is the time slot length, ρ is the power allocation factor;

[0061] The total communication capacity (i.e., system sum rate) of the entire mission process is R c It can be expressed as:

[0062]

[0063] 2) Perception Model

[0064] This embodiment uses radar mutual information to measure perception capability. Considering the transmission link and return link of the radar signal, in time slot n, the radar link channel power between the drone and IoT user k is It can be expressed as

[0065]

[0066] Among them, β r is the perceived channel fading coefficient, d k (n) is the distance between the drone and the user.

[0067] Therefore, in time slot n, the radar mutual information between the drone and IoT user k is:

[0068]

[0069] The total radar mutual information of IoT user k during the entire flight of the drone for:

[0070]

[0071] 3) Wireless energy transmission model

[0072] In time slot n, the energy E collected by IoT user k k (n) can be expressed as:

[0073]

[0074] Example 2

[0075] like Figure 4 As shown, a method for optimizing energy harvesting and resource allocation in a synaesthesia integrated system includes the following steps:

[0076] S1, based on the user's wireless energy harvesting threshold and perception threshold, constructs the UAV optimization problem and decomposes it into the user scheduling sub-problem, the time slot allocation sub-problem and the UAV trajectory optimization sub-problem;

[0077] The preconditions of the UAV optimization problem include: the user's collected energy is greater than the energy collection threshold φ min , the perceived mutual information is greater than the perception threshold E min .

[0078] The drone optimization problem is expressed as P1:

[0079]

[0080] C4:u(1)=u ini ,u(N)=u fin

[0081]

[0082] Among them, A represents the user scheduling strategy set, B represents the time slot allocation strategy set, U represents the UAV trajectory, R c represents the system and rate, N represents the number of time slots, K represents the number of users, and a k (n) represents the user scheduling policy, P t Indicates the transmit power, represents the communication channel, u ini Indicates the starting point of flight, V max represents the maximum flight speed of the UAV, ρ represents the power allocation factor, σ 2 represents the noise power, β(n) represents the time slot allocation factor, δ t Indicates the sub-slot length, represents the perceptual mutual information, represents the sensing channel, φ min represents the perception threshold, E k (n) represents wireless harvesting energy, E min represents the energy threshold, u fin Indicates the end of the flight;

[0083] Constraint C1 is a perception constraint, which means that the radar mutual information for any user k in the entire task must be greater than the minimum detection threshold φ min Constraint C2 is the user selection schedule; Constraint C3 is the wireless charging constraint, which means that in each time slot n, the energy charged by user k must be greater than or equal to the energy E of information decoding min ; C4 and C5 are the UAV flight constraints, indicating the initial and final positions of the UAV, and the UAV's flight speed does not exceed the maximum speed V max .

[0084] The user scheduling subproblem is expressed as P2:

[0085]

[0086] The time slot scheduling sub-problem is expressed as P3

[0087]

[0088] The UAV trajectory optimization sub-problem is expressed as P5:

[0089]

[0090] C4:u(1)=uini ,u(N)=u fin

[0091]

[0092] S2 uses greedy methods, linear programming methods and continuous convex approximation methods to iteratively solve the user scheduling subproblem, time slot allocation subproblem and UAV trajectory optimization subproblem to determine the optimal user scheduling strategy, time slot allocation strategy and UAV flight trajectory.

[0093] 1) P2 is a 0, 1 integer programming problem. This embodiment uses a greedy approach to solve the user scheduling subproblem. The scheduling matrix is ​​constructed by selecting the user that maximizes the communication capacity gain in each time slot, while ensuring that all constraints are met. The greedy approach is then used to solve the user scheduling problem, allocating a time slot to each user so that user k achieves the maximum communication rate within this time slot while satisfying the energy constraint. The optimal user scheduling strategy is obtained by preferentially allocating the remaining time slots to users whose perceived mutual information does not meet the constraints, and otherwise allocating them to users that maximize the system sum rate. The specific steps include:

[0094] 1.1) Initialize the user scheduling matrix is empty;

[0095] 1.2) Randomly assign a time slot to each user to ensure that the energy constraint is met;

[0096] 1.3) Assign a time slot to each user so that the communication rate of user k is maximized within this time slot while satisfying the energy constraint;

[0097] 1.4) For the remaining time slots, they are preferentially allocated to users whose perceived mutual information does not meet the constraints, otherwise they are allocated to users who can maximize the system sum rate;

[0098] 1.5) Check whether each user meets the perception constraints. If not, continue to allocate available time slots until the perception constraints are met or there are no available time slots;

[0099] 1.6) The maximum system sum rate is obtained and the corresponding user scheduling strategy can be obtained.

[0100] 2) Solve the time slot allocation subproblem using linear programming to obtain the optimal time slot allocation strategy. Solve the linear programming problem using Matlab's solver. The specific steps include:

[0101] Introducing intermediate variables C(n) and D k (n), we can reformulate problem P3 as follows:

[0102]

[0103] in,

[0104]

[0105] It can be observed that this is a linear programming problem about the variable B, and the optimal time slot allocation B can be obtained by using the Matlab solver CVX.

[0106] 3) Solve the UAV trajectory subproblem using continuous convex approximation to obtain the optimal UAV flight trajectory, including the following steps:

[0107] 3.1) Obtain the lower bound of the objective function of problem P5 through first-order Taylor expansion;

[0108] 3.2) Simplify the objective function expression of problem P5 by introducing slack variables;

[0109] 3.3) Obtain the current optimal UAV position through continuous convex approximation;

[0110] 3.4) Determine whether the objective function of problem P5 converges. If so, stop the iteration and obtain the optimal trajectory of the drone.

[0111] Specifically: In problem P5, it can be observed that the objective function and constraints C5, C8, and C9 are highly coupled with respect to the optimization variable U. This embodiment adopts a continuous convex approximation method, which relaxes the original problem into a convex problem by performing a first-order Taylor expansion on the non-convex expression, and then solves it with the help of a convex optimization tool. For the convenience of expression, the relaxation variable q(n) = H is introduced. 2 +||u(n)-b k || 2 , which is expressed as the square of the distance between the UAV in the nth time slot and the user k selected by the scheduling strategy. In order to simplify the expression, an intermediate variable is introduced, and it is set as follows:

[0112] F k (n) = a k (n)(1-β(n))δ t

[0113]

[0114] At this point, problem P5 can be reformulated as:

[0115]

[0116] C4:u(1)=u ini ,u(N)=u fin

[0117]

[0118] It can be seen that the non-convexity of P6 mainly lies in the objective function and constraint C8. The objective function is convex with respect to the optimization variable q(n), which can be proved by finding that the second-order derivative of the objective function with respect to the variable q(n) is always greater than 0. Therefore, the first-order Taylor expansion of the objective function can express the lower bound of this convex function, which can be expressed at a given q l Perform a first-order Taylor expansion at (n) to obtain the lower bound of the objective function:

[0119]

[0120] The objective function of the optimization problem P6 can be relaxed to maximize its lower bound The first-order derivative of the logarithmic part of the left side of the constraint C8 inequality with respect to q(n) can be expressed as:

[0121]

[0122] The second-order derivative can be expressed as

[0123]

[0124] Therefore, the constraint C8 is convex with respect to q(n), and C8 is also at point q l The first-order Taylor expansion at (n) is:

[0125]

[0126] Replace the left side of the constraint C8 inequality with its lower bound Through the above relaxation transformation, in the lth iteration of the continuous convex approximation, the optimization problem can be expressed as:

[0127]

[0128] C4,C5

[0129] By iteratively solving subproblem P7.1, we can get the optimal solution to problem P6. Then, through q(n)=H 2 +||u(n)-b k || 2 Can calculate the optimal drone trajectory

[0130] By solving problems P2, P3, and P7, we can obtain the optimized user scheduling strategy, time slot allocation, and UAV flight trajectory respectively, and perform alternating iterative solutions until the objective function converges to obtain the optimal solutions for the user scheduling strategy, UAV flight trajectory, and time slot allocation.

[0131] By alternately iterating problems P1, P2, and P5, the optimal user scheduling, time slot allocation, and UAV flight trajectory are obtained, including the following steps:

[0132] (1) Initialize the drone trajectory Time slot allocation Let o = 1;

[0133] (2) By solving P2, update

[0134] (3) By solving P4, update

[0135] (4) Let l = 1, q l-1 (n) = q o-1 (n);

[0136] (5) By solving P7.1, at point q l-1 Perform Taylor expansion at (n) to obtain q l (n);

[0137] (6) l = l + 1;

[0138] (7) The objective function of P6.l converges or l ≥ l max , otherwise return (5);

[0139] (8) Update q o (n) = q l (n), o = o + 1;

[0140] (9) The P1 objective function converges or reaches the maximum number of iterations, otherwise return to (2).

[0141] Based on similar inventive concepts, an embodiment of the present invention further provides a computer storage medium storing a readable program, which, when executed by a processor, can execute the above-mentioned method for optimizing energy harvesting and resource allocation in a synaesthesia integrated system.

[0142] Based on similar inventive concepts, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0143] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned energy harvesting and resource allocation optimization method in the synaesthesia integrated system.

[0144] Based on similar inventive concepts, an embodiment of the present invention further provides a computer program product, including computer instructions, which instruct a computing device to execute operations corresponding to the above-mentioned energy harvesting and resource allocation optimization method in a synaesthesia integrated system.

[0145] Example 3

[0146] In order to verify that the method proposed in the present invention (Example 2) is true, consider comparing it with the following two methods:

[0147] (1) Comparison method 1: The drone trajectory is not optimized. The drone flies in a straight line from the starting point to the end point, and only the time slot allocation and scheduling strategy are optimized.

[0148] (2) Comparative method 2: The time slot allocation of synaesthesia is not optimized, and the communication and perception sub-time slots each occupy half. Only the flight trajectory of the UAV and the user scheduling strategy are optimized.

[0149] Figure 5 The optimized drone trajectory diagram of the present invention shows that the optimized drone trajectory approaches each user in turn, but due to the limited number of time slots and flight speed, it does not stop over users 1 and 2. To achieve maximum throughput, the optimized drone trajectory chooses to briefly stop over user 3 to transmit information and energy. By approaching the user locations, the optimized trajectory improves channel gain, better ensures energy transmission and sensing constraints, and maximizes the total throughput of the drone flight mission.

[0150] Figure 6 The figure shows how the optimized drone trajectories change with the number of flight slots. As the figure shows, the three optimized trajectories all approach each user sequentially. As the number of time slots increases, the drone's trajectory moves closer to each user. This is because with fewer time slots, the drone's flight speed limits it from getting closer to and staying above each user, maintaining a good communication link. It can be seen that when the number of flight slots reaches 30, the drone can already stay above each user, transmitting information and harvesting energy.

[0151] Figure 7 and Figure 8 The following plots show how the drone's trajectory changes with different numbers of users, for K = 4 and K = 5, respectively. The user locations are randomly generated within a 100m x 100m area. As can be seen from both figures, at the beginning of the flight, the drone flies straight to the nearest user, then approaches the remaining users in sequence, finally reaching the destination. Figure 7 In order to achieve the maximum system sum rate, the drone trajectory is offset to each user. Figure 8In ,users 1, 2 and are far away from the straight trajectory, and the ,UAV trajectory shifts towards users 1, 2, and 3, respectively, to maximize the system ,rate.

[0152] Figure 9 The changes of the user scheduling strategy of the present invention during the flight of the UAV are given, and it can be seen that the UAV selects users during the flight. The UAV maintains communication with user 3 for a longer time than with user 1 and user 2. This is because after the 11th time slot, the distance between the UAV and user 3 is the closest, and a higher communication capacity can be achieved. It can also be seen that the UAV schedules with users 1, 2, and 3 in turn during the flight. By observing Figure 5 ,The drone approaches users 1, 2, and 3 in sequence, which is consistent with the user scheduling ,strategy.

[0153] Figure 10 The figure shows how the system sum rate changes with the number of users. As can be seen from the figure, the system sum rate increases with the number of users. The system sum rate of the present invention is higher than that of the two comparison methods. Compared with comparison method 1, optimizing the drone trajectory can ensure better channel quality and improve the system sum rate. Compared with comparison method 2, the length of the communication time slot directly affects the system sum rate. Optimizing the communication time slot length can significantly improve the system sum rate.

[0154] Figure 11 The figure shows how the system sum rate changes with the maximum transmit power of the drone. The figure shows that the system sum rate increases monotonically with the drone's maximum transmit power. This is due to several factors. First, the system sum rate itself is proportional to the transmit power. Second, as the power increases, the length of each perception subslot shortens, the communication subslot increases, and the system sum rate increases. As can be seen in the figure, since the time slot length of comparison method 2 remains unchanged, the performance gap between the present invention and the comparison method increases with increasing transmit power. The proposed method outperforms both comparison methods, demonstrating that optimizing drone trajectories and time slot length allocation can improve the system sum rate.

[0155] Figure 12 The figure shows how the system sum rate changes with the drone's flight time. As can be seen from the figure, the system sum rate increases monotonically with the increase in the drone's flight time. This is because the drone's flight time increases, but the number of time slots remains unchanged, which increases the drone's travel distance in a single time slot. The drone can hover above the user for longer periods of time to perform telepathy and charging tasks, thereby increasing the system sum rate. As the flight time increases, the performance gap between the present invention and Comparative Method 1 becomes increasingly larger. This is because the increased flight time increases the drone's spatial freedom. Compared to a fixed trajectory, optimizing the drone's trajectory allows the drone to hover above the user for longer periods of time, maximizing the system sum rate.

[0156] Figure 13 A graph comparing the system sum rate and the drone's flight altitude is presented. The graph shows that the system sum rate decreases as the drone's flight altitude increases. This is because as the drone's altitude increases, the gain of the communication channel between the drone and the user decreases. This decrease in communication channel gain directly affects the system sum rate. This decrease in perception channel gain increases the length of the perception subslot and decreases the length of the communication subslot, further reducing the system sum rate.

[0157] Example 4

[0158] Based on the energy collection and resource allocation optimization method in the synaesthesia integrated system proposed in Example 2, this embodiment proposes an energy collection and resource allocation optimization device in the synaesthesia integrated system, including:

[0159] Optimization problem construction module: Based on the user's wireless energy harvesting threshold and perception threshold, the drone optimization problem is constructed and decomposed into the user scheduling sub-problem, the time slot allocation sub-problem, and the drone trajectory optimization sub-problem;

[0160] Optimization problem solving module: Utilizes greedy methods, linear programming methods, and continuous convex approximation methods to iteratively solve the user scheduling subproblem, time slot allocation subproblem, and UAV trajectory optimization subproblem to determine the optimal user scheduling strategy, time slot allocation strategy, and UAV flight trajectory.

[0161] The method of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware (such as an ASIC or FPGA). It will be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown here, the execution of the code converts the general-purpose computer into a special-purpose computer for executing the method shown here.

[0162] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for optimizing energy collection and resource allocation in a synaesthesia integrated system, characterized in that: The following steps are involved: Based on the user's wireless energy harvesting threshold and perception threshold, a UAV optimization problem is constructed and decomposed into user scheduling sub-problem, time slot allocation sub-problem and UAV trajectory optimization sub-problem; The greedy method, linear programming method and continuous convex approximation method are used to iteratively solve the user scheduling subproblem, time slot allocation subproblem and UAV trajectory optimization subproblem to determine the optimal user scheduling strategy, time slot allocation strategy and UAV flight trajectory.

2. The method for optimizing energy collection and resource allocation in a synaesthesia integrated system according to claim 1, characterized in that: The integrated synaesthesia system includes a drone and K IoT users. The drone flies at a constant speed from a starting point to an end point, providing services to the K IoT users on the ground during the flight. The drone senses the environmental information around the IoT users and sends it to the IoT users, while also charging the IoT users to enable them to complete information reception. After receiving the charging and communication signals from the drone, the IoT users split the power of the received signals and perform energy harvesting and information decoding respectively.

3. The method for optimizing energy collection and resource allocation in a synaesthesia integrated system according to claim 1, characterized in that: The drone optimization problem is expressed as P1: Among them, A represents the user scheduling strategy set, B represents the time slot allocation strategy set, U represents the UAV trajectory, R c represents the system and rate, N represents the number of time slots, K represents the number of users, and a k (n) represents the user scheduling policy, P t Indicates the transmit power, represents the communication channel, u ini Indicates the starting point of flight, V max represents the maximum flight speed of the UAV, ρ represents the power allocation factor, σ 2 represents the noise power, β(n) represents the time slot allocation factor, δ t Indicates the sub-slot length, represents the perceptual mutual information, represents the sensing channel, φ min represents the perception threshold, E k (n) represents wireless harvesting energy, E min represents the energy threshold, u fin Indicates the end of the flight; Constraint C1 is a perception constraint, which means that the radar mutual information for any user k in the entire task must be greater than the perception threshold φ min Constraint C2 is the user selection scheduling; Constraint C3 is the wireless charging constraint, which means that in each time slot n, the energy charged by user k must be greater than or equal to the energy threshold E for information decoding. min ; C4 and C5 are the UAV flight constraints, indicating the initial and final positions of the UAV, and the UAV's flight speed must not exceed the UAV's maximum flight speed V max .

4. The method for optimizing energy collection and resource allocation in a synaesthesia integrated system according to claim 3, characterized in that: The user scheduling sub-problem is:

5. The method for optimizing energy collection and resource allocation in a synaesthesia integrated system according to claim 3, characterized in that: The time slot scheduling sub-problem is:

6. The method for optimizing energy collection and resource allocation in a synaesthesia integrated system according to claim 3, characterized in that: The UAV trajectory optimization sub-problem is:

7. An energy collection and resource allocation optimization device in a synaesthesia integrated system, characterized in that: include: Optimization problem construction module: Based on the user's wireless energy harvesting threshold and perception threshold, the drone optimization problem is constructed and decomposed into the user scheduling sub-problem, the time slot allocation sub-problem, and the drone trajectory optimization sub-problem; Optimization problem solving module: Utilizes greedy methods, linear programming methods, and continuous convex approximation methods to iteratively solve the user scheduling subproblem, time slot allocation subproblem, and UAV trajectory optimization subproblem to determine the optimal user scheduling strategy, time slot allocation strategy, and UAV flight trajectory.

8. A computer storage medium storing a readable program, characterized in that: When the program is executed by the processor, the method for optimizing energy collection and resource allocation in a synaesthesia integration system according to any one of claims 1 to 6 can be executed.

9. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the energy harvesting and resource allocation optimization method in the synaesthesia integration system according to any one of claims 1 to 6.

10. A computer program product comprising computer instructions, characterized in that The computer instructions instruct the computing device to execute operations corresponding to the method for optimizing energy harvesting and resource allocation in a synaesthesia integration system as described in any one of claims 1 to 6.

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