A smart reflector based wp-iot charging time minimization method

Abstract

The application discloses a WP-IoT charging time minimization method based on an intelligent reflecting surface, relates to the wireless power supply technical field and comprises the following steps: acquiring the number of mixed access points and the number of Internet of Things devices in a region; acquiring the information of the mixed access points, the intelligent reflecting surface and the Internet of Things devices; constructing a coordinate system about the region with the mixed access points as the origin, respectively, acquiring the coordinates of the Internet of Things devices and the intelligent reflecting surface in the corresponding coordinate system, and dividing the Internet of Things devices in the region into multiple clusters; acquiring the node coordinates of the sensor nodes in each cluster, decomposing a complex problem into multiple sub-problems, facilitating separate solving and optimization, introducing a relaxation variable and a semi-positive definite programming to convert a non-convex problem into a convex problem or a more easily handled form, improving solving efficiency, simultaneously considering multiple factors such as beam forming, device transmission power, IRS phase shift and time scheduling, and realizing overall improvement of system performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless power supply, in particular to a WP-IoT charging time minimization method based on intelligent reflecting surface. BACKGROUND

[0002] Intelligent reflecting surface is a new type of wireless communication technology that uses large-scale reflecting elements such as micro antennas or phase adjustment elements to create a dynamically controllable surface. These reflecting elements can change their phase, amplitude or polarization state as needed to control the reflection, propagation and reception of wireless signals.

[0003] In a wireless communication system, intelligent reflecting surface can be used as an infrastructure to enhance or optimize the propagation of wireless signals. For example, by controlling the intelligent reflecting surface, the propagation path of the wireless signal can be changed, the coverage range can be increased, the interference can be reduced, and the performance of the communication system can be improved.

[0004] Although there have been a lot of research on intelligent reflecting surface assisted WP-IOT networks, so far most of the research work has been conducted under the assumption that the mixed access point is fixed in position, while in the actual environment, the mixed access point can be flexibly moved to supplement energy and collect information for Internet of Things devices in the network; deploying intelligent reflecting surface is to adjust the energy reflection signal to avoid signal attenuation due to long distance or the presence of obstacles, so as to build an efficient charging area; and few research works consider time optimization problems, and charging time is one of the important factors to maintain efficient network operation. Therefore, the present application proposes a WP-IoT charging time minimization method based on intelligent reflecting surface. SUMMARY

[0005] To solve the above technical problems, a WP-IoT charging time minimization method based on intelligent reflecting surface is provided, which solves the problem that most research work is conducted under the assumption that the mixed access point is fixed in position, while in the actual environment, the mixed access point can be flexibly moved.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is:

[0007] A WP-IoT charging time minimization method based on intelligent reflecting surface, comprising:

[0008] S100, obtaining the number of mixed access points and the number of Internet of Things devices in the area;

[0009] S200, obtaining information of the mixed access point, intelligent reflecting surface and Internet of Things device;

[0010] S300: Build a coordinate system for the region with the hybrid access point as the origin, obtain the coordinates of the IoT devices and the smart reflective surface in the corresponding coordinate system, and divide the IoT devices in the region into multiple clusters:

[0011] S301, constructing a three-dimensional coordinate system with the hybrid access point as the origin;

[0012] S302: Obtain the coordinates of the IoT device and the smart reflective surface in each coordinate system respectively;

[0013] S303, dividing the IoT devices into multiple clusters according to the coordinates of each IoT device;

[0014] S400, obtaining the node coordinates of the sensor nodes in each cluster, and obtaining multiple channel parameters based on the node coordinates and known information:

[0015] S401, obtaining the node coordinates of the sensor nodes in each cluster;

[0016] S402, obtaining a path loss per unit distance;

[0017] S403, respectively obtaining the distances from the hybrid access point to the intelligent reflective surface, the distances from the intelligent reflective surface to the sensor node, and the distances from the hybrid access point to the sensor node;

[0018] S404. Obtain the distance between the IoT device and the antenna in the hybrid access point;

[0019] S405, obtaining a carrier wavelength for signal transmission;

[0020] S406. According to the node coordinates and the acquired information, respectively acquire the channels from the hybrid access point to the smart reflective surface, the smart reflective surface to the sensor node, and the hybrid access point to the sensor node;

[0021] S500, based on multiple channel parameters and information about the smart reflective surface, obtain energy collected by IoT devices, node received power, and node energy;

[0022] S600: 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.

[0023] S700: Obtain the energy transmission and information transmission time of each cluster, and obtain the optimal solution based on the time consumption:

[0024] S701, obtaining the total time of each cluster of IoT devices in the energy transmission and information transmission stages;

[0025] S702: Construct a multi-directional constraint model based on known information:

[0026] 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;

[0027] S7022. Obtain a signal-to-noise ratio (SNR) of the hybrid access point, and make the SNR of the hybrid access point greater than a SNR of the IoT device at the hybrid access point.

[0028] S7023. Obtain the maximum received power of the hybrid access point, and make the maximum received power of the hybrid access point greater than or equal to the modulus of the transmission beam vector;

[0029] S7024. Extracting the modulus of the received beam and the unit mode of the phase shift of the smart reflector;

[0030] S7025. Construct a multi-directional constraint model based on known information;

[0031] Among them, the multi-directional constraint model is:

[0032] ;

[0033] Where, is the signal-to-noise ratio of the hybrid access point, is the maximum energy receiving power of the hybrid access point;

[0034] S703, respectively obtaining the minimum time of the energy transmission and information transmission phases in the constraint model under different hybrid access points;

[0035] S704. Compare the minimum time under different hybrid access points;

[0036] S705, wherein the minimum time of different hybrid access points is the optimal charging position for each cluster;

[0037] S706 , planning a path based on the optimal charging location of each cluster of different hybrid access points, which is the optimal solution;

[0038] The calculation formula for the total time of energy transmission and information transmission is:

[0039] ;

[0040] Among them, the minimum time of energy transfer and information transmission in the constraint model is:

[0041] ;

[0042] Among them, the minimum time under different hybrid access points is:

[0043] .

[0044] Preferably, the constructing a coordinate system about the region with the mixed access point as the origin, and obtaining the coordinates of the Internet of Things devices and the smart reflector in the corresponding coordinate system, and dividing the Internet of Things devices in the region into multiple clusters comprises the following steps:

[0045] Wherein, the coordinates of the smart reflector are

[0046] .

[0047] Preferably, the 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:

[0048] Wherein, the channel calculation formulas of the mixed access point to the smart reflector, the smart reflector to the sensor node, and the mixed access point to the sensor node are:

[0049] ;

[0050] In the formula, is the distance from the mixed access point to the smart reflector, is the distance from the smart reflector to the kth sensor node, is the distance from the mixed access point to the sensor node, is the channel from the mixed access point to the smart reflector, is the channel from the smart reflector to the sensor node, is the channel from the mixed access point to the sensor node, is the carrier wavelength, is the antenna distance, is the cosine value of the mixed access point-smart reflector link signal departure angle, is the cosine value of the smart reflector-node link signal departure angle, is the path loss per unit distance.

[0051] Preferably, the obtaining the distances from the mixed access point to the smart reflector, the smart reflector to the sensor node, and the mixed access point to the sensor node respectively comprises the following steps:

[0052] The specific calculation formula of the distances from the mixed access point to the smart reflector, the smart reflector to the sensor node, and the mixed access point to the sensor node respectively is:

[0053] ;

[0054] In the formula, is the initial coordinate of the mixed access point in the xy coordinate system, is the coordinate of the smart reflector in the xy coordinate system, The z-axis coordinate of the intelligent reflecting surface in the xyz coordinate system, The coordinate of the kth sensor node in the xy coordinate system.

[0055] Preferably, the method comprises the following steps of acquiring the energy collected by the Internet of Things device, the node received power and the node energy according to the plurality of channel parameters and the information of the intelligent reflecting surface.

[0056] S501, acquiring an energy reflection beamforming matrix of the intelligent reflecting surface to the hybrid access point;

[0057] S502, acquiring a duration of the intelligent reflecting surface reflecting the incident radio frequency signal to the Internet of Things device with energy collection function;

[0058] S503, extracting the energy transmission power and the energy signal of the hybrid access point;

[0059] S504, acquiring the additive white Gaussian noise at the node;

[0060] S505, acquiring the transmission beam vector;

[0061] S506, acquiring the energy collected by the Internet of Things device, the node received power and the node energy according to the known information and the acquired information;

[0062] The calculation formula of the node received signal is:

[0063]

[0064] The calculation formula of the node received power is:

[0065]

[0066] The calculation formula of the node energy is:

[0067]

[0068] In the formula, is the collected energy of the Internet of Things device, is the node received power, is the node energy, is the transmission beam vector, and , is the maximum transmission power of the hybrid access point, is the diagonal matrix of the energy reflection beamforming matrix of the intelligent reflecting surface to the hybrid access point, is the energy signal transmitted by the hybrid access point, is the energy transmission power of the hybrid access point, is the additive white Gaussian noise at the node, ​​​is the duration of signal transmission.

[0069] Preferably, extracting the energy transmission time of the hybrid access point to all IoT devices, obtaining the information transmission time of each cluster of IoT devices to the hybrid access point, and obtaining, based on known information, 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 includes the following steps:

[0070] S601, extracting the energy transmission time of the hybrid access point to all IoT devices;

[0071] S602. Obtain information transmission time of each cluster of IoT devices to the hybrid access point;

[0072] S603: Obtain a reflection matrix of the energy reflection beam of each cluster of IoT devices on the intelligent reflection surface;

[0073] S604, obtaining the transmit power of the sensor node;

[0074] S605, obtaining a node receiving beam;

[0075] S606: Obtain unit bandwidth;

[0076] S607. 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;

[0077] The calculation formulas for the signals 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 are as follows:

[0078] ;

[0079] Where, For hybrid access points to receive signals from sensors, is the signal-to-noise ratio of IoT devices at 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 reflection matrix that reflects the energy of each cluster of IoT devices to the smart reflector.

[0080] Preferably, 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:

[0081] The calculation formula for the total time of energy transmission and information transmission is:

[0082] ;

[0083] wherein the minimum time of energy transmission and information transmission in the constraint model is:

[0084] ;

[0085] wherein the minimum time under different hybrid access points is:

[0086] .

[0087] Preferably, the step of constructing a multi-directional constraint model according to known information comprises the following steps:

[0088] wherein the multi-directional constraint model is:

[0089] ;

[0090] wherein, is the signal-to-noise ratio of the hybrid access point, is the maximum energy receiving power of the hybrid access point.

[0091] Compared with the prior art, the application has the advantages that: the application solves the complex problem by dividing it into two stages, first, according to the energy demand, location distribution and other factors of the Internet of Things nodes, the Internet of Things devices randomly distributed in the network are processed for node allocation, and a plurality of cluster center positions available for the mobile hybrid access point to stop are output, then, for the positions of the cluster center nodes, the cluster center nodes are path planned and the positions of the nodes are sequentially output in order, the shortest path nodes are selected to form a Hamiltonian circuit, and the mobile hybrid access point will charge the Internet of Things devices in each cluster along the planned path; secondly, a model for minimizing charging time is established, 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 divided into three sub-problems, which can be solved independently under the condition that other parameters are fixed, and then jointly solved until convergence, the complex problem is divided into multiple sub-problems, which is convenient for separate solving and optimization, by introducing a relaxation variable and a semi-positive definite programming, the non-convex problem is converted into a convex problem or a more easily handled form, the solving efficiency is improved, and multiple factors such as beam forming, device transmitting power, IRS phase shift and time scheduling are considered, so that the overall system performance is improved. BRIEF DESCRIPTION OF DRAWINGS

[0092] Figure 1 is a flowchart of steps S100-S700 in a WP-IoT charging time minimization method based on an intelligent reflecting surface proposed by the application;

[0093] Figure 2This is a flow chart of steps S301-S303 in a method for minimizing charging time of WP-IoT based on a smart reflective surface proposed in the present invention;

[0094] 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 in the present invention;

[0095] 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 in the present invention;

[0096] 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 in the present invention;

[0097] Figure 6 This is a flow chart of steps S701-S706 in a method for minimizing charging time of a WP-IoT based on a smart reflective surface proposed in the present invention;

[0098] 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

[0099] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0100] Reference Figure 1-7 As shown, a method for minimizing charging time of WP-IoT based on a smart reflective surface includes:

[0101] S100, obtaining the number of hybrid access points and IoT devices in the area;

[0102] S200, obtaining information about hybrid access points, smart reflective surfaces, and IoT devices;

[0103] S300: Build a coordinate system for the region with the hybrid access point as the origin, obtain the coordinates of the IoT devices and the smart reflective surface in the corresponding coordinate system, and divide the IoT devices in the region into multiple clusters:

[0104] S301, constructing a three-dimensional coordinate system with the hybrid access point as the origin;

[0105] S302: Obtain the coordinates of the IoT device and the smart reflective surface in each coordinate system respectively;

[0106] S303, dividing the IoT devices into multiple clusters according to the coordinates of each IoT device;

[0107] S400, obtaining the node coordinates of the sensor nodes in each cluster, and obtaining multiple channel parameters based on the node coordinates and known information:

[0108] S401, obtaining the node coordinates of the sensor nodes in each cluster;

[0109] S402, obtaining a path loss per unit distance;

[0110] S403, respectively obtaining the distances from the hybrid access point to the intelligent reflective surface, the distances from the intelligent reflective surface to the sensor node, and the distances from the hybrid access point to the sensor node;

[0111] S404. Obtain the distance between the IoT device and the antenna in the hybrid access point;

[0112] S405, obtaining a carrier wavelength for signal transmission;

[0113] S406. According to the node coordinates and the acquired information, respectively acquire the channels from the hybrid access point to the smart reflective surface, the smart reflective surface to the sensor node, and the hybrid access point to the sensor node;

[0114] S500, based on multiple channel parameters and information about the smart reflective surface, obtain energy collected by IoT devices, node received power, and node energy;

[0115] S600: 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.

[0116] S700: Obtain the energy transmission and information transmission time of each cluster, and obtain the optimal solution based on the time consumption:

[0117] S701, obtaining the total time of each cluster of IoT devices in the energy transmission and information transmission stages;

[0118] S702: Construct a multi-directional constraint model based on known information:

[0119] 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;

[0120] S7022, obtain the 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 Internet of Things device at the hybrid access point;

[0121] 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 modulus of the transmission beam vector;

[0122] S7024, extract the modulus of the receiving beam and the unit modulus of the intelligent reflecting surface phase shift;

[0123] S7025, construct a multi-directional constraint model according to known information;

[0124] The multi-directional constraint model is:

[0125] ;

[0126] 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;

[0127] S703, respectively obtain the minimum time of the energy transmission and information transmission stage in the constraint model under different hybrid access points;

[0128] S704, compare the minimum time under different hybrid access points;

[0129] S705, wherein the minimum time of different hybrid access points is the best charging position of each cluster;

[0130] S706, according to the best charging position of each cluster of different hybrid access points, plan a path, which is the best scheme;

[0131] The calculation formula of the total time of the energy transmission and information transmission stage is:

[0132] ;

[0133] The minimum time of the energy transmission and information transmission stage in the constraint model is:

[0134] ;

[0135] The minimum time under different hybrid access points is:

[0136] ;

[0137] The skilled in the art can understand that, in order to avoid a large amount of energy loss of the network, improve the performance of the wireless power supply Internet of Things network and prolong the life cycle of the network, in the intelligent reflecting surface assisted WP-IOT, for the problem of energy supplement of the Internet of Things devices in the network by the mobile hybrid access point, factors such as the position of the Internet of Things devices in the network, the energy demand situation and the moving range of the mobile hybrid access point need to be considered, in order to minimize the charging time of the wireless devices by the hybrid access point, the complex problem is divided into two stages for solving, first, according to the energy demand, position distribution and other factors of the Internet of Things nodes, the node allocation processing is performed on the randomly distributed Internet of Things devices in the network, and a plurality of cluster center positions available for the mobile hybrid access point to stop are output; then, for the positions of the cluster center nodes, an approximate Christofides algorithm is used to plan the paths of the cluster center nodes and output the positions of the nodes in sequence; the mobile hybrid access point starts from the initial point, moves along the cluster center positions and stops for a period of time to charge the Internet of Things devices in the cluster; then, according to the output charging position information, the intelligent reflecting surface is deployed at a suitable position to reduce the influence of factors such as signal propagation efficiency attenuation and interference in the network; considering the charging time problem of the hybrid access point to the Internet of Things devices, a joint optimization problem of beam forming, device transmission power, intelligent reflecting surface phase shift and time scheduling is designed, and a multivariable coupled optimization model is established, therefore, the original non-convex problem is converted into three sub-problems easy to handle by block coordinate descent method, and the sub-problems are solved by introducing relaxation variables, semi-definite programming, maximum ratio transmission and other methods.

[0138] As shown in Figure 2 , the coordinate system about the region is constructed with the hybrid access point as the origin, and the coordinates of the Internet of Things devices and the intelligent reflecting surface in the corresponding coordinate system are obtained, and the Internet of Things devices in the region are divided into a plurality of clusters including the following steps:

[0139] Among them, the coordinates of the intelligent reflecting surface are

[0140] ;

[0141] Those skilled in the art can understand that the three-dimensional coordinate system is constructed with the mixed access point as the origin, which provides a unified reference framework for the system, makes the subsequent position description of the Internet of Things devices and intelligent reflecting surfaces accurate and consistent, and provides a basis for subsequent channel parameter calculation, signal and energy transmission path planning, etc. Accurate acquisition of the coordinates of each Internet of Things device and intelligent reflecting surface in the coordinate system is the key to system design and optimization, and the coordinate data is the basis for obtaining subsequent data. According to the coordinates of the Internet of Things devices, they are divided into multiple clusters, which helps to realize 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 realize 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 state of the devices to adapt to system changes, further improving the adaptability and efficiency of the system. Assuming that all devices operate on the same frequency band, time division duplex circuits are implemented on the mixed access point and each device to separate energy and information transmission. The mixed access point performs energy beamforming in the downlink and performs receive beamforming in the uplink information transmission. In order to improve the propagation performance, we use an intelligent reflecting surface composed of passive reflecting elements to assist WP-IOT transmission. The intelligent reflecting surface can dynamically adjust the phase shift of each reflecting element according to the propagation environment.

[0142] As shown in Figure 3 , the node coordinates of the sensor nodes in each cluster are obtained, and a plurality of channel parameters are obtained according to the node coordinates and known information, including the following steps:

[0143] Among them, the channel calculation formula of the mixed access point to the intelligent reflecting surface, the intelligent reflecting surface to the sensor node and the mixed access point to the sensor node is:

[0144] ;

[0145] In the formula, is the distance from the mixed access point to the intelligent reflecting surface, is the distance from the intelligent reflecting surface to the kth sensor node, is the distance from the mixed access point to the sensor node, is the channel from the mixed access point to the intelligent reflecting surface, is the channel from the intelligent reflecting surface to the sensor node, is the channel from the mixed access point to the sensor node, is the carrier wavelength, is the antenna distance, is the cosine value of the mixed access point-intelligent reflecting surface link signal departure angle, cosine value of the smart reflective surface-node link signal departure angle, path loss per unit distance;

[0146] The skilled in the art can understand that this stage is to understand the position distribution and some basic characteristics of the Internet of Things nodes in the first stage, the node coordinates are the basis for determining the position of the sensor nodes in space, which is crucial for subsequent signal transmission path planning, energy allocation and channel modeling, the 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 in the transmission process; the distance of the antenna directly affects the transmission quality of the signal from the hybrid access point to the Internet of Things device; the carrier wavelength is a basic parameter of signal transmission, which determines the signal propagation characteristics and interference characteristics, in the initial state, the mobile hybrid access point is located at the initial position, when the sensor node needs to supplement energy, the mobile hybrid access point will start from the initial position to provide service to the sensor node, and finally return to the initial position, for the sake of 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 within the considered time, in the first stage, the hybrid access point transmits wireless energy to all Internet of Things devices in the downlink, at the same time, the smart reflective surface scatters the incident signal from the hybrid access point to the Internet of Things devices, so that the Internet of Things devices receive signals from the direct link and the reflected link channel. The second stage is the uplink information transmission, and the Internet of Things devices transmit independent information to the hybrid access point.

[0147] As shown in Figure 3 , the distances of the hybrid access point to the smart reflective surface, the smart reflective surface to the sensor node and the hybrid access point to the sensor node are obtained respectively, including the following steps:

[0148] The specific calculation formula of the distances of the hybrid access point to the smart reflective surface, the smart reflective surface to the sensor node and the hybrid access point to the sensor node is:

[0149] ;

[0150] 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 kth sensor node in the xy coordinate system.

[0151] As shown in Figure 4As shown, according to a plurality of channel parameters, in combination with the information of the intelligent reflecting surface, the energy collected by the Internet of Things device, the node receiving power and the node energy include the following steps:

[0152] S501, acquiring an energy reflection beamforming matrix of the intelligent reflecting surface to the hybrid access point;

[0153] S502, acquiring the duration of the intelligent reflecting surface reflecting the incident radio frequency signal to the Internet of Things device with energy collection function;

[0154] S503, extracting the energy transmission power of the hybrid access point and the energy signal;

[0155] S504, acquiring the additive white Gaussian noise at the node;

[0156] S505, acquiring the transmission beam vector;

[0157] S506, according to the known information and the acquired information, respectively acquiring the energy collected by the Internet of Things device, the node receiving power and the node energy;

[0158] Wherein, the calculation formula of the energy collected by the Internet of Things device is:

[0159]

[0160] Wherein, the calculation formula of the node receiving power is:

[0161]

[0162] Wherein, the calculation formula of the node energy is:

[0163]

[0164] In the formula, is the collected energy of the Internet of Things device, is the node receiving power, is the node energy, is the transmission beam vector, and , is the maximum transmission power of the hybrid access point, is the diagonal matrix of the energy reflection beamforming matrix of the intelligent reflecting surface to the hybrid access point, is the energy signal transmitted by the hybrid access point, is the energy transmission power of the hybrid access point, is the additive white Gaussian noise at the node, is the duration of signal transmission;

[0165] ​​​Those skilled in the art can understand that this stage is the first stage of understanding the energy requirements of the IoT nodes. In the downlink energy transmission stage, the hybrid access point transmits an energy signal to the smart reflective surface, and the smart reflective surface reflects the incident RF signal to the IoT device with energy collection function; the beamforming matrix is ​​the key for the smart reflective surface to control the direction and intensity of the reflected signal. By obtaining this matrix, it is possible to accurately control how the smart reflective surface reflects the energy signal transmitted 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, ensuring 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 key to the hybrid access point to the IoT The energy collected by IoT devices is mainly used to maintain circuit operation and data transmission. Node received power is an indicator for evaluating the strength of the device’s received signal, which is crucial for the device’s energy collection and function realization. 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 communication, providing a key basis for the system’s energy management and optimization.

[0166] 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 includes the following steps:

[0167] S601, extracting the energy transmission time of the hybrid access point to all IoT devices;

[0168] S602. Obtain information transmission time of each cluster of IoT devices to the hybrid access point;

[0169] S603: Obtain a reflection matrix of the energy reflection beam of each cluster of IoT devices on the intelligent reflection surface;

[0170] S604, obtaining the transmit power of the sensor node;

[0171] S605, obtaining a node receiving beam;

[0172] S606: Obtain unit bandwidth;

[0173] S607. 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;

[0174] The calculation formulas for the signals 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 are as follows:

[0175] ;

[0176] Where, For hybrid access points to receive signals from sensors, is the signal-to-noise ratio of IoT devices at 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 reflection matrix for intelligently reflecting energy reflection beams facing each cluster of IoT devices;

[0177] Those skilled in the art will appreciate 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 reflective surface reflects the signal transmitted by the node to the hybrid access point. At the same time, the collected energy is used 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 simultaneously transmit independent information to the hybrid access point. At the same time, the intelligent reflective surface reflects the transmitted 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, combined with the duration, , 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 IoT devices; the information transmission time is the key indicator for IoT devices 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 IoT devices. 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.

[0178] like Figure 6As shown, obtaining the energy transmission and information transmission time of each cluster and obtaining the optimal solution based on the consumption time includes the following steps:

[0179] The calculation formula for the total time of energy transmission and information transmission is:

[0180] ;

[0181] Among them, the minimum time of energy transfer and information transmission in the constraint model is:

[0182] ;

[0183] Among them, the minimum time under different hybrid access points is:

[0184] ;

[0185] 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, and 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 covered by it is determined. By calculation, the specific time required for each IoT device to receive energy from the hybrid access point is obtained, which provides 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. Based on 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. This 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 shortest minimum time is selected as the best charging location. Therefore, this location is regarded as the best choice for charging IoT devices. By obtaining the best choice for charging IoT devices in each cluster, with charging time as the weight, the moving paths of different hybrid access points are planned, which is the best solution.

[0186] like Figure 7 As shown, building a multi-directional constraint model based on known information includes the following steps:

[0187] Among them, the multi-directional constraint model is:

[0188] ;

[0189] Where, a signal-to-noise ratio of the hybrid access point, a maximum energy received power of the hybrid access point;

[0190] Those skilled in the art can understand that, guarantee that the energy consumption of the data transmission phase cannot be 0, denotes a time scheduling constraint, satisfies a quality of service constraint, denotes a minimum transmission power constraint of the hybrid access point, a constraint for receiving beamforming, denotes a unit modulus constraint of the phase shift of the intelligent reflecting surface.

[0191] In summary, the advantages of the present application are that: the present application solves the complex problem by dividing it into two stages, 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 processed for node allocation, and a plurality of cluster center positions available for the mobile hybrid access point to dock are output, then, for the positions of these cluster center nodes, the cluster center nodes are path planned and the positions of the nodes are sequentially output, the shortest path nodes are selected to form a Hamiltonian circuit, and the mobile hybrid access point will charge the Internet of Things devices in each cluster along the planned path; secondly, a model for minimizing charging time is established, 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 under the condition that other parameters are fixed, and then jointly solved until convergence, so that the hybrid access point can move flexibly to charge, the complex problem is divided into multiple sub-problems, which is convenient for separate solving and optimization, by introducing relaxation variables and semi-definite programming, the non-convex problem is converted into a convex problem or a more easily handled form, the solving efficiency is improved, and multiple factors such as beamforming, device transmission power, IRS phase shift and time scheduling are considered, the overall system performance is improved.

[0192] The basic principles, main features and advantages of the present application are shown and described above. Those skilled in the art should understand that the present application is not limited by the above examples, the above examples and descriptions in the specification are only the principles of the present application, various changes and improvements can be made to the present application without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection claimed by the present application is defined by the appended 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: S100, obtaining the number of hybrid access points and IoT devices in the area; S200, obtaining information about hybrid access points, smart reflective surfaces, and IoT devices; S300: Build a coordinate system for the region with the hybrid access point as the origin, obtain the coordinates of the IoT devices and the smart reflective surface in the corresponding coordinate system, and divide the IoT devices in the region into multiple clusters: S301, constructing a three-dimensional coordinate system with the hybrid access point as the origin; S302: Obtain the coordinates of the IoT device and the smart reflective surface in each coordinate system respectively; S303, dividing the IoT devices into multiple clusters according to the coordinates of each IoT device; S400, obtaining the node coordinates of the sensor nodes in each cluster, and obtaining multiple channel parameters based on the node coordinates and known information: 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 sensor node, and the distances from the hybrid access point to the sensor node; S404. Obtain 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 the channels from the hybrid access point to the smart reflective surface, the smart reflective surface to the sensor node, and the hybrid access point to the sensor node; S500, based on multiple channel parameters and information about the smart reflective surface, obtain energy collected by IoT devices, node received power, and node energy; S600: 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. S700: Obtain the energy transmission and information transmission time of each cluster, and obtain the optimal solution based on the time consumption: S701, obtaining the total time of each cluster of IoT devices in the energy transmission and information transmission stages; S702. Construct a multi-directional constraint model based on known information: 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 (SNR) of the hybrid access point, and make the SNR of the hybrid access point greater than a SNR of the IoT device at the hybrid access point. S7023. Obtain the maximum received power of the hybrid access point, and make the maximum received power of the hybrid access point greater than or equal to the modulus of the transmission beam vector; S7024. Extracting the modulus of the received beam and the unit mode of the phase shift of the smart reflector; S7025. Construct a multi-directional constraint model based on known information; Among them, the multi-directional constraint model is: ; Where, is the signal-to-noise ratio of the hybrid access point, is the maximum energy receiving power of the hybrid access point; S703, respectively obtaining the minimum time of the energy transmission and information transmission phases in the constraint model under different hybrid access points; S704. Compare the minimum time under different hybrid access points; S705, wherein the minimum time of different hybrid access points is the optimal charging position for each cluster; S706 , planning a path based on the optimal charging location of each cluster of different hybrid access points, which is the optimal solution; The calculation formula for the total time of energy transmission and information transmission 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: 。 2. The method for minimizing charging time of a WP-IoT based on a smart reflective surface according to claim 1, characterized in that: The hybrid access point is used as the origin to construct a coordinate system for the area, obtain the coordinates of the IoT devices and the smart reflective surface in the corresponding coordinate system, and divide the IoT devices in the area into multiple clusters. The following steps are involved: Among them, the coordinates of the smart reflective surface are 。 3. The method for minimizing charging time of a WP-IoT based on a smart reflective surface according to claim 2, characterized in that: The node coordinates of the sensor nodes in each cluster are obtained, and multiple channel parameters are obtained based on the node coordinates and known information. The following steps are involved: The channel calculation formulas from the hybrid access point to the smart reflective surface, the smart reflective surface to the sensor node, and the hybrid access point to the sensor node are as follows: ; Where, is the distance from the hybrid access point to the smart reflective surface, is the distance from the smart reflective surface to the kth sensor node, is the distance from the hybrid access point to the sensor node, is the channel from the hybrid access point to the smart reflective surface, is the channel from the smart reflector to the sensor node, is the channel from the hybrid access point to the sensor node, is the carrier wavelength, is the antenna distance, is the cosine value of the departure angle of the signal from the hybrid access point to the smart reflector link, is the cosine value of the departure angle of the smart reflector-node link signal, is the path loss per unit distance.

4. The method for minimizing charging time of a WP-IoT based on a smart reflective surface according to claim 3, characterized in that: The method of respectively obtaining the distances from the hybrid access point to the intelligent reflective surface, the distances from the intelligent reflective surface to the sensor node, and the distances from the hybrid access point to the sensor node comprises the following steps: The specific calculation formulas for respectively obtaining the distances from the hybrid access point to the intelligent reflective surface, the intelligent reflective surface to the sensor node, and the hybrid access point to the sensor node are: ; Where, 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 kth sensor 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 step of obtaining the node received signal, node received power, and node energy of the node based on the multiple channel parameters and in combination with the information of the smart reflective surface comprises the following steps: S501, obtaining an energy reflection beamforming matrix of a hybrid access point facing an intelligent reflection; S502: Obtain the duration of time during which the incident radio frequency signal is reflected by the smart reflective surface to the IoT device with energy harvesting function; S503, extracting the 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. Obtain the energy collected by the IoT device, the node received power, and the node energy based on the known information and the acquired information; The calculation formula for the node receiving signal is: ; The calculation formula for node received power is: ; The calculation formula of node energy is: ; Where, 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, It is a diagonal array of the energy reflection beamforming matrix facing the hybrid access point 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: Extracting the energy transmission time of the hybrid access point to all IoT devices, 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: S601, extracting the energy transmission time of the hybrid access point to all IoT devices; S602. Obtain information transmission time of each cluster of IoT devices to the hybrid access point; S603: Obtain a reflection matrix of the energy reflection beam of each cluster of IoT devices on the intelligent reflection surface; S604, obtaining the transmit power of the sensor node; S605, obtaining a node receiving beam; S606: Obtain unit bandwidth; S607. 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 formulas for the signals 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 are as follows: ; Where, For hybrid access points to receive signals from sensors, is the signal-to-noise ratio of IoT devices at 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 reflection matrix that reflects the energy of each cluster of IoT devices to the smart reflector.

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