Charging time minimization method of WP-IoT based on intelligent reflecting surface

Abstract

The invention discloses a WP-IoT charging time minimization method based on an intelligent reflection surface, and relates to the technical field of wireless power supply, and the method comprises the steps: obtaining the number of hybrid access points and the number of Internet of Things devices in a region; acquiring information of the hybrid access point, the intelligent reflecting surface and the Internet of Things equipment; constructing a coordinate system about the region by taking the hybrid access point as an original point, acquiring coordinates of the Internet of Things equipment and the intelligent reflecting surface in the corresponding coordinate system, and dividing the Internet of Things equipment in the region into a plurality of clusters; the node coordinates of the sensor nodes in each cluster are obtained, a complex problem is decomposed into a plurality of sub-problems to facilitate respective solving and optimization, by introducing slack variables and positive semi-definite programming, a non-convex problem is converted into a convex problem or an easier-to-process form, the solving efficiency is improved, and the method is easy to implement. Meanwhile, multiple factors such as beam forming, equipment transmitting power, IRS phase shift and time scheduling are considered, and the overall performance of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless power supply, and in particular to a method for minimizing the charging time of a WP-IoT based on a smart reflective surface. Background Art

[0002] Smart reflective surfaces are a new type of wireless communication technology that uses large-scale reflective elements, such as tiny antennas or phase adjustment elements, to construct a dynamically controllable surface. These reflective elements can control the reflection, propagation and reception of wireless signals by changing their phase, amplitude or polarization state as needed.

[0003] In wireless communication systems, smart reflective surfaces can be used as an infrastructure to enhance or optimize the propagation of wireless signals. For example, by regulating smart reflective surfaces, the propagation path of wireless signals can be changed, the coverage can be increased, interference can be reduced, and the performance of the communication system can be improved.

[0004] Although there have been a lot of research on smart reflective surface-assisted WP-IOT networks, so far, most of the research work has been conducted under the assumption that the hybrid access point is fixed in position. In the actual environment, the hybrid access point can be flexibly moved to replenish energy and collect information for IoT devices in the network on demand; the deployment of smart reflective surfaces is to adjust the energy reflection signal to avoid signal attenuation due to long distances or obstacles, so that an efficient charging area can be constructed; and few research works consider time optimization issues, and charging time is one of the important factors in maintaining efficient network operation. Therefore, the present invention proposes a charging time minimization method for WP-IoT based on smart reflective surfaces. Summary of the invention

[0005] In order to solve the above technical problems, a method for minimizing the charging time of WP-IoT based on smart reflective surface is provided, which solves the problem that most of the above research works are carried out under the assumption that the position of the hybrid access point is fixed, while in the actual environment, the hybrid access point can be flexibly moved.

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is: A method for minimizing charging time of WP-IoT based on a smart reflective surface, comprising: Get the number of hybrid access points and IoT devices in the area; Get information about hybrid access points, smart reflective surfaces, and IoT devices; Taking the hybrid access point as the origin, a coordinate system about the region is constructed, and the coordinates of the IoT devices and the smart reflective surface in the corresponding coordinate system are obtained, and the IoT devices in the region are divided into multiple clusters; Obtain the node coordinates of the sensor nodes in each cluster, and obtain multiple channel parameters based on the node coordinates and known information; According to multiple channel parameters and in combination with the information of the intelligent reflective surface, the node receiving signal, the node receiving power and the node energy of the node are obtained; Extract the energy transmission time of the hybrid access point to all IoT devices, and obtain the information transmission time of each cluster of IoT devices to the hybrid access point. Based on the known information, obtain the signal received by the hybrid access point from the sensor, the signal-to-noise ratio of the IoT device at the hybrid access point, and the data transmission speed of each cluster of devices; Obtain the time consumed by each cluster in energy transmission and information transmission, and obtain the optimal solution based on the consumption time.

[0007] Preferably, the steps of constructing a coordinate system about the region with the hybrid access point as the origin, obtaining the coordinates of the IoT device and the smart reflective surface in the corresponding coordinate system, and dividing the IoT devices in the region into multiple clusters include the following steps: A three-dimensional coordinate system is constructed with the hybrid access point as the origin; Obtain the coordinates of the IoT device and the smart reflective surface in each coordinate system respectively; The IoT devices are divided into multiple clusters according to the coordinates of each IoT device; Among them, the coordinates of the smart reflection surface are .

[0008] Preferably, the step of obtaining the node coordinates of the sensor nodes in each cluster and obtaining a plurality of channel parameters according to the node coordinates and known information comprises the following steps: Get the node coordinates of the sensor nodes in each cluster; Get the path loss per unit distance; respectively obtaining the distances from the hybrid access point to the intelligent reflective surface, the distances from the intelligent reflective surface to the node, and the distances from the hybrid access point to the node; Get the distance between the IoT device and the antenna in the hybrid access point; Obtain the carrier wavelength of signal transmission; According to the node coordinates and the acquired information, the channels from the hybrid access point to the intelligent reflection surface, the channels from the intelligent reflection surface to the node, and the channels from the hybrid access point to the node are acquired respectively; The channel calculation formulas from the hybrid access point to the intelligent reflective surface, from the intelligent reflective surface to the node, and from the hybrid access point to the node are: ; In the formula, is the distance from the hybrid access point to the smart reflective surface, is the distance from the smart reflector to the node, is the distance from the hybrid access point to the node, is the channel from the hybrid access point to the smart reflective surface, is the channel from the smart reflector to the node, is the channel from access point to node, is the carrier wavelength, is the antenna distance, is the cosine value of the signal departure angle of the hybrid access point-intelligent reflector link, is the cosine value of the signal departure angle of the smart reflector-node link, is the path loss per unit distance.

[0009] Preferably, the step of respectively obtaining the distances from the hybrid access point to the intelligent reflecting surface, the distances from the intelligent reflecting surface to the node, and the distances from the hybrid access point to the node comprises the following steps: The specific calculation formulas for respectively obtaining the distances from the hybrid access point to the intelligent reflective surface, from the intelligent reflective surface to the node, and from the hybrid access point to the node are: ; In the formula, is the initial coordinate of the hybrid access point in the xy coordinate system, is the coordinate of the smart reflective surface in the xy coordinate system, is the z-axis coordinate of the smart reflective surface in the xyz coordinate system, is the coordinate of the node in the xy coordinate system.

[0010] Preferably, the method of obtaining the energy collected by the IoT device, the node receiving power and the node energy based on the multiple channel parameters and the information of the smart reflective surface comprises the following steps: Get the energy reflection beamforming matrix of the intelligent reflection face hybrid access point; Obtain the duration of the incident radio frequency signal reflected by the smart reflective surface to the IoT device with energy harvesting function; Extracting energy transmission power and energy signal of hybrid access point; Get the additive Gaussian white noise at the node; Get the transmission beam vector; Based on the known information and the acquired information, the energy collected by the IoT device, the node receiving power and the node energy are obtained respectively; Among them, the calculation formula for the node receiving signal is: ; The calculation formula of the node receiving power is: ; The calculation formula of node energy is: ; In the formula, Harvesting energy for IoT devices, is the node receiving power, is the node energy, is the transmission beam vector, and , is the maximum transmit power of the hybrid access point, A diagonal array of energy-reflecting beamforming matrices facing hybrid access points for intelligent reflection. The energy signal transmitted by the hybrid access point, is the energy transmission power of the hybrid access point, is the additive Gaussian white noise at the nodes, is the duration of signal transmission.

[0011] Preferably, extracting the energy transmission time of the hybrid access point to all IoT devices, and obtaining the information transmission time of each cluster of IoT devices to the hybrid access point, and obtaining the signal received by the hybrid access point from the sensor, the signal-to-noise ratio of the IoT device at the hybrid access point, and the data transmission speed of each cluster of devices based on known information include the following steps: Extract the energy transfer time of hybrid access points to all IoT devices; Obtain the information transmission time of each cluster of IoT devices to the hybrid access point; Obtain a reflection matrix of the energy reflection beam of each cluster of IoT devices on the intelligent reflection surface; Get the transmit power of the sensor node; Get the node receiving beam; Get unit bandwidth; Based on the known information, obtain the signal received by the hybrid access point from the sensor, the signal-to-noise ratio of the IoT device at the hybrid access point, and the data transmission speed of each cluster device; The calculation formula for the signal received by the hybrid access point from the sensor, the signal-to-noise ratio of the IoT device at the hybrid access point, and the data transmission speed of each cluster device is: ; In the formula, The hybrid access point receives the signal from the sensor, The signal-to-noise ratio of IoT devices in hybrid access points, is the data transmission speed of each cluster device, is the transmit power of the sensor node, is the node receiving beam, and , A reflective matrix that reflects energy beams toward each cluster of IoT devices for intelligent reflection.

[0012] Preferably, obtaining the time consumed by each cluster in energy transmission and information transmission, and obtaining the optimal solution according to the time consumed comprises the following steps: Obtain the total time of each cluster of IoT devices in the energy transmission and information transmission stages; Construct a multi-directional constraint model based on known information; The minimum time of energy transmission and information transmission phases in the constraint model under different hybrid access points is obtained respectively; Compare the minimum time under different hybrid access points; The minimum time of different hybrid access points is the optimal charging position for each cluster; The best solution is to plan the path based on the best charging position of each cluster of different hybrid access points; Among them, the calculation formula for the total time of energy transmission and information transmission stage is: ; Among them, the minimum time of energy transfer and information transmission in the constraint model is: ; Among them, the minimum time under different hybrid access points is: .

[0013] Preferably, the step of constructing a multi-directional constraint model based on known information comprises the following steps: Obtain the transmission energy of the corresponding cluster sensor node, and make the transmission energy greater than or equal to the node energy comparison; Obtain a signal-to-noise ratio of a hybrid access point, and make the signal-to-noise ratio of the hybrid access point greater than a signal-to-noise ratio of the IoT device at the hybrid access point; Obtaining the maximum receiving power of the hybrid access point, and making the maximum receiving power of the hybrid access point greater than or equal to the transmission beam vector modulus; Extract the mode length of the received beam and the unit mode of the phase shift of the smart reflector; Construct a multi-directional constraint model based on known information; Among them, the multi-directional constraint model is: ; In the formula, is the signal-to-noise ratio of the hybrid access point, is the maximum energy receiving power of the hybrid access point.

[0014] Compared with the prior art, the advantages of the present invention are as follows: the present invention solves the complex problem by dividing it into two stages. First, according to factors such as the energy demand and location distribution of the IoT nodes, the randomly distributed IoT devices in the network are allocated nodes, and a number of cluster center locations for the mobile hybrid access point to dock are output. Subsequently, for the locations of these cluster center nodes, the paths of the cluster center nodes are planned and the locations of the nodes are output in sequence. The shortest path nodes are selected to form a Hamiltonian circuit. The mobile hybrid access point will charge the IoT devices in each cluster along the planned path. Secondly, a charging time is established. A model of minimizing the time is proposed. Since the optimization problem is coupled and non-convex, a block coordinate descent method is used to solve the above problem. The original problem is decomposed into three sub-problems, which can be solved independently when other parameters are fixed, and then solved jointly until convergence. The complex problem is decomposed into multiple sub-problems, which are convenient for solving and optimizing separately. By introducing slack variables and semi-definite programming, the non-convex problem is transformed into a convex problem or a more tractable form, which improves the solution efficiency. At the same time, multiple factors such as beamforming, device transmit power, IRS phase shift and time scheduling are considered, achieving an overall improvement in system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of steps S100-S700 in a method for minimizing charging time of WP-IoT based on a smart reflective surface proposed by the present invention; Figure 2 A flow chart of steps S301-S303 in a method for minimizing charging time of WP-IoT based on a smart reflective surface proposed by the present invention; Figure 3 This is a flow chart of steps S401-S406 in a method for minimizing charging time of WP-IoT based on a smart reflective surface proposed by the present invention; Figure 4 This is a flow chart of steps S501-S506 in a method for minimizing charging time of WP-IoT based on a smart reflective surface proposed by the present invention; Figure 5 This is a flow chart of steps S601-S607 in a method for minimizing charging time of WP-IoT based on a smart reflective surface proposed by the present invention; Figure 6 This is a flow chart of steps S701-S706 in a method for minimizing charging time of WP-IoT based on a smart reflective surface proposed by the present invention; Figure 7 This is a flow chart of steps S7021-S7025 in a method for minimizing charging time of WP-IoT based on a smart reflective surface proposed in the present invention. DETAILED DESCRIPTION

[0016] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0017] Reference Figure 1-7 As shown, a charging time minimization method of WP-IoT based on a smart reflective surface includes: S100, obtaining the number of hybrid access points and the number of IoT devices in the area; S200, obtaining information of hybrid access points, smart reflective surfaces, and IoT devices; S300, respectively constructing a coordinate system for the region with the hybrid access point as the origin, obtaining coordinates of the IoT devices and the smart reflective surface in the corresponding coordinate system, and dividing the IoT devices in the region into multiple clusters; S400, obtaining node coordinates of sensor nodes in each cluster, and obtaining multiple channel parameters according to the node coordinates and known information; S500, based on multiple channel parameters and in combination with information of the intelligent reflective surface, obtain energy collected by the IoT device, node receiving power, and node energy; S600, extracting the energy transmission time of the hybrid access point to all IoT devices, and obtaining the information transmission time of each cluster of IoT devices to the hybrid access point, and obtaining the signal received by the hybrid access point from the sensor, the signal-to-noise ratio of the IoT device at the hybrid access point, and the data transmission speed of each cluster of devices based on the known information; S700, obtaining the consumption time of each cluster in energy transmission and information transmission, and obtaining the optimal solution according to the consumption time; It can be understood by those skilled in the art that in order to avoid a large amount of energy loss in the network, improve the performance of the wireless power supply Internet of Things network and extend the life cycle of the network, in the intelligent reflective surface assisted WP-IOT, for the problem of energy replenishment of the Internet of Things devices in the network by the mobile hybrid access point, it is usually necessary to consider factors such as the location of the Internet of Things devices in the network, the energy demand situation and the moving range of the mobile hybrid access point. In order to minimize the charging time of the hybrid access point for the wireless device, we divide this complex problem into two stages for solution. First, according to the energy demand, location distribution and other factors of the Internet of Things nodes, the randomly distributed Internet of Things devices in the network are allocated nodes, and several cluster center positions for the mobile hybrid access point to dock are output; then, for the positions of these cluster center nodes, an approximate Chri The Stofides algorithm plans the paths of cluster core nodes and outputs the positions of the nodes in sequence; the mobile hybrid access point starts from the initial point, moves along these cluster core positions and stops for a period of time to charge the IoT devices in the cluster; then, the smart reflector is deployed at a suitable position according to the output charging position information to reduce the influence of factors such as attenuation and interference on the signal propagation efficiency in the network; considering the charging time problem of hybrid access points for IoT devices, the joint optimization problem of beamforming, device transmission power, smart reflector phase shift and time scheduling is designed, and a multivariable coupled optimization model is established. Therefore, the original non-convex problem is transformed into three easy-to-handle sub-problems through the block coordinate descent method, and the sub-problems are transformed and solved by introducing slack variables, semi-positive definite programming, maximum ratio transmission and other methods.

[0018] like Figure 2 As shown, taking the hybrid access point as the origin, constructing a coordinate system about the area, obtaining the coordinates of the IoT device and the smart reflective surface in the corresponding coordinate system, and dividing the IoT devices in the area into multiple clusters includes the following steps: S301, constructing a three-dimensional coordinate system with the hybrid access point as the origin; S302, respectively obtaining the coordinates of the IoT device and the smart reflective surface in each coordinate system; S303, dividing the IoT devices into multiple clusters according to the coordinates of each IoT device; Among them, the coordinates of the smart reflection surface are ; It can be understood by those skilled in the art that constructing a three-dimensional coordinate system with the hybrid access point as the origin provides a unified reference framework for the system, so that the subsequent position description of the IoT device and the smart reflective surface becomes accurate and consistent, and provides a basis for subsequent channel parameter calculation, signal and energy transmission path planning, etc. Accurately obtaining the coordinates of the IoT device and the smart reflective surface in each coordinate system is the key to system design and optimization. The coordinate data is the basis for obtaining subsequent data. Dividing the IoT device into multiple clusters according to its coordinates helps to achieve more efficient energy transmission and information exchange. Through cluster division, similar devices can be grouped together, thereby optimizing the energy transmission path, reducing energy loss, and improving transmission efficiency. Cluster division helps to achieve distributed management of the system. Each cluster can independently perform energy transmission and information exchange, reducing the complexity of the system and improving the scalability and flexibility of the system. In addition, cluster division can also be dynamically adjusted according to the actual needs and energy status of the device to adapt to system changes, further improving the adaptability and efficiency of the system. It is assumed that all devices operate on the same frequency band, wherein a time division duplex circuit is implemented on the hybrid access point and each device to separate energy and information transmission. The hybrid access point performs energy beamforming in the downlink and receive beamforming in the uplink information transmission. To improve the propagation performance, we adopt The intelligent reflective surface composed of passive reflective elements is used to assist the transmission of WP-IOT. The intelligent reflective surface can dynamically adjust the phase shift of each reflective element according to the propagation environment.

[0019] like Figure 3 As shown, obtaining the node coordinates of the sensor nodes in each cluster and obtaining multiple channel parameters based on the node coordinates and known information includes the following steps: S401, obtaining the node coordinates of the sensor nodes in each cluster; S402, obtaining a path loss per unit distance; S403, respectively obtaining the distances from the hybrid access point to the intelligent reflective surface, the distances from the intelligent reflective surface to the node, and the distances from the hybrid access point to the node; S404, obtaining the distance between the IoT device and the antenna in the hybrid access point; S405, obtaining a carrier wavelength for signal transmission; S406, according to the node coordinates and the acquired information, respectively acquire channels from the hybrid access point to the smart reflective surface, from the smart reflective surface to the node, and from the hybrid access point to the node; The channel calculation formulas from the hybrid access point to the intelligent reflective surface, from the intelligent reflective surface to the node, and from the hybrid access point to the node are: ; In the formula, is the distance from the hybrid access point to the smart reflective surface, is the distance from the smart reflector to the node, is the distance from the hybrid access point to the node, is the channel from the hybrid access point to the smart reflective surface, is the channel from the smart reflector to the node, is the channel from access point to node, is the carrier wavelength, is the antenna distance, is the cosine value of the signal departure angle of the hybrid access point-intelligent reflector link, is the cosine value of the signal departure angle of the smart reflector-node link, is the path loss per unit distance; It can be understood by those skilled in the art that this stage is the first stage to understand the location distribution and some basic characteristics of the IoT nodes. The node coordinates are the basis for determining the location of the sensor node in space, which is crucial for subsequent signal transmission path planning, energy allocation and channel modeling. Distance information is the basis for channel modeling and signal transmission path planning; the path loss per unit distance is an important indicator for evaluating the degree of signal attenuation during transmission; the distance of the antenna directly affects the transmission quality of the signal from the hybrid access point to the IoT device; the carrier wavelength is the basic parameter of signal transmission, which determines the propagation characteristics and interference characteristics of the signal. In the initial state, the mobile hybrid access point The point is located at the initial position. When the sensor node needs to replenish energy, the mobile hybrid access point will provide services to the sensor node from the initial position and finally return to the initial position. For simplicity, we assume that all channels follow the quasi-static flat fading model and follow the channel reciprocity between the downlink and the uplink, that is, the channel state information of the uplink and the downlink remains unchanged during the considered time. In the first stage, the hybrid access point transmits wireless energy to all IoT devices in the downlink. At the same time, the smart reflective surface scatters the incident signal from the hybrid access point to the IoT device, so that the IoT device receives signals from the direct link and the reflection link channel. The second stage is the uplink information transmission, and the IoT device transmits independent information to the hybrid access point.

[0020] like Figure 3 As shown, respectively obtaining the distances from the hybrid access point to the intelligent reflective surface, the intelligent reflective surface to the node, and the hybrid access point to the node includes the following steps: The specific calculation formulas for obtaining the distances from the hybrid access point to the intelligent reflective surface, the intelligent reflective surface to the node, and the hybrid access point to the node are as follows: ; In the formula, is the initial coordinate of the hybrid access point in the xy coordinate system, is the coordinate of the smart reflective surface in the xy coordinate system, is the z-axis coordinate of the smart reflective surface in the xyz coordinate system, is the coordinate of the node in the xy coordinate system.

[0021] like Figure 4 As shown, based on multiple channel parameters and combined with the information of the smart reflective surface, obtaining the energy collected by the IoT device, the node receiving power and the node energy includes the following steps: S501, obtaining an energy reflection beamforming matrix of a hybrid access point facing an intelligent reflection; S502, obtaining the duration of the incident radio frequency signal reflected by the smart reflective surface to the IoT device with energy harvesting function; S503, extracting energy transmission power and energy signal of the hybrid access point; S504, obtaining additive Gaussian white noise at the node; S505, obtaining a transmission beam vector; S506, obtaining energy collected by IoT devices, node receiving power, and node energy respectively based on known information and acquired information; Among them, the calculation formula for the energy collected by IoT devices is: ; The calculation formula of the node receiving power is: ; The calculation formula of node energy is: ; In the formula, Harvesting energy for IoT devices, is the node receiving power, is the node energy, is the transmission beam vector, and , is the maximum transmit power of the hybrid access point, A diagonal array of energy-reflecting beamforming matrices facing hybrid access points for intelligent reflection. The energy signal transmitted by the hybrid access point, is the energy transmission power of the hybrid access point, is the additive Gaussian white noise at the nodes, is the duration of signal transmission; It can be understood by those skilled in the art that this stage is to understand the energy requirements of the IoT nodes in the first stage. In the downlink energy transmission stage, the hybrid access point transmits an energy signal to the intelligent reflective surface, and the intelligent reflective surface reflects the incident RF signal to the IoT device with energy collection function; the beamforming matrix is ​​the key for the intelligent reflective surface to control the direction and intensity of the reflected signal. By obtaining this matrix, it is possible to accurately control how the intelligent reflective surface reflects the energy signal emitted by the hybrid access point to the target IoT device, thereby improving the efficiency and accuracy of energy transmission; understanding the duration helps to optimize the energy transmission strategy and ensure that the IoT device obtains sufficient energy within a limited time to support its normal operation. The energy transmission power and energy signal are the transmission parameters of the hybrid access point to the IoT. The energy collected by IoT devices is mainly used to maintain circuit operation and data transmission; the node received power is an indicator for evaluating the strength of the device receiving signal, which is crucial for the energy collection and function realization of the device. Node energy is the basis for the normal operation of IoT devices. By obtaining this information, we can fully understand the energy status of the device during the communication process, and provide a key basis for the energy management and optimization of the system.

[0022] like Figure 5 As shown, extracting the energy transmission time of the hybrid access point to all IoT devices, and obtaining the information transmission time of each cluster of IoT devices to the hybrid access point, and obtaining the signal received by the hybrid access point from the sensor, the signal-to-noise ratio of the IoT device at the hybrid access point, and the data transmission speed of each cluster of devices based on the known information include the following steps: S601, extracting energy transmission time of the hybrid access point to all IoT devices; S602, obtaining the information transmission time of each cluster of IoT devices to the hybrid access point; S603, obtaining a reflection matrix of the energy reflection beam of each cluster of IoT devices on the intelligent reflection surface; S604, obtaining the transmission power of the sensor node; S605, obtaining a node receiving beam; S606, obtaining unit bandwidth; S607, based on the known information, obtaining a signal received by the hybrid access point from the sensor, a signal-to-noise ratio of the IoT device at the hybrid access point, and a data transmission speed of each cluster device; The calculation formula for the signal received by the hybrid access point from the sensor, the signal-to-noise ratio of the IoT device at the hybrid access point, and the data transmission speed of each cluster device is: ; In the formula, The hybrid access point receives the signal from the sensor, The signal-to-noise ratio of IoT devices in hybrid access points, is the data transmission speed of each cluster device, is the transmit power of the sensor node, is the node receiving beam, and , A reflective matrix for intelligently reflecting energy-reflecting beams facing each cluster of IoT devices; It can be understood by those skilled in the art that, in this stage, randomly distributed IoT devices are obtained for node allocation processing in the first stage. In the uplink information transmission stage, multiple IoT nodes transmit independent information to the hybrid access point, and the intelligent reflection surface reflects the signal transmitted by the node to the hybrid access point, and uses the collected energy to transmit the respective information to the hybrid access point. In the subsequent wireless information transmission stage, all devices use the energy collected in stage one to transmit independent information to the hybrid access point at the same time. At the same time, the intelligent reflection surface reflects the transmission signal of the IoT device to the hybrid access point. By extracting the energy transmission time of the hybrid access point to all IoT devices, the duration is combined to be , obtain the time slot allocation of the system's downlink wireless energy transmission and uplink wireless information transmission. The energy transmission time is the key parameter for the hybrid access point to provide energy support for the IoT device; the information transmission time is the key indicator for the IoT device to transmit data to the hybrid access point; the reflection matrix is ​​the core of the intelligent reflection surface to control the direction and intensity of energy reflection; the transmission power is the basis for the sensor node to send signals, which directly affects the transmission distance and quality of the signal; the receiving beam determines the direction and intensity of the node receiving the signal; the hybrid access point receives the signal from the sensor, which is the basis for the system to obtain information from the IoT device. The signal-to-noise ratio is an important indicator for evaluating the quality of signal transmission, which directly affects the accuracy and reliability of the data. The data transmission speed is the key parameter for measuring the communication performance of the system, which determines the efficiency and real-time performance of data transmission.

[0023] like Figure 6 As shown, obtaining the time consumed by each cluster in energy transmission and information transmission, and obtaining the optimal solution based on the time consumed includes the following steps: S701, obtaining the total time of each cluster of IoT devices in the energy transmission and information transmission stages; S702, constructing a multi-directional constraint model based on known information; S703, respectively obtaining the minimum time of energy transmission and information transmission phases in the constraint model under different hybrid access points; S704, comparing the minimum time under different hybrid access points; S705, the minimum time of different hybrid access points, wherein the minimum time is the optimal charging position of each cluster; S706, planning a path according to the optimal charging position of each cluster of different hybrid access points, which is the optimal solution; Among them, the calculation formula for the total time of energy transmission and information transmission stage is: ; Among them, the minimum time of energy transfer and information transmission in the constraint model is: ; Among them, the minimum time under different hybrid access points is: ; It can be understood by those skilled in the art that the energy transmission time of the hybrid access point to all IoT devices is extracted, the time required for the hybrid access point (i.e., a node that can provide both energy and information transmission functions) to transmit energy to all IoT devices it covers is determined, and the specific time required for each IoT device to receive energy from the hybrid access point is obtained through calculation, so as to provide a basis for subsequent optimization and decision-making. Networked devices are usually organized in a clustering manner to improve communication efficiency. The time required for each cluster of IoT devices to transmit information to the hybrid access point is calculated, and the total time required for each cluster of IoT devices in the two stages of energy transmission and information transmission is calculated, which reflects the total energy consumption required for the device to complete a complete energy replenishment and information exchange process. When charging, a constraint model is constructed to reduce the mutual influence between energy transmission and information transmission. On the basis of the constructed multi-directional constraint model, the minimum time of energy transmission and information transmission stages under different hybrid access points is solved. The minimum time under different hybrid access points is compared, and the differences and advantages and disadvantages between them are analyzed, which helps to identify which hybrid access points are better in energy transmission and information transmission. After comparing the minimum time of different hybrid access points, the hybrid access point with the smallest minimum time is selected as the best charging position. Therefore, this position is regarded as the best choice for charging IoT devices. By obtaining the best choice for charging IoT devices in each cluster, the charging time is used as the weight, and the moving paths of different hybrid access points are planned, which is the best solution.

[0024] like Figure 7 As shown, constructing a multi-directional constraint model based on known information includes the following steps: S7021, obtaining the transmission energy of the corresponding cluster sensor node, and making the transmission energy greater than or equal to the node energy comparison; S7022. Obtain a signal-to-noise ratio of the hybrid access point, and make the signal-to-noise ratio of the hybrid access point greater than the signal-to-noise ratio of the IoT device at the hybrid access point; S7023. Obtain the maximum receiving power of the hybrid access point, and make the maximum receiving power of the hybrid access point greater than or equal to the transmission beam vector modulus length; S7024, extracting the mode length of the receiving beam and the unit mode of the phase shift of the smart reflection surface; S7025. Construct a multi-directional constraint model based on known information; Among them, the multi-directional constraint model is: ; In the formula, is the signal-to-noise ratio of the hybrid access point, is the maximum energy receiving power of the hybrid access point; It can be understood by those skilled in the art that It ensures that the energy consumption during the data transmission phase will not be zero. represents the time scheduling constraint, Satisfy quality of service constraints, represents the minimum transmission power constraint of the hybrid access point, is the receive beamforming constraint, Unit mode constraint representing the phase shift of a smart reflector.

[0025] In summary, the advantages of the present invention are as follows: the present invention solves the complex problem by dividing it into two stages. First, according to factors such as the energy demand and location distribution of the IoT nodes, the randomly distributed IoT devices in the network are allocated nodes, and a number of cluster center locations for the mobile hybrid access point to dock are output. Subsequently, for the locations of these cluster center nodes, the paths of the cluster center nodes are planned and the locations of the nodes are output in sequence. The shortest path nodes are selected to form a Hamiltonian circuit. The mobile hybrid access point will charge the IoT devices in each cluster along the planned path. Secondly, a model for minimizing the charging time is established. Since the The optimization problem is coupled and non-convex. A block coordinate descent method is used to solve the above problem. The original problem is decomposed into three sub-problems. These sub-problems can be solved independently when other parameters are fixed, and then jointly solved until convergence, so that the hybrid access point can be flexibly moved for charging. The complex problem is decomposed into multiple sub-problems, which are convenient for solving and optimizing separately. By introducing slack variables and semi-definite programming, the non-convex problem is transformed into a convex problem or a more tractable form, which improves the solution efficiency. At the same time, multiple factors such as beamforming, device transmit power, IRS phase shift and time scheduling are considered, achieving an overall improvement in system performance.

[0026] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for minimizing charging time of WP-IoT based on a smart reflective surface, characterized in that: include: Get the number of hybrid access points and IoT devices in the area; Get information about hybrid access points, smart reflective surfaces, and IoT devices; Taking the hybrid access point as the origin, a coordinate system of the region is constructed, and the coordinates of the IoT devices and the smart reflective surface in the corresponding coordinate system are obtained, and the IoT devices in the region are divided into multiple clusters; Obtain the node coordinates of the sensor nodes in each cluster, and obtain multiple channel parameters based on the node coordinates and known information; According to multiple channel parameters and in combination with the information of the intelligent reflective surface, the node receiving signal, the node receiving power and the node energy of the node are obtained; Extract the energy transmission time of the hybrid access point to all IoT devices, and obtain the information transmission time of each cluster of IoT devices to the hybrid access point. Based on the known information, obtain the signal received by the hybrid access point from the sensor, the signal-to-noise ratio of the IoT device at the hybrid access point, and the data transmission speed of each cluster of devices; Obtain the time consumed by each cluster in energy transmission and information transmission, and obtain the optimal solution based on the consumption time.

2. According to claim 1, a method for minimizing charging time of WP-IoT based on a smart reflective surface is characterized in that: The method of constructing a coordinate system about the region with the hybrid access point as the origin, obtaining the coordinates of the IoT device and the intelligent reflective surface in the corresponding coordinate system, and dividing the IoT devices in the region into multiple clusters includes the following steps: A three-dimensional coordinate system is constructed with the hybrid access point as the origin; Obtain the coordinates of the IoT device and the smart reflective surface in each coordinate system respectively; The IoT devices are divided into multiple clusters according to the coordinates of each IoT device; Among them, the coordinates of the smart reflection surface are 。 3. The method for minimizing charging time of WP-IoT based on a smart reflective surface according to claim 2, characterized in that: The step of obtaining the node coordinates of the sensor nodes in each cluster and obtaining a plurality of channel parameters according to the node coordinates and known information comprises the following steps: Get the node coordinates of the sensor nodes in each cluster; Get the path loss per unit distance; respectively obtaining the distances from the hybrid access point to the intelligent reflective surface, the distances from the intelligent reflective surface to the node, and the distances from the hybrid access point to the node; Get the distance between the IoT device and the antenna in the hybrid access point; Obtain the carrier wavelength of signal transmission; According to the node coordinates and the acquired information, the channels from the hybrid access point to the intelligent reflection surface, the channels from the intelligent reflection surface to the node, and the channels from the hybrid access point to the node are acquired respectively; The channel calculation formulas from the hybrid access point to the intelligent reflective surface, from the intelligent reflective surface to the node, and from the hybrid access point to the node are: ; In the formula, is the distance from the hybrid access point to the smart reflective surface, is the distance from the smart reflector to the node, is the distance from the hybrid access point to the node, is the channel from the hybrid access point to the smart reflective surface, is the channel from the smart reflector to the node, is the channel from access point to node, is the carrier wavelength, is the antenna distance, is the cosine value of the signal departure angle of the hybrid access point-intelligent reflector link, is the cosine value of the signal departure angle of the smart reflector-node link, is the path loss per unit distance.

4. The method for minimizing charging time of WP-IoT based on a smart reflective surface according to claim 3, characterized in that: The step of respectively obtaining the distances from the hybrid access point to the intelligent reflective surface, from the intelligent reflective surface to the node, and from the hybrid access point to the node comprises the following steps: The specific calculation formulas for respectively obtaining the distances from the hybrid access point to the intelligent reflective surface, from the intelligent reflective surface to the node, and from the hybrid access point to the node are: ; In the formula, is the initial coordinate of the hybrid access point in the xy coordinate system, is the coordinate of the smart reflective surface in the xy coordinate system, is the z-axis coordinate of the smart reflective surface in the xyz coordinate system, is the coordinate of the node in the xy coordinate system.

5. The method for minimizing charging time of WP-IoT based on a smart reflective surface according to claim 4, characterized in that: The method of obtaining the node received signal, node received power and node energy of the node according to the multiple channel parameters and in combination with the information of the smart reflective surface comprises the following steps: Get the energy reflection beamforming matrix of the intelligent reflection face hybrid access point; Obtain the duration of the incident radio frequency signal reflected by the smart reflective surface to the IoT device with energy harvesting function; Extracting energy transmission power and energy signal of hybrid access point; Get the additive Gaussian white noise at the node; Get the transmission beam vector; According to the known information and the acquired information, the node receiving signal, the node receiving power and the node energy of the node are acquired respectively; Among them, the calculation formula for the node receiving signal is: ; The calculation formula of the node receiving power is: ; The calculation formula of node energy is: ; In the formula, Energy harvested for IoT devices, is the node receiving power, is the node energy, is the transmission beam vector, and , is the maximum transmit power of the hybrid access point, A diagonal array of energy-reflecting beamforming matrices facing hybrid access points for intelligent reflection. The energy signal transmitted by the hybrid access point, is the energy transmission power of the hybrid access point, is the additive Gaussian white noise at the node, is the duration of signal transmission.

6. The method for minimizing charging time of WP-IoT based on a smart reflective surface according to claim 5, characterized in that: The method of extracting the energy transmission time of the hybrid access point to all IoT devices, and obtaining the information transmission time of each cluster of IoT devices to the hybrid access point, and obtaining the signal received by the hybrid access point from the sensor, the signal-to-noise ratio of the IoT device at the hybrid access point, and the data transmission speed of each cluster of devices based on known information includes the following steps: Extract the energy transfer time of hybrid access points to all IoT devices; Obtain information transmission time of each cluster of IoT devices to the hybrid access point; Obtain a reflection matrix of the energy reflection beam of each cluster of IoT devices on the intelligent reflection surface; Get the transmit power of the sensor node; Get the node receiving beam; Get unit bandwidth; Based on the known information, obtain the signal received by the hybrid access point from the sensor, the signal-to-noise ratio of the IoT device at the hybrid access point, and the data transmission speed of each cluster device; The calculation formula for the signal received by the hybrid access point from the sensor, the signal-to-noise ratio of the IoT device at the hybrid access point, and the data transmission speed of each cluster device is: ; In the formula, The hybrid access point receives the signal from the sensor, The signal-to-noise ratio of IoT devices in hybrid access points, is the data transmission speed of each cluster device, is the transmit power of the sensor node, is the node receiving beam, and , A reflective matrix that reflects energy beams toward each cluster of IoT devices for intelligent reflection.

7. The method for minimizing charging time of WP-IoT based on a smart reflective surface according to claim 6, characterized in that: The step of obtaining the energy transmission and information transmission consumption time of each cluster and obtaining the optimal solution according to the consumption time comprises the following steps: Obtain the total time of each cluster of IoT devices in the energy transmission and information transmission stages; Construct a multi-directional constraint model based on known information; The minimum time of energy transmission and information transmission phases in the constraint model under different hybrid access points is obtained respectively; Compare the minimum time under different hybrid access points; The minimum time of different hybrid access points is the optimal charging position for each cluster; The best solution is to plan the path based on the best charging position of each cluster of different hybrid access points; Among them, the calculation formula for the total time of energy transmission and information transmission stage is: ; Among them, the minimum time of energy transfer and information transmission in the constraint model is: ; Among them, the minimum time under different hybrid access points is: 。 8. The method for minimizing charging time of WP-IoT based on a smart reflective surface according to claim 6, characterized in that: The multi-directional constraint model is constructed based on known information and includes the following steps: Obtain the transmission energy of the corresponding cluster sensor node, and make the transmission energy greater than or equal to the node energy comparison; Obtain a signal-to-noise ratio of a hybrid access point, and make the signal-to-noise ratio of the hybrid access point greater than a signal-to-noise ratio of the IoT device at the hybrid access point; Obtaining the maximum receiving power of the hybrid access point, and making the maximum receiving power of the hybrid access point greater than or equal to the transmission beam vector modulus; Extract the mode length of the received beam and the unit mode of the phase shift of the smart reflector; Construct a multi-directional constraint model based on known information; Among them, the multi-directional constraint model is: ; In the formula, is the signal-to-noise ratio of the hybrid access point, is the maximum energy receiving power of the hybrid access point.

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