Multi-device energy beam focusing method and device for wireless energy transmission system of unmanned aerial vehicle

Abstract

The invention relates to a multi-device energy beam focusing method and device for a wireless energy transmission system of an unmanned aerial vehicle, the energy transmission system of the unmanned aerial vehicle comprises an energy source, a transmitting antenna array and a receiving antenna array, and the method comprises the following steps: the receiving antenna array receives radio frequency guide signals transmitted by each device to be charged; the unmanned aerial vehicle determines position information of each to-be-charged device based on the radio frequency guide signal; the unmanned aerial vehicle calculates a transmitting beam focusing vector based on the position information of the to-be-charged equipment; and the transmitting antenna array generates focusing beams for the plurality of to-be-charged devices by using the energy source according to the transmitting beam focusing vector. According to the invention, energy can be efficiently transmitted to a plurality of devices in an energy radio frequency radiation near-field area at the same time or in batches according to needs, and electromagnetic energy pollution to non-target area devices is reduced.

Description

Technical Field

[0001] This application belongs to the field of wireless power transfer technology for unmanned aerial vehicles (UAVs), specifically, it relates to a method and apparatus for focusing energy beams across multiple devices in a wireless power transfer system for UAVs. Background Technology

[0002] Currently, drones equipped with WPT (Wireless Power Transfer) systems can provide targeted charging for devices. Unlike wired charging or periodic battery replacement, WPT systems can charge multiple devices simultaneously or at different times, offering flexible deployment and significantly reducing maintenance costs.

[0003] Existing WPT systems utilize beamforming technology for directional energy transfer, limiting their application to the far-field region of energy radio frequency radiation. This requires a distance greater than the Rayleigh distance between transceivers, and energy leakage caused by directional beam diffusion not only reduces energy transfer efficiency but also causes electromagnetic pollution to devices in non-target areas, particularly in high-density IoT applications. With the utilization of millimeter-wave frequencies, ELAA (Extremely Large-scale Antenna Array) is driving the expansion of WPT systems into the near-field region of radiation, where energy can be focused. Unlike the far-field region of electromagnetic wave propagation, which exhibits a planar wavefront, the near-field region exhibits a spherical wavefront, enabling beam focusing and concentrating energy in a smaller three-dimensional space. This improves energy transfer efficiency and reduces energy pollution to non-target areas.

[0004] Despite the significant advantages of near-field beam focusing, ELAA-based WPT systems still face challenges. Most existing techniques rely on the assumption of perfect known channel information, which is impractical for ELAA because accurately acquiring spherical wave channel information requires high pilot overhead proportional to the number of ELAA antenna elements. While some techniques have explored beam focusing schemes that do not require channel information, these suffer from significant processing overhead and latency. Currently, no method or system can achieve simultaneous energy beam focusing from multiple devices. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application provides a multi-device energy beam focusing method and apparatus for a UAV wireless energy transfer system, which enables multiple devices to perform energy beam focusing simultaneously with low processing overhead and low latency.

[0006] The technical solution adopted in this application is as follows.

[0007] The first aspect of this application provides a multi-device energy beam focusing method for a UAV wireless energy transfer system, the UAV energy transfer system comprising an energy source, a transmitting antenna array, and a receiving antenna array, the method comprising:

[0008] The receiving antenna array receives the radio frequency guidance signals transmitted by each device to be charged;

[0009] The drone uses radio frequency guidance signals to determine the location information of each device to be charged;

[0010] The drone calculates the focusing vector of the transmitted beam based on the location information of the device to be charged.

[0011] The transmitting antenna array uses an energy source to generate a focused beam for multiple devices to be charged, according to the focusing vector of the transmitting beam.

[0012] Optionally, the transmitting antenna array is a square array structure, which is divided into M transmitting antenna subarrays. If the number of devices to be charged is not greater than M, the corresponding number of transmitting antenna subarrays are activated to generate a focused beam for each device to be charged. If the number of devices to be charged is greater than M, the number of devices to be charged is divided into batches in units of M, and a focused beam for each batch of devices is generated simultaneously. M is a positive integer greater than 1.

[0013] Optionally, the receiving antenna array is arranged around the periphery of the transmitting antenna array.

[0014] Optionally, the receiving antenna array includes a horizontal linear receiving array and a vertical linear receiving array. The horizontal linear receiving array is arranged horizontally around the transmitting antenna array, and the vertical linear receiving array is arranged vertically around the transmitting antenna array.

[0015] Optionally, a transmit beam focusing vector is calculated based on the location information of the device to be charged, and the transmit antenna array uses an energy source to generate multiple energy-focused beams according to the transmit beam focusing vector, including:

[0016] A near-field channel model is constructed based on the location information of each device to be charged, and the vectorized channel coefficient estimates from the transmitting antenna array to each device to be charged are obtained.

[0017] The transmit beam focusing vector of the transmit antenna array is initialized based on the vectorized channel coefficient estimates;

[0018] The energy collected by each device to be charged is calculated based on the vectorized channel coefficient estimate and the transmit beam focusing vector.

[0019] The objective function is to maximize the weighted logarithmic sum of the energy collected by all devices. The objective function is then solved by combining the maximum transmit power constraint of the transmitting antenna array, the quantization phase constraint, and the near-field boundary constraint to obtain the optimal quantization phase for each device to be charged.

[0020] The optimal transmit beam focusing vector is obtained based on the optimal quantization phase, and the transmit antenna array uses the energy source to generate multiple energy focusing beams according to the optimal transmit beam focusing vector.

[0021] Optionally, the objective function is calculated and solved based on the alternating quantization refinement algorithm to obtain the optimal quantization phase for each device to be charged, specifically including:

[0022] Calculate the gradient of the objective function with respect to the transmit beam focusing vector, multiply the gradient by the iteration step size and sum it with the temporary transmit beam focusing vector of the previous iteration to obtain the temporary transmit beam focusing vector of the current iteration, project the temporary transmit beam focusing vector of the current iteration onto the unit modulus value, and repeat the above alternating quantization refinement steps until convergence, to obtain the transmit beam focusing vector with continuous phase values.

[0023] The continuous phase value is extracted from the focusing vector of the transmitted beam with continuous phase value. The discrete phase value that is closest to the extracted continuous phase value is found in the discrete phase set to obtain the optimal quantized phase corresponding to the device to be charged.

[0024] Optionally, determining the location information of the device to be charged based on the radio frequency guidance signal includes:

[0025] The direction of arrival of the radio frequency (RF) pilot signal is calculated based on the RF pilot signal.

[0026] Calculate the average power of the received signal from the receiving antenna array, and estimate the distance between the transmitting antenna array and the device to be charged based on the average power of the received signal;

[0027] The location information of the device to be charged is determined based on the direction of arrival of the radio frequency guidance signal and the distance between the transmitting antenna array and the device to be charged.

[0028] Optionally, the direction of arrival of the radio frequency (RF) pilot signal can be calculated based on the RF pilot signal, including:

[0029] Calculate the received signal vector of the antenna element in the receiving antenna array based on the radio frequency pilot signal;

[0030] Calculate the covariance matrix of the received signal vector;

[0031] The covariance matrix is ​​decomposed into eigenvalues, and the eigenvalues ​​are sorted in descending order. The spatial spectrum is constructed from the eigenvalues ​​greater than the eigenvalue threshold using the following formula:

[0032]

[0033] in, V represents the spatial spectrum. X φ represents the feature matrix consisting of the eigenvectors corresponding to the eigenvalues ​​greater than the eigenvalue threshold in the covariance matrix. X,s This represents the estimated direction of the s-th device reaching the receiving antenna array. This represents the steering vector of the receiving antenna array for estimating the direction of the s-th device reaching the receiving antenna array; [·] H Represents the conjugate transpose operator;

[0034] The arrival direction of the radio frequency guiding signal is extracted from the angle corresponding to the spectral peaks of the spatial spectrum.

[0035] A second aspect of this application provides a multi-device energy beam focusing device for a UAV wireless energy transfer system, implementing the aforementioned multi-device energy beam focusing method for a UAV wireless energy transfer system, the device comprising:

[0036] The receiving module is used to receive the radio frequency guidance signals transmitted by each device to be charged;

[0037] The estimation module is used to determine the location information of each device to be charged based on the radio frequency guidance signal;

[0038] The calculation module is used to calculate the focusing vector of the transmitted beam based on the location information of the device to be charged;

[0039] The transmitting module is used to generate a focused beam for multiple devices to be charged by using an energy source according to the beam focusing vector.

[0040] A third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when loaded onto the processor, implements the aforementioned multi-device energy beam focusing method for a UAV wireless energy transfer system.

[0041] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-device energy beam focusing method for a UAV wireless energy transfer system.

[0042] Compared with the prior art, the beneficial effects of this application include at least the following:

[0043] This application involves the devices to be charged sequentially transmitting radio frequency (RF) guidance signals to a wireless power transmission system in a predetermined order. The wireless power transmission system uses a Γ-type receiving antenna array to receive the RF guidance signals and estimate the spatial orientation and distance of each device. Then, the wireless power transmission system constructs a near-field channel model based on the location information of each device. Finally, the wireless power transmission system calculates the optimal transmit beam focusing vector of the VMI array based on the near-field channel model, simultaneously or in batches, to achieve beam focusing and power transmission for multiple devices. This enables efficient power transmission to multiple devices in the near-field region of energy RF radiation as needed, reducing processing overhead, mitigating latency, and reducing electromagnetic energy pollution to devices in non-target areas. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0045] Figure 1 This is a schematic diagram of a WPT system model provided in an embodiment of this application;

[0046] Figure 2 This is a schematic diagram of an energy harvester structure provided in an embodiment of this application;

[0047] Figure 3 This is a schematic diagram of the weighted logarithm of the collected energy and its variation with the number of bits of the digital phase quantization phase shifter, provided in an embodiment of this application.

[0048] Figure 4 This is a schematic diagram of the energy beam focusing effect of the device equal-weight SCA algorithm provided in an embodiment of this application;

[0049] Figure 5 This is a schematic diagram of the energy beam focusing effect of an equal-weight AQR algorithm provided in an embodiment of this application;

[0050] Figure 6 This is a schematic diagram of the energy beam focusing effect of the AQR algorithm under different device priorities provided in an embodiment of this application;

[0051] Figure 7 This is a schematic diagram of the energy beam focusing effect of the AQR algorithm under different device priorities provided in another embodiment of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. The embodiments described in this application are merely some embodiments of this application, and not all embodiments. Based on the spirit of this application, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this application.

[0053] Combination Figure 1 As shown in the figure, this application provides a multi-device energy beam focusing method for a UAV wireless energy transfer system. The UAV energy transfer system includes an energy source, a transmitting antenna array, and a receiving antenna array. The method includes:

[0054] The receiving antenna array receives the radio frequency guidance signals transmitted by each device to be charged;

[0055] The drone uses radio frequency guidance signals to determine the location information of each device to be charged;

[0056] The drone calculates the focusing vector of the transmitted beam based on the location information of the device to be charged.

[0057] The transmitting antenna array uses an energy source to generate a focused beam for multiple devices to be charged, according to the focusing vector of the transmitting beam.

[0058] In this embodiment, by equipping a small-scale receiving antenna array, the location information of the device to be charged is estimated using the same number of radio frequency guidance signals as the number of devices, and then the transmit beam focusing vector is estimated. Compared with the prior art, which often requires the calculation of high-order statistics, resulting in large overhead and delay, the present application only uses a small-scale receiving antenna array to determine the transmit beam focusing vector, thus reducing the amount of calculation, processing overhead and delay.

[0059] Preferably, but not limitingly, the receiving antenna array is arranged around the periphery of the transmitting antenna array.

[0060] Preferably, but not limitingly, the receiving antenna array includes a horizontal linear receiving array and a vertical linear receiving array, wherein the horizontal linear receiving array is arranged horizontally around the transmitting antenna array, and the vertical linear receiving array is arranged vertically around the transmitting antenna array.

[0061] Specifically, a drone carrying a power source and antenna array is used to directionally charge multiple devices via radio frequency. The antenna array consists of two parts: one is an ELAA (Electronic Energy Alignment Array), configured with N... 2One part consists of four antenna elements forming a square transmitting antenna array structure to generate multiple energy-focusing beams to charge the equipment. The other part is a Г-shaped RAA (Receiving Antenna Array) deployed around the perimeter of the ELAA, used to receive radio frequency guidance signals transmitted from the equipment. Through signal processing, the equipment's location information is obtained, and then the energy beam focusing vector is calculated. K antenna elements are equipped horizontally and vertically respectively, forming two linear receiving antenna array structures, where K is much smaller than N. 2 .

[0062] Combination Figure 2 As shown, each device is equipped with an Energy Harvester (EH) to harvest energy from the WPT system. The energy harvester includes a rectifier antenna, a rectifier circuit and matching network, a DC-DC converter, and an energy storage device. It receives the radio frequency energy signal transmitted from the WPT system via the rectifier antenna, converts it into a DC signal using the rectifier circuit and matching network, and finally uses the DC-DC converter to convert the DC signal into a DC voltage suitable for the energy storage device specifications.

[0063] More specifically, a three-dimensional Cartesian coordinate system is established with the center of the square ELAA plane as the origin O, the horizontal direction to the right as the positive X-axis, the vertical direction upward as the positive Y-axis, and the direction perpendicular to the square ELAA plane and pointing towards the area of ​​the device to be charged as the positive Z-axis. The position coordinates of the (m,n)th antenna element of the ELAA are:

[0064]

[0065] Where, λ E It is the radio frequency wavelength of the energy signal emitted by ELAA, and N represents the number of antenna elements along the X-axis or Y-axis.

[0066] The Г-shaped RAA is formed by a translation distance λ from the X-axis. E A linear subarray Γ uniformly arranged along the X-axis at a ratio of (N-1) / 4. X The translation distance from the Y-axis is -λ E (N-1) / 4 linear subarrays Γ uniformly arranged along the Y-axis Y Composition, Γ X and Γ Y Position coordinates of the middle antenna element and They are respectively:

[0067]

[0068] Where, λ G It is the radio frequency wavelength of the guidance signal. Γ represents a linear subarray uniformly arranged along the X-axis.X The position coordinates of the i-th antenna element. Γ represents a linear subarray uniformly arranged along the Y-axis. Y The position coordinates of the j-th antenna element, where K represents the linear subarray Γ uniformly arranged along the X-axis. X Or a linear subarray Γ uniformly arranged along the Y-axis Y The number of antenna elements.

[0069] In one embodiment, the method specifically includes the following steps:

[0070] S1: The device to be charged transmits radio frequency guidance signals to the WPT system in a predetermined order, wherein the RAA is located in the far field region of the radio frequency guidance signal.

[0071] S2: After receiving the radio frequency guidance signal, the receiving antenna array estimates the location information of the device to be charged. S2 specifically includes:

[0072] S2.1: Arrival direction estimation. S2.1 specifically includes:

[0073] S2.1.1: The devices to be charged, s = 1, ..., S, sequentially transmit wavelengths of λ to the WPT system in a predetermined order. G Power is P G radio frequency guidance signal x s ;

[0074] S2.1.2: Γ of the Г-type RAA X and Γ Y The received signal vectors are as follows:

[0075]

[0076] in, It is Γ X Mid-position coordinates The received signal from the antenna. It is Γ Y Mid-position coordinates The received signal from the antenna.

[0077] S2.1.3: The equipment location estimation module of the WPT system for Γ X and Γ Y The received signal vector z X ,z Y Oversampling was performed to obtain F groups of samples z. X (f),z Y (f), f=1,…,F

[0078] S2.1.4: Calculate the covariance matrix using samples

[0079]

[0080] Among them, z X (f) represents the received signal vector z X The corresponding f-th sample, z X (f) represents the received signal vector z Y The corresponding f-th sample, where F represents the number of samples.

[0081] S2.1.5: For the covariance matrix Perform eigenvalue decomposition, sort the eigenvalues ​​in descending order, extract the eigenvalues ​​corresponding to the pilot signal power, and construct the spatial spectrum using the eigenvectors corresponding to the eigenvalues:

[0082]

[0083] Among them, V X yes The feature matrix V is composed of the eigenvectors corresponding to eigenvalues ​​greater than the eigenvalue threshold. Y yes The feature matrix consisting of the eigenvectors corresponding to eigenvalues ​​greater than the eigenvalue threshold, φ X,s This indicates that the s-th device has arrived at the linear subarray Γ. X Direction estimate, φ Y,s This indicates that the s-th device has arrived at the linear subarray Γ. Y Direction estimate, a X (φ A,s ) represents Γ X For radio frequency guidance signal x s The guidance vector of the arrival direction, a Y (φ E,s ) represents Γ Y For radio frequency guidance signal x s The guidance vector to the direction of arrival.

[0084] It should be noted that after eigenvalue decomposition, large eigenvalues ​​correspond to the received pilot signal power, and small eigenvalues ​​correspond to noise power. The signal power is significantly greater than the noise power. The maximum ratio among all eigenvalues ​​can be used as the eigenvalue threshold. When the ratio is maximum, all eigenvalues ​​preceding it are the large eigenvalues ​​(i.e., signal power), and all eigenvalues ​​following it are the small eigenvalues ​​(i.e., noise power).

[0085] Γ X and Γ Y For radio frequency guidance signal x s The guiding vector a formed by the direction of arrival X (φ A,s ),a Y (φE,s )for:

[0086]

[0087] S2.1.6: Search Space Spectrum By determining the angle corresponding to the spectral peak, the direction φ of the RF guidance signal transmitted by the s-th device reaching the RAA can be obtained. X,s and φ Y,s .

[0088] Understandably, the steering vector is a mathematical vector that describes the phase and amplitude response of an antenna array to an incident signal at a specific spatial angle, representing the reception state of the signal at a specific angle on each antenna element of the array.

[0089] Understandably, the spatial spectrum is used to quantify the matching degree between different spatial angles and the received signal, transforming the angle estimation problem into a power spectrum peak search problem. By calculating the signal power response value at each possible angle, the angle with the largest response value, i.e. the spectrum peak, is the actual direction of arrival of the device.

[0090] S2.2: Distance estimation between the device to be charged and ELAA, specifically including:

[0091] Calculate the average power of the received signal from the receiving antenna array, and estimate the distance between the transmitting antenna array and the device to be charged based on the average power of the received signal.

[0092] For Γ X The midpoint coordinates are and The antenna received signal power is averaged to obtain Γ X Average power of received signal For Γ Y The midpoint coordinates are and The antenna received signal power is averaged to obtain Γ Y Average power of received signal

[0093]

[0094] The distance between the receiving antenna array and the device to be charged can be calculated by using the path loss model:

[0095] Γ X and Γ Y Distance to the s-th device waiting to be charged and Right now

[0096]

[0097] in, Indicates ΓX Distance to the s-th device waiting to be charged Indicates Γ Y Distance to the s-th device waiting to be charged, P G This indicates the power of the radio frequency guidance signal emitted by the device to be charged; this is the same for all devices.

[0098] S2.3: Determine the location information of the device to be charged based on the arrival direction of the radio frequency guidance signal and the distance between the transmitting antenna array and the device to be charged.

[0099] Specifically, the receiving antenna array is projected as the distance between the center of the transmitting array and the s-th device to be charged, using the estimated direction of arrival:

[0100]

[0101] in, φ represents the distance between the center of the transmitting array and the s-th device to be charged. X,s This indicates that the s-th device has arrived at the linear subarray Γ. X Direction estimate, φ Y,s This indicates that the s-th device has arrived at the linear subarray Γ. Y The estimated direction.

[0102] use Estimate the position coordinates of the equipment s = 1, ..., S:

[0103]

[0104] in, This represents the location information of the s-th device waiting to be charged.

[0105] S3: Construct a near-field channel model based on the location information of the device to be charged, and obtain the vectorized channel coefficient estimates from the transmitting antenna array to the device to be charged. S3 specifically includes:

[0106] S3.1: Estimate the channel coefficient from the (m,n)th transmit antenna of ELAA to the s-th device.

[0107] in, Let represent the estimated channel coefficient from the (m,n)th transmit antenna of ELAA to the sth device. p represents the estimated location of the s-th device to be charged. m,n This represents the position information of the (m,n)th antenna element in the ELAA. Let λ represent the estimated distance from the (m,n)th transmitting antenna of ELAA to the s-th device to be charged. E It is the radio frequency wavelength of the energy signal emitted by ELAA.

[0108] The estimated distance from the (m,n)th transmitting antenna of ELAA to the sth device to be charged is calculated using the following formula:

[0109]

[0110] S3.2: Estimated vectorized channel coefficients from ELAA to the s-th device for:

[0111]

[0112] in, Let represent the vectorized channel coefficient estimate from ELAA to the s-th device, [·] T It is the transpose operator.

[0113] In this embodiment, the direction of arrival is estimated using the same number of radio frequency guidance signals as the number of devices, and the transmission distance is estimated using simple received signal power. The computational load is small, and multiple devices can be used to focus energy beams simultaneously with low processing overhead and low latency.

[0114] S4: Based on the near-field channel model, the Alternating Quantization-Refinement (AQR) algorithm is used to calculate the transmit beam focusing vector w of ELAA, and beam focusing and energy transmission of multiple devices can be achieved simultaneously or in batches.

[0115] Based on the location information of the device to be charged, the transmitting antenna array uses an energy source to generate multiple energy-focusing beams, including:

[0116] A near-field channel model is constructed based on the location information of the device to be charged, and the vectorized channel coefficient estimates from the transmitting antenna array to the device to be charged are obtained.

[0117] The transmit beam focusing vector of the transmit antenna array is initialized based on the vectorized channel coefficient estimates;

[0118] The energy collected by the device to be charged is calculated based on the vectorized channel coefficient estimates and the beamforming vector.

[0119] The objective function is to maximize the weighted logarithmic sum of the collected energy. The objective function is solved by combining the maximum transmit power constraint of the transmitting antenna array, the quantization phase constraint, and the near-field boundary constraint of the transmitting antenna array, so as to obtain the optimal quantization phase corresponding to the device to be charged.

[0120] The optimal transmit beam focusing vector is obtained based on the optimal quantization phase, and the transmit antenna array uses the energy source to generate multiple energy focusing beams according to the optimal transmit beam focusing vector.

[0121] Preferably, but not limitingly, the transmitting antenna array is a square array structure, and the transmitting antenna array is divided into M transmitting antenna subarrays. If the number of devices to be charged is not greater than M, the corresponding number of transmitting antenna subarrays are activated to generate a focused beam for each device to be charged. If the number of devices to be charged is greater than M, the number of devices to be charged is divided into batches in units of M, and a focused beam for each batch of devices is generated simultaneously. M is a positive integer greater than 1.

[0122] In this embodiment, the symmetrical distribution of the square antenna elements ensures a more uniform power density at the energy focus, avoids the problem of power attenuation at the edge focus, ensures consistent charging efficiency for devices in different locations, and improves energy transmission efficiency.

[0123] S4 specifically includes:

[0124] S4.1: Divide the square ELAA structure into 4 sub-ELAAs, corresponding to the 4 quadrants of the XOY plane respectively. Each sub-ELAA generates an energy focusing beam for one device.

[0125] S4.2: Divide the number of devices to be charged into batches G, with each batch consisting of 4 devices.

[0126]

[0127] in, This indicates the rounding up operation.

[0128] S4.3: If the number of devices to be charged S≤4, activate the corresponding number of sub-ELAAs to generate energy focusing beams for each device simultaneously. If the number of devices to be charged S>4, generate energy focusing beams for each batch of devices simultaneously according to batch G.

[0129] S4.4: Calculate the energy beam focusing vector for each device in each batch based on the alternating quantization refinement algorithm. Taking the first batch (G=1) and the first sub-ELAA as an example, the calculation of the energy beam focusing vectors for the remaining batches and sub-ELAAs is similar:

[0130] S4.4.1: According to the optimal transmit beam focusing vector, w = (h s ) H The rule is to use the estimated vectorized channel coefficient estimates. Initialize the transmit beam focusing vector

[0131] S4.4.2: Set the iteration step size η, and construct the discrete phase set Q = {2πk / 2} based on the phase quantization bit number b of the digital phase shifter. b |k=0,1,…,2 b-1}, design the objective function, and solve the objective function by combining the maximum transmit power constraint, quantization phase constraint and near-field boundary constraint of ELAA to obtain the optimal quantization phase of the device to be charged.

[0132]

[0133] C1: 0 ≤ P E ≤P max

[0134]

[0135] Where, ω s This represents the weight corresponding to the s-th device waiting to be charged, and is a weight coefficient characterizing the priority of energy acquisition by the device. ω>0 is a very small constant, set empirically; P DC (w,r s ) represents the DC power collected by the energy harvester of the s-th device to be charged, w represents the beam focusing vector of the ELAA, and r s This represents the location information of the s-th device to be charged; C1 represents the maximum transmit power constraint of the ELAA, C2 represents the quantization phase constraint, C3 represents the near-field boundary constraint of the ELAA, and P E P represents the radio frequency transmit power of each transmit antenna in ELAA. max θ represents the maximum transmit power of ELAA. i Let Q represent the phase of the i-th transmit antenna phase shifter, Q represent the discrete phase set, b represent the number of bits for phase quantization of the digital phase shifter, k represent the index of the discrete phase, and D represent the effective aperture of the square ELAA.

[0136] It should be noted that ELAA's maximum transmit power budget is limited by hardware configuration and safety regulations. The output power of the power amplifiers driving the antenna units is limited, and in order to optimize system energy efficiency, ensure thermal management, and comply with electromagnetic radiation safety regulations, the RF transmit power P of each ELAA transmit antenna is limited. E The maximum transmit power constraint C1 must be met.

[0137] The performance of near-field energy focusing using ELAA depends on the phase adjustment accuracy of the phase shifter. Considering cost, practical digital phase shifters can only achieve a limited number of phase quantization bits. The phase θ of the i-th transmit antenna phase shifter... i Only from 2 b The beam must be selected from a set of discrete phases, thus satisfying the quantization phase constraint C2. This limited phase resolution introduces inherent phase quantization errors, causing the actual focused beam to deviate from the desired value and reducing the energy harvesting efficiency of the device's energy harvester. Therefore, it is necessary to approximate the optimal value as closely as possible through an iterative approach.

[0138] The system design is based on a specific environment, namely, the energy harvester of the device is located in the near-field region of ELAA radiation, at an energy radio frequency wavelength λ. E Below, the distance between ELAA and the s-th energy harvester. The near-field boundary constraint C3 of ELAA is satisfied. Within the radio frequency energy radiation region defined by this constraint, the electromagnetic wavefront exhibits spherical wave propagation characteristics. ELAA can utilize a large aperture to focus electromagnetic energy onto a specific spatial point, generating a localized high power density.

[0139] In this embodiment, the objective function is sampled in the form of a weighted logarithmic sum, rather than a simple weighted linear sum. This avoids high-priority devices from preempting too much energy, while low-priority devices approach zero and cannot charge. This application ensures that devices with poor channel conditions can collect baseline-level power. By adjusting the weights, differentiated power transmission is achieved, which satisfies the energy needs of high-priority devices without ignoring low-priority devices, enabling multiple devices to perform energy beam focusing simultaneously.

[0140] Furthermore, the power collected by the energy harvester in the existing model is a sigmoid function. Therefore, the objective function set based on the power collected by the energy harvester in the existing model exhibits non-convex and non-concave characteristics, which cannot be solved by traditional convex optimization techniques. However, this application uses the weighted logarithmic sum of the collected energy as the objective function, transforming the problem that cannot be solved by traditional convex optimization techniques into a smooth convex function. The discrete phase is relaxed to a continuous interval and then projected back to discrete, thereby obtaining a quantized phase solution with the minimum performance loss and the highest quality, thus improving the energy beam focusing efficiency of multi-devices.

[0141] Specifically, the DC power P collected by the energy harvester of the s-th device to be charged DC (w,r s Obtain it by following these steps:

[0142] (1) Calculate the received signal of the collector rectifier antenna of the s-th device to be charged as follows:

[0143]

[0144] Among them, P E is the RF transmit power of each transmit antenna of the ELAA, w is the beam focusing vector of the ELAA, [·] H It is the conjugate transpose operator.

[0145] (2) Calculate the beam focusing vector of ELAA:

[0146]

[0147] Where, θ ii = (-(N-1) / 2, -(N-1) / 2), ..., 0, ..., ((N-1) / 2, (N-1) / 2) represents the phase value of the phase shifter in each antenna element of the ELAA. For a b-bit digital phase shifter, its value is selected from 2... b A set of discrete phases.

[0148] (3) The received power P of the rectifier antenna of the energy harvester of the s-th device RF (w,r s ):

[0149] P RF (w,r s )=||y s || 2

[0150] =w H h s (h s ) H wP E

[0151] (4) The radio frequency signal is converted into a DC signal through a rectifier circuit and a matching network. The DC power P collected by the energy harvester of the s-th device is... DC (w,r s )for:

[0152]

[0153] Ω=1 / (1+exp(a1×a2))

[0154]

[0155] Where M is a constant related to the saturation current of the energy harvester rectifier circuit, and a1 and a2 are two fixed parameters related to the structure of the energy harvester rectifier circuit.

[0156] S4.4.3: Calculate the gradient of the objective function J with respect to the transmitted beam focusing vector w:

[0157]

[0158] Update temporary vector w temp ←w (i-1) +η×g, Perform unit modulus projection:

[0159] w (i) ←exp(j∠w temp ) / N

[0160] Repeat the process until convergence, obtaining the transmit beam focusing vector w with continuous phase values. cont ←w (i) .

[0161] Where η represents the iteration step size, used to balance the convergence speed and control accuracy of the beam focusing vector update, w (i-1) Let represent the temporary beam focusing vector at the (i-1)th iteration, j represent the imaginary part of the complex number, and ∠w temp This represents the phase value of the extracted temporary beam focusing vector, and N represents the number of antenna elements in the horizontal or vertical direction of the ELAA.

[0162] S4.4.4: From w cont Extracting continuous phase values ​​∠w cont Iterate through the discrete phase set Q to find the value of the continuous phase ∠w. cont The closest discrete phase value:

[0163]

[0164] in,

[0165] S4.4.5: Calculate the transmit beam focusing vector with discrete phase values:

[0166]

[0167] S4.4.6: Calculate the objective function J with respect to... Calculate the gradient and update the temporary vector:

[0168]

[0169] S4.4.7: Combining the phase constraint conditions, from Extracting phase Traverse and search the discrete phase set Q to find the phase that is related to the discrete phase set Q. The closest discrete phase value:

[0170]

[0171] S4.4.8: Calculate the final transmit beam focusing vector with discrete phase values:

[0172]

[0173] Embodiment 2 of this application provides an application embodiment of the multi-device beam focusing method for a UAV wireless power transfer system provided in Embodiment 1, namely, a WPT system embodiment based on ELAA. The specific settings are as follows: a square ELAA is configured with N = 129 × 129 antenna elements, spaced at intervals λ along the horizontal and vertical directions. EThe 129×129 square ELAA is divided into four 64×64 sub-ELAAs by uniformly arranging the ELAAs. This allows for simultaneous energy focusing beams for four devices. The number of antenna elements in the horizontal and vertical linear array configuration of the Г-type RAA is K=16. The three devices to be charged are located in the radiation near-field region of the ELAA, 10-15m away from the center of the ELAA. Other parameter settings are shown in Table 1.

[0174] Table 1

[0175]

[0176] The SCA (Successive Convex Approximation) algorithm and the IGPO (Iterative Greedy Phase Optimization) algorithm, both based on perfect CSI (Channel State Information), were selected as benchmark algorithms to evaluate the performance of the AQR algorithm based on alternating quantization refinement. The SCA algorithm can achieve local optima with known perfect channel information, while the IGPO algorithm iteratively searches for the beam focusing vector based on the received power fed back by the device. Figure 3 The figure shows the weighted power collected by three devices under three different algorithms and the curves showing the change in the number of bits of phase quantization in the digital phase shifter. It can be seen that, compared to the SCA algorithm based on continuous phase, the energy transfer capabilities of the IGPO and AQR algorithms are closely related to the phase resolution of the phase shifter. When the phase quantization bit is 1 bit, the AQR algorithm exhibits significantly suboptimal performance. This is a direct consequence of coarse quantization; the limited number of only two phase states severely constrains the search space for optimizing the beam focusing vector, fundamentally limiting the ability to converge to the optimal energy beam focusing vector. As the number of bits of phase quantization increases, a substantial improvement is made to the energy collection capability of the devices. This improvement is attributed to the expanded size of the discrete phase set, allowing for the exploration of finer discrete phase values ​​and enabling more accurate refinement of the beam focusing vector. When the number of bits of phase quantization increases to 4 bits, the performance of the AQR algorithm is almost identical to that of the SCA algorithm, with the performance gap narrowing to less than 2%. This indicates that a digital phase shifter using 4-bit quantization can achieve near-continuous phase, which is the optimal performance. This means that the discrete phase set based on 4-bit quantization is sufficient to approximate a continuous solution domain. Therefore, a more cost-effective digital phase shifter with moderate phase resolution can be equipped in the WPT system, without the need for a high-cost high-resolution digital phase shifter, thus reducing processing overhead.

[0177] Figure 4 , Figure 5 , Figure 6 and Figure 7The image shows the energy beam focusing effect under different device priorities. Figure 3 Three key performance characteristics were revealed: accurate beam focusing, adaptive power distribution control, and radiation near-field region constraint. Figures 5 to 7 This demonstrates the AQR algorithm's ability to adjust the power distribution received by the energy harvester through strategic weight adjustments to the objective function. Specifically, Figure 6 The weights are set to 0.5:0.3:0.2, which can be obtained from... Figure 6 This shows that under this weight, the received power of UE1 is given priority; while... Figure 4 With a weighting of 0.33:0.33:0.33, power was distributed as evenly as possible among the energy harvesters. Figure 7 With a weighting of 0.6:0.2:0.2, the received power of UE1 was further enhanced, while the power to UE2 and UE3 was reduced, demonstrating the practical value of the algorithm in adaptive power allocation.

[0178] Embodiment 3 of this application provides a multi-device energy beam focusing system for a UAV wireless energy transfer system, which operates the multi-device energy beam focusing method for a UAV wireless energy transfer system as described in Embodiment 1. The system includes:

[0179] The receiving module is used to receive the radio frequency guidance signal transmitted from the device to be charged by the antenna array;

[0180] The estimation module determines the location information of the device to be charged based on the radio frequency guidance signal;

[0181] The transmitting energy module calculates the transmitting beam focusing vector based on the location information of the device to be charged. The transmitting antenna array uses the energy source to generate multiple energy focusing beams according to the transmitting beam focusing vector to simultaneously charge multiple devices to be charged.

[0182] Regarding the system in the above embodiments, the specific manner in which each unit performs operations has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0183] Embodiment 4 of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the multi-device energy beam focusing method of the UAV wireless energy transfer system described in Embodiment 1.

[0184] Embodiment 5 of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the multi-device energy beam focusing method for a UAV wireless energy transfer system according to Embodiment 1.

[0185] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0186] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit them. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of this application. Any modifications or equivalent substitutions that do not depart from the spirit and scope of this application should be covered within the protection scope of the claims of this application.

Claims

1. A method for focusing energy beams across multiple devices in a UAV wireless power transfer system, wherein the UAV power transfer system comprises an energy source, a transmitting antenna array, and a receiving antenna array, characterized in that, The method includes: The receiving antenna array receives the radio frequency guidance signals transmitted by each device to be charged; The drone uses radio frequency guidance signals to determine the location information of each device to be charged; The drone calculates the focusing vector of the transmitted beam based on the location information of the device to be charged. The transmitting antenna array uses an energy source to generate a focused beam for multiple devices to be charged, according to the focusing vector of the transmitting beam.

2. The multi-device energy beam focusing method for a UAV wireless energy transfer system according to claim 1, characterized in that: The transmitting antenna array is a square array structure. The transmitting antenna array is divided into M transmitting antenna subarrays. If the number of devices to be charged is no more than M, the corresponding number of transmitting antenna subarrays are activated to generate a focused beam for each device to be charged. If the number of devices to be charged is greater than M, the number of devices to be charged is divided into batches in units of M. A focused beam for each batch of devices is generated simultaneously. M is a positive integer greater than 1.

3. The multi-device energy beam focusing method for a UAV wireless energy transfer system according to claim 1, characterized in that: The receiving antenna array is positioned around the periphery of the transmitting antenna array.

4. The multi-device energy beam focusing method for a UAV wireless energy transfer system according to claim 3, characterized in that: The receiving antenna array includes a horizontal linear receiving array and a vertical linear receiving array. The horizontal linear receiving array is arranged horizontally around the transmitting antenna array, and the vertical linear receiving array is arranged vertically around the transmitting antenna array.

5. The multi-device energy beam focusing method for a UAV wireless energy transfer system according to claim 1, characterized in that: Based on the location information of the device to be charged, the transmit beam focusing vector is calculated. The transmit antenna array uses an energy source to generate multiple energy-focused beams according to the transmit beam focusing vector, including: A near-field channel model is constructed based on the location information of each device to be charged, and the vectorized channel coefficient estimates from the transmitting antenna array to each device to be charged are obtained. The transmit beam focusing vector of the transmit antenna array is initialized based on the vectorized channel coefficient estimates; The energy collected by each device to be charged is calculated based on the vectorized channel coefficient estimate and the transmit beam focusing vector. The objective function is to maximize the weighted logarithmic sum of the energy collected by all devices. The objective function is then solved by combining the maximum transmit power constraint of the transmitting antenna array, the quantization phase constraint, and the near-field boundary constraint to obtain the optimal quantization phase for each device to be charged. The optimal transmit beam focusing vector is obtained based on the optimal quantization phase, and the transmit antenna array uses the energy source to generate multiple energy focusing beams according to the optimal transmit beam focusing vector.

6. The multi-device energy beam focusing method for a UAV wireless energy transfer system according to claim 5, characterized in that: The objective function is calculated and solved based on the alternating quantization refinement algorithm to obtain the optimal quantization phase for each device to be charged, specifically including: Calculate the gradient of the objective function with respect to the transmit beam focusing vector, multiply the gradient by the iteration step size and sum it with the temporary transmit beam focusing vector of the previous iteration to obtain the temporary transmit beam focusing vector of the current iteration, project the temporary transmit beam focusing vector of the current iteration onto the unit modulus value, and repeat the above alternating quantization refinement steps until convergence, to obtain the transmit beam focusing vector with continuous phase values. The continuous phase value is extracted from the focusing vector of the transmitted beam with continuous phase value. The discrete phase value that is closest to the extracted continuous phase value is found in the discrete phase set to obtain the optimal quantized phase corresponding to the device to be charged.

7. The multi-device energy beam focusing method for a UAV wireless energy transfer system according to claim 1, characterized in that: Determining the location information of the device to be charged based on radio frequency guidance signals includes: The direction of arrival of the radio frequency (RF) pilot signal is calculated based on the RF pilot signal. Calculate the average power of the received signal from the receiving antenna array, and estimate the distance between the transmitting antenna array and the device to be charged based on the average power of the received signal; The location information of the device to be charged is determined based on the direction of arrival of the radio frequency guidance signal and the distance between the transmitting antenna array and the device to be charged.

8. The multi-device energy beam focusing method for a UAV wireless energy transfer system according to claim 7, characterized in that: The direction of arrival of the radio frequency (RF) pilot signal, calculated based on the RF pilot signal, includes: Calculate the received signal vector of the antenna element in the receiving antenna array based on the radio frequency pilot signal; Calculate the covariance matrix of the received signal vector; The covariance matrix is ​​decomposed into eigenvalues, and the eigenvalues ​​are sorted in descending order. The spatial spectrum is constructed from the eigenvalues ​​greater than the eigenvalue threshold using the following formula: in, V represents the spatial spectrum. X φ represents the feature matrix consisting of the eigenvectors corresponding to the eigenvalues ​​greater than the eigenvalue threshold in the covariance matrix. X,s This represents the estimated direction of the s-th device reaching the receiving antenna array. This represents the steering vector of the receiving antenna array for estimating the direction of the s-th device reaching the receiving antenna array; [·] H Represents the conjugate transpose operator; The arrival direction of the radio frequency guiding signal is extracted from the angle corresponding to the spectral peaks of the spatial spectrum.

9. A multi-device energy beam focusing device for a UAV wireless power transfer system, implementing the multi-device energy beam focusing method for a UAV wireless power transfer system according to any one of claims 1-8, characterized in that, The device includes: The receiving module is used to receive the radio frequency guidance signals transmitted by each device to be charged; The estimation module is used to determine the location information of each device to be charged based on the radio frequency guidance signal; The calculation module is used to calculate the focusing vector of the transmitted beam based on the location information of the device to be charged; The transmitting module is used to generate a focused beam for multiple devices to be charged by using an energy source according to the beam focusing vector.

10. An electronic device, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the multi-device energy beam focusing method for a UAV wireless energy transfer system according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the multi-device energy beam focusing method for a UAV wireless energy transfer system according to any one of claims 1-8.

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