CN116867090A - An Internet of Things resource allocation method, device, equipment and storage medium - Google Patents

Abstract

The invention relates to the field of wireless communication technology, and relates to an Internet of Things resource allocation method. For the Internet of Things network, an optimization condition is constructed with the goal of maximizing throughput, and a throughput iteration model is constructed according to the optimization conditions, combined with signals of the Internet of Things network. Transmit data, obtain the throughput data corresponding to the adjacent iteration numbers output by the throughput iteration model, and obtain the resource configuration parameters corresponding to the target iteration number, as the resource allocation strategy of the Internet of Things network to be allocated, through joint optimization of transmission energy And the time distribution of information, the phase shift of the intelligent reflective surface and the coordinates of the moving antenna maximize the throughput of the IoT network, improve the performance of the system, and further realize the reasonable allocation of IoT resources.

Description

An Internet of Things resource allocation method, device, equipment and storage medium

Technical field

The present invention relates to the field of wireless communication technology, and in particular to an Internet of Things resource allocation method, device, equipment and storage medium.

Background technique

The rapid development of the Internet of Things has brought about a surge in the number of devices connected to the Internet of Things, and remotely providing energy to IoT devices through radio frequency circuits is regarded as a solution to achieve the sustainable development of the Internet of Things. Specifically, first on the downlink energy transmission link, the hybrid access point is used to broadcast energy signals to the IoT devices within the broadcast range; then on the uplink information transmission link, the IoT devices use the collected energy signals as energy sources to the hybrid Access points transmit information. By integrating smart reflectors into the IoT wireless energy information transmission network and equipping each IoT device with a mobile antenna, it can effectively alleviate the energy attenuation during the transmission of energy and improve the receiving end signal during the transmission of information. Noise ratio, thereby improving the system throughput. However, how to jointly design the reflection phase of the smart reflective surface connected to the IoT network, the mobile antenna coordinates at the IoT device, and the energy information transmission time to achieve the throughput of the IoT network? Volume maximization has not been effectively studied.

Contents of the invention

Based on this, the purpose of the present invention is to provide an Internet of Things resource allocation method, device, equipment and storage medium, to construct optimization conditions targeting maximum throughput for the Internet of Things network, and to construct throughput iterations based on the optimization conditions. model, combined with the signal transmission data of the Internet of Things network, obtain the throughput data corresponding to the adjacent iteration numbers output by the throughput iteration model, to obtain the resource configuration parameters corresponding to the target iteration number, as the resources of the Internet of Things network to be allocated The allocation strategy maximizes the throughput of the IoT network, improves the performance of the system, and further realizes the use of IoT resources by jointly optimizing the time allocation of transmission energy and information, the phase shift of the smart reflector, and the coordinates of the moving antenna. reasonable distribution.

In the first aspect, embodiments of this application provide an Internet of Things resource allocation method, including the following steps:

Obtain signal transmission data of the Internet of Things network to be distributed, wherein the Internet of Things network includes a hybrid access point, a smart reflective surface, and several Internet of Things devices;

Construct optimization conditions with the goal of maximizing throughput, and construct a throughput iteration model according to the optimization conditions, wherein the throughput iteration model includes a data optimization module, an antenna field response vector calculation module, an energy consumption module, and a throughput calculation module;

Input the signal transmission data into the throughput iteration model to obtain resource configuration parameters corresponding to the current iteration number output by the data optimization module, where the resource configuration parameters are used to indicate the hybrid access point, intelligent Optimization results of resource configuration parameters of reflective surfaces and several IoT devices;

According to the resource configuration parameters and signal transmission data corresponding to the current iteration number, the antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module is obtained, wherein the antenna field response vector includes the hybrid interface The antenna field response vector of the entry point and the mobile antenna field response vector of each of the Internet of Things devices;

According to the resource configuration parameters and signal transmission data corresponding to the current iteration number, obtain the energy collection data of each of the Internet of Things devices corresponding to the current iteration number output by the energy consumption module;

According to the signal transmission data, the resource configuration parameters corresponding to the current iteration number, the antenna field response vector, and the energy collection data of each of the Internet of Things devices, the throughput data corresponding to the current iteration number output by the throughput calculation module is obtained;

Obtain the throughput data corresponding to the last iteration number, and determine whether convergence is based on the current iteration number and the throughput data corresponding to the previous iteration number. If convergence is achieved, obtain the resource configuration parameters corresponding to the current iteration number as the to-be-allocated Resource allocation strategies for IoT networks.

In the second aspect, embodiments of the present application provide an Internet of Things resource allocation device, including:

A data acquisition module, used to obtain signal transmission data of the Internet of Things network to be distributed, wherein the Internet of Things network includes a hybrid access point, an intelligent reflective surface, and several Internet of Things devices;

A model construction module, used to construct optimization conditions with the goal of maximizing throughput, and construct a throughput iteration model according to the optimization conditions, wherein the throughput iteration model includes a data optimization module, an antenna field response vector calculation module, and an energy consumption module and throughput calculation module;

A resource configuration parameter calculation module, configured to input the signal transmission data into the throughput iteration model and obtain the resource configuration parameters corresponding to the current number of iterations output by the data optimization module, where the resource configuration parameters are used to indicate Optimization results of resource configuration parameters of the hybrid access point, intelligent reflective surface and several Internet of Things devices;

The antenna field response vector calculation module is configured to obtain the antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module according to the resource configuration parameters and signal transmission data corresponding to the current iteration number, wherein, The antenna field response vector includes the antenna field response vector of the hybrid access point and the mobile antenna field response vector of each of the Internet of Things devices;

An energy collection calculation module, configured to obtain the energy collection data of each of the Internet of Things devices corresponding to the current iteration number output by the energy consumption module based on the resource configuration parameters and signal transmission data corresponding to the current iteration number;

A throughput calculation module, configured to obtain the current iteration output by the throughput calculation module based on the signal transmission data, the resource configuration parameters corresponding to the current iteration number, the antenna field response vector, and the energy collection data of each of the Internet of Things devices. Throughput data corresponding to times;

The resource allocation strategy output module is used to obtain the throughput data corresponding to the last iteration number. Based on the current iteration number and the throughput data corresponding to the previous iteration number, determine whether convergence occurs. If convergence occurs, obtain the resources corresponding to the current iteration number. Configuration parameters serve as the resource allocation strategy of the Internet of Things network to be allocated.

In a third aspect, embodiments of the present application provide a computer device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor; the computer program is used by the processor When executed, the steps of the Internet of Things resource allocation method described in the first aspect are implemented.

In a fourth aspect, embodiments of the present application provide a storage medium that stores a computer program. When the computer program is executed by a processor, the steps of the Internet of Things resource allocation method described in the first aspect are implemented.

In the embodiment of this application, an Internet of Things resource allocation method, device, equipment and storage medium are provided. For the Internet of Things network, optimization conditions with the goal of maximizing throughput are constructed, and a throughput iteration model is constructed according to the optimization conditions. Combined with the signal transmission data of the Internet of Things network, obtain the throughput data corresponding to the adjacent iteration numbers output by the throughput iteration model to obtain the resource configuration parameters corresponding to the target iteration number as the resource allocation strategy for the Internet of Things network to be allocated. , by jointly optimizing the time allocation of transmission energy and information, the phase shift of the intelligent reflector and the coordinates of the moving antenna, the throughput of the IoT network is maximized, the performance of the system is improved, and the rationalization of IoT resources is further realized distribute.

For better understanding and implementation, the present invention will be described in detail below with reference to the accompanying drawings.

Description of the drawings

Figure 1 is a schematic flow chart of an Internet of Things resource allocation method provided by an embodiment of the present application;

Figure 2 is a schematic flowchart of S3 in the Internet of Things resource allocation method provided by an embodiment of the present application;

Figure 3 is a schematic flowchart of S4 in the Internet of Things resource allocation method provided by an embodiment of the present application;

Figure 4 is a schematic flowchart of S5 in the Internet of Things resource allocation method provided by an embodiment of the present application;

Figure 5 is a schematic flowchart of S6 in the Internet of Things resource allocation method provided by an embodiment of the present application;

Figure 6 is a schematic structural diagram of an Internet of Things resource allocation device provided by an embodiment of the present application;

Figure 7 is a schematic structural diagram of a computer device provided by an embodiment of the present application.

Detailed ways

Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the appended claims.

The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a," "the" and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It will also be understood that the term "and/or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, the information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the present application, the first information may also be called second information, and similarly, the second information may also be called first information. Depending on the context, the words "if"/"if" as used herein may be interpreted as "when" or "when" or "in response to determining."

Please refer to Figure 1. Figure 1 is a schematic flowchart of an Internet of Things resource allocation method provided by an embodiment of the present application. The method includes the following steps:

S1: Obtain the signal transmission data of the IoT network to be allocated.

The execution subject of the Internet of Things resource allocation method is the allocation device of the Internet of Things resource allocation method (hereinafter referred to as the allocation device). The allocation device can be implemented by software and/or hardware, and the IoT resource allocation method can be implemented by software and/or hardware. The allocation device can be composed of two or more physical entities, or it can be composed of one physical entity. . The hardware pointed by the distribution device essentially refers to a computer device. For example, the distribution device can be a computer, a mobile phone, a tablet or an interactive tablet. In an optional embodiment, the distribution device may be a server, or a server cluster formed by combining multiple computer devices.

In this embodiment, the distribution device obtains the signal transmission data of the Internet of Things network to be distributed, where the Internet of Things network includes a hybrid access point, a smart reflective surface, and several Internet of Things devices. All IoT devices are configured with a single mobile antenna. Each IoT device first collects the energy radiated by the hybrid access point, and then uses the collected energy to transmit the collected information to the hybrid access point. At the same time, the smart reflector passes through Reflection of energy and information to enhance the system's energy collection and data transmission capabilities.

The signal transmission data includes a channel matrix of the Internet of Things network, a transmission power of the hybrid access point, an antenna channel path, and channel data of each of the Internet of Things devices, wherein the channel matrix includes the hybrid access point. The channel response matrix from the access point to each IoT device and the channel response matrix from the hybrid access point to the smart reflective surface. The channel data includes the mobile antenna channel path and the elevation and horizontal angles of several channel paths.

S2: Construct optimization conditions with the maximum throughput as the goal, and construct a throughput iteration model based on the optimization conditions.

By setting the time parameter , phase shift matrix/> and the coordinate parameters of k IoT devices/> The joint optimization of and the throughput is maximized. In this embodiment, the allocation device constructs optimization conditions with the goal of maximizing throughput, and constructs a throughput iteration model according to the optimization conditions, wherein the throughput iteration model includes a data optimization module , antenna field response vector calculation module, energy consumption module and throughput calculation module.

Specifically, the optimization condition for maximizing the throughput is:

In the formula, is the time parameter,/> It is a column vector with no subscript indication. Its elements include the energy transfer time parameters of the hybrid access point and the information transfer time parameters of K IoT devices. Its specific expression is as follows: , where the superscript T is the transpose symbol, /> is the energy transfer time parameter of the hybrid access point,/> is the information transmission time parameter of the k -th IoT device,/> is the information transmission time parameter of the Kth IoT device,/> is the phase shift matrix of the smart reflective surface,

Construct functions for matrices, /> is the amplitude reflection coefficient of the Nth reflection element of the smart reflective surface,/> is the phase shift offset coefficient of the Nth reflection element of the smart reflective surface.

r is the coordinate parameter, which is a column of vectors without subscript indication. Its elements include the coordinate parameters of K IoT devices. Its specific expression is as follows: ,/> for/> The transposition of /> is the coordinate parameter of the k -th IoT device, which is a column of vectors. Its elements include the abscissa parameter and ordinate parameter of the k- th IoT device. Its specific expression is as shown below:/> ,/> is the abscissa parameter in the coordinate parameters of the k -th IoT device,/> is the ordinate parameter among the coordinate parameters of the k -th IoT device,/> is the energy efficiency parameter of the k -th IoT device, is the mobile antenna field response vector of the k -th IoT device, P is the transmission power of the hybrid access point,/> is the antenna field response vector of the hybrid access point,/> is the transpose of the channel response matrix from the hybrid access point to the k -th IoT device, T is the transpose symbol,/> is the channel response matrix from the hybrid access point to the intelligent reflective surface,/> and/> Rayleigh fading channel models of corresponding dimensions can be used for modeling.

is the noise power,/> is the phase shift offset coefficient of the nth reflection element of the smart reflective surface,/> Represents the reflection phase constraints of all reflection elements of the intelligent reflective surface reflection matrix,/> Represents the overall constraint on transmission time,/> is the abscissa parameter in the coordinate parameters of the k -th IoT device, a is a preset positive integer,/> is the ordinate parameter among the coordinate parameters of the k -th IoT device,/> Represents the coordinate constraints of mobile antennas for all IoT devices.

In this embodiment, the distribution device establishes several sub-optimization conditions corresponding to the optimization model according to the optimization model as the data optimization module, wherein the sub-optimization conditions include the goal of maximizing the phase shift vector of the intelligent reflective surface. The sub-optimization condition of , the sub-optimization condition of the maximum coordinate parameter as the target, and the sub-optimization condition of the maximum time parameter as the target.

For the sub-optimization condition where the maximum phase shift matrix of the intelligent reflective surface is the target, the allocation device fixes the time parameter according to the optimization condition. , coordinate parameter r solves the phase shift matrix/> , first introduce the auxiliary row vector/> , , T is the transpose symbol, /> for/> The transposition of /> is the Hadamard product, and then introduce the auxiliary column vector v ,/> , quantize the column vectorization of the diagonal elements of the smart reflective surface reflection matrix, and then use/> replace Substituting the optimization condition with the maximum throughput as the target, construct a sub-optimization condition in which the phase shift vector of the intelligent reflective surface corresponding to the optimization condition with the maximum throughput as the target is the target, wherein the phase shift vector is the target with the maximum The sub-optimization conditions are:

In the formula, is the first auxiliary variable,/> is the auxiliary row vector of the kth IoT device, ,/> is the Hadamard product, v is the auxiliary column vector, /> ,/> is the phase shift offset coefficient of the Nth reflection element of the smart reflective surface,/> To relax constraints,/> is the nth element in the column vector, w is the preset local point, /> Represents taking the real part of a complex number,/> for/> The conjugate of is the conjugate transpose of w .

For the sub-optimization condition where the maximum coordinate parameter is the target, the equipment is assigned to fix the time parameter according to the optimization condition. and phase shift matrix/> , to solve the coordinate parameter r , first introduce the auxiliary row variable/> ,/> , construct the sub-optimization condition in which the maximum coordinate parameter is the target corresponding to the optimization condition in which the maximum throughput is the target, wherein the sub-optimization condition in which the maximum coordinate parameter is the target is:

In the formula, and/> Indicates the introduction of auxiliary vectors/> ,/> To loosen constraints, .

For the sub-optimization condition where the maximum time parameter is the target, the allocation device fixes the phase shift matrix according to the optimization condition. , coordinate parameter r solution time parameter/> , constructing the sub-optimization condition corresponding to the optimization condition with the maximum throughput as the target and the maximum time parameter as the target, where the sub-optimization condition with the maximum time parameter as the target is:

In the formula, is the second auxiliary variable,/> .

S3: Input the signal transmission data into the throughput iteration model, and obtain the resource configuration parameters corresponding to the current iteration number output by the data optimization module.

The resource configuration parameters are used to indicate the optimization results of the resource configuration parameters of the hybrid access point, the intelligent reflective surface, and several Internet of Things devices.

In this embodiment, the distribution device inputs the signal transmission data into the throughput iteration model to obtain resource configuration parameters corresponding to the current number of iterations output by the data optimization module, wherein the resource configuration parameters include the The phase shift matrix of the smart reflective surface, the coordinate parameters and time parameters of each of the Internet of Things devices.

Please refer to Figure 2. Figure 2 is a schematic flowchart of S3 in the Internet of Things resource allocation method provided by an embodiment of the present application, including steps S31~S33, specifically as follows:

S31: Obtain the phase shift vector according to the signal transmission data and the preset sub-optimization condition in which the maximum phase shift vector is the target, perform diagonal calculation on the phase shift vector, and obtain the phase shift matrix.

In this embodiment, the distribution device adopts the successive convex approximation algorithm to obtain the phase shift vector according to the signal transmission data and the preset sub-optimization condition where the maximum phase shift vector is the target. , for the phase shift vector/> Perform normalization processing to obtain the normalization processing result, and then perform diagonal calculation on the normalization processing result to obtain the phase shift matrix .

S32: Obtain the coordinate parameters of each of the Internet of Things devices according to the signal transmission data and the preset sub-optimization condition where the maximum coordinate parameter is the target.

Genetic algorithms are suitable for solving the optimization conditions of multimodal functions with only single real variables and domain constraints. Genetic algorithms can effectively overcome the problem of local optimal solutions by simultaneously exploring multiple solutions in the search space through multiple individuals in the population.

In this embodiment, the distribution device uses a genetic algorithm to obtain the coordinate parameters of each of the Internet of Things devices based on the signal transmission data and the preset sub-optimization conditions where the maximum coordinate parameter is the target. .

S33: Obtain the time parameter according to the signal transmission data and the preset sub-optimization condition where the maximum time parameter is the target.

In this embodiment, the distribution device uses the Lagrange multiplier method to obtain the time parameters according to the signal transmission data and the preset sub-optimization conditions with the maximum time parameter as the target, where the time parameters include hybrid interfaces. The energy transfer time parameters of the entry point and the information transfer time parameters of the IoT device are as follows:

In the formula, is the energy transfer time parameter of the hybrid access point,/> is the third auxiliary variable and is the formula solution,/> is the information transmission time parameter of the Internet of Things device.

S4: According to the resource configuration parameters and signal transmission data corresponding to the current iteration number, obtain the antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module.

In this embodiment, the distribution device obtains the antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module according to the resource configuration parameters and signal transmission data corresponding to the current iteration number, wherein, the antenna field response vector The field response vector includes the antenna field response vector of the hybrid access point and the mobile antenna field response vector of each of the Internet of Things devices.

Please refer to Figure 3. Figure 3 is a schematic flowchart of S4 in the Internet of Things resource allocation method provided by an embodiment of the present application, including steps S41~S42, specifically as follows:

S41: Obtain the antenna field response vector of the hybrid access point according to the signal transmission data and the preset first antenna field response vector calculation algorithm.

The first antenna field response vector calculation algorithm is:

In the formula, is the antenna field response vector of the hybrid access point,/> is the antenna channel path of the hybrid access point, is the first-dimensional channel response matrix from the hybrid access point to the smart reflective surface.

In this embodiment, the distribution device obtains the antenna field response vector of the hybrid access point based on the signal transmission data and the preset first antenna field response vector calculation algorithm.

S42: Obtain the mobile antenna field response vector of each of the Internet of Things devices according to the signal transmission data, resource configuration parameters and the preset second antenna field response vector calculation algorithm.

The second antenna field response vector calculation algorithm is:

In the formula, is the mobile antenna field response vector corresponding to the coordinate parameters of the k -th IoT device,/> is the coordinate parameter of the k -th IoT device, K is the total number of IoT devices,/> is the carrier wavelength,/> is the /> of the k -th IoT device The influence of channel phase,/> is the mobile antenna channel path of the k -th IoT device.

In this embodiment, the distribution device obtains the mobile antenna field response vector of each of the Internet of Things devices based on the signal transmission data, resource configuration parameters and the preset second antenna field response vector calculation algorithm.

S5: According to the resource configuration parameters and signal transmission data corresponding to the current iteration number, obtain the energy collection data of each of the Internet of Things devices corresponding to the current iteration number output by the energy consumption module.

In this embodiment, the distribution device obtains the energy collection data of each of the Internet of Things devices corresponding to the current iteration number output by the energy consumption module based on the resource configuration parameters and signal transmission data corresponding to the current iteration number.

Please refer to Figure 4. Figure 4 is a schematic flowchart of S5 in the Internet of Things resource allocation method provided by an embodiment of the present application, including step S51, as follows:

S51: Obtain the energy collection data of each of the Internet of Things devices according to the signal transmission data, resource configuration parameters and the preset energy collection calculation algorithm.

The energy collection calculation algorithm is:

In the formula, Collect energy data for the kth IoT device,/> is the phase shift matrix of the smart reflective surface.

In this embodiment, the distribution device obtains the energy collection data of each of the Internet of Things devices based on the signal transmission data, resource configuration parameters and the preset energy collection calculation algorithm.

S6: Obtain the throughput corresponding to the current iteration number output by the throughput calculation module according to the signal transmission data, the resource configuration parameters corresponding to the current iteration number, the antenna field response vector, and the energy collection data of each of the Internet of Things devices. data.

In this embodiment, the distribution device obtains the current output of the throughput calculation module based on the signal transmission data, the resource configuration parameters corresponding to the current iteration number, the antenna field response vector, and the energy collection data of each of the Internet of Things devices. Throughput data corresponding to the number of iterations.

Please refer to Figure 5. Figure 5 is a schematic flowchart of S6 in the Internet of Things resource allocation method provided by an embodiment of the present application, including step S61, as follows:

S61: Obtain the throughput data of each of the Internet of Things devices according to the signal transmission data, resource configuration parameters, antenna field response vectors, energy collection data of each of the Internet of Things devices, and the preset throughput calculation algorithm.

The throughput calculation algorithm is:

In the formula, is the throughput data of the k -th IoT device,/> is the time parameter, .

In this embodiment, the distribution device obtains each of the Internet of Things devices based on the signal transmission data, resource configuration parameters, antenna field response vectors, energy collection data of each of the Internet of Things devices, and a preset throughput calculation algorithm. throughput data.

S7: Obtain the throughput data corresponding to the last iteration number, and determine whether convergence is achieved based on the current iteration number and the throughput data corresponding to the previous iteration number. If convergence is achieved, obtain the resource configuration parameters corresponding to the current iteration number as the above Resource allocation strategy for the IoT network to be allocated.

In this embodiment, the allocation device obtains the throughput data corresponding to the last iteration number, and based on the current iteration number, the throughput data corresponding to the last iteration number and the preset threshold , determine whether it converges. Specifically, the allocation device obtains the absolute value of the difference between the throughput data corresponding to the current iteration number and the previous iteration number. If the absolute value is less than the threshold, convergence is determined. If the absolute value is greater than or equal to The threshold value determines that there is no convergence.

If there is no convergence, the allocation device obtains the throughput data corresponding to the next iteration number, and repeats the convergence judgment until the iteration number, and stops calculation; if it converges, the allocation device obtains the resource configuration parameters corresponding to the current iteration number as the to-be-determined number. Resource allocation strategies for allocated IoT networks.

Please refer to Figure 6. Figure 6 is a schematic structural diagram of an Internet of Things resource allocation device provided by an embodiment of the present application. The device can implement all or part of the Internet of Things resource allocation device through software, hardware, or a combination of both. The device 6 include:

The data acquisition module 61 is used to obtain the signal transmission data of the Internet of Things network to be distributed, wherein the Internet of Things network includes a hybrid access point, an intelligent reflective surface, and several Internet of Things devices;

The model construction module 62 is used to construct optimization conditions with the goal of maximizing throughput, and construct a throughput iteration model according to the optimization conditions, wherein the throughput iteration model includes a data optimization module, an antenna field response vector calculation module, and an energy consumption module. module and throughput calculation module;

The resource configuration parameter calculation module 63 is used to input the signal transmission data into the throughput iteration model and obtain the resource configuration parameters corresponding to the current iteration number output by the data optimization module, wherein the resource configuration parameters are used for Indicate the optimization results of resource configuration parameters of the hybrid access point, intelligent reflective surface and several Internet of Things devices;

The antenna field response vector calculation module 64 is configured to obtain the antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module according to the resource configuration parameters and signal transmission data corresponding to the current iteration number, wherein, The antenna field response vector includes the antenna field response vector of the hybrid access point and the mobile antenna field response vector of each of the Internet of Things devices;

The energy collection calculation module 65 is configured to obtain the energy collection data of each of the Internet of Things devices corresponding to the current iteration number output by the energy consumption module according to the resource configuration parameters and signal transmission data corresponding to the current iteration number;

The throughput calculation module 66 is configured to obtain the current output of the throughput calculation module based on the signal transmission data, the resource configuration parameters corresponding to the current iteration number, the antenna field response vector, and the energy collection data of each of the Internet of Things devices. Throughput data corresponding to the number of iterations;

The resource allocation strategy output module 67 is used to obtain the throughput data corresponding to the last iteration number, and determine whether convergence is based on the current iteration number and the throughput data corresponding to the previous iteration number. If convergence is achieved, obtain the throughput data corresponding to the current iteration number. Resource configuration parameters serve as the resource allocation strategy of the Internet of Things network to be allocated.

In this embodiment, the signal transmission data of the Internet of Things network to be distributed is obtained through the data acquisition module, where the Internet of Things network includes a hybrid access point, an intelligent reflective surface, and several Internet of Things devices; through the model building module , construct optimization conditions with the goal of maximizing throughput, and construct a throughput iteration model according to the optimization conditions, wherein the throughput iteration model includes a data optimization module, an antenna field response vector calculation module, an energy consumption module, and a throughput calculation module ; Through the resource configuration parameter calculation module, input the signal transmission data to the throughput iteration model to obtain the resource configuration parameters corresponding to the current iteration number output by the data optimization module, where the resource configuration parameters are used to indicate Optimization results of the resource configuration parameters of the hybrid access point, smart reflective surface and several Internet of Things devices; through the antenna field response vector calculation module, based on the resource configuration parameters and signal transmission data corresponding to the current iteration number, the obtained The antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module, wherein the antenna field response vector includes the antenna field response vector of the hybrid access point and the mobile antenna field of each of the Internet of Things devices Response vector; Through the energy collection calculation module, according to the resource configuration parameters and signal transmission data corresponding to the current iteration number, the energy collection data of each of the Internet of Things devices corresponding to the current iteration number output by the energy consumption module is obtained; through The throughput calculation module obtains the current iteration number output by the throughput calculation module based on the signal transmission data, the resource configuration parameters corresponding to the current iteration number, the antenna field response vector, and the energy collection data of each of the Internet of Things devices. The throughput data of The resource configuration parameters corresponding to the number of times are used as the resource allocation strategy of the Internet of Things network to be allocated. For the Internet of Things network, construct optimization conditions with the goal of maximizing throughput, build a throughput iteration model based on the optimization conditions, and combine the signal transmission data of the Internet of Things network to obtain the throughput corresponding to the number of adjacent iterations output by the throughput iteration model. Amount of data to obtain the resource configuration parameters corresponding to the target iteration number, as the resource allocation strategy of the IoT network to be allocated, by jointly optimizing the time allocation of transmission energy and information, the phase shift of the smart reflective surface and the coordinates of the mobile antenna In this way, the throughput of the IoT network is maximized, the performance of the system is improved, and the reasonable allocation of IoT resources is further realized.

Please refer to Figure 7, which is a schematic structural diagram of a computer device provided by an embodiment of the present application. The computer device 7 includes: a processor 71, a memory 72, and a computer program 73 stored on the memory 72 and executable on the processor 71. ; The computer device can store multiple instructions, and the instructions are suitable for the processor 71 to load and execute the method steps of the embodiments shown in Figures 1 to 6. The specific execution process can be seen in the specific descriptions of Figures 1 to 6, here No further details will be given.

Among them, the processor 71 may include one or more processing cores. The processor 71 uses various interfaces and lines to connect various parts in the server, and executes the Internet of Things resource allocation device by running or executing instructions, programs, code sets or instruction sets stored in the memory 72, and calling data in the memory 72. 6. Various functions and data processing. Optionally, the processor 71 can adopt digital signal processing (Digital Signal Processing, DSP), field-programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programble Logic Array (PLA) in at least one hardware form. The processor 71 may integrate one or a combination of a central processing unit 71 (Central Processing Unit, CPU), a graphics processor 71 (Graphics Processing Unit, GPU), a modem, and the like. Among them, the CPU mainly handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content that needs to be displayed on the touch screen; the modem is used to handle wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 71 and may be implemented by a separate chip.

The memory 72 may include a random access memory 72 (Random Access Memory, RAM) or a read-only memory 72 (Read-Only Memory). Optionally, the memory 72 includes non-transitory computer-readable storage medium. Memory 72 may be used to store instructions, programs, codes, sets of codes, or sets of instructions. The memory 72 may include a program storage area and a data storage area, where the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch instructions, etc.), and instructions for implementing each of the above method embodiments. instructions, etc.; the storage data area can store data, etc. involved in each of the above method embodiments. The memory 72 may optionally be at least one storage device located remotely from the aforementioned processor 71 .

Embodiments of the present application also provide a storage medium. The storage medium can store multiple instructions. The instructions are suitable for the processor to load and execute the method steps of the embodiments shown in FIG. 1 to FIG. 6. Specifically, For the process, please refer to the detailed description of Figures 1 to 6 and will not be described again here.

Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above functional units and modules is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs. Module completion means dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be hardware-based. It can also be implemented in the form of software functional units. In addition, the specific names of each functional unit and module are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present application. For the specific working processes of the units and modules in the above system, please refer to the corresponding processes in the foregoing method embodiments, and will not be described again here.

In the above embodiments, each embodiment is described with its own emphasis. For parts that are not detailed or documented in a certain embodiment, please refer to the relevant descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each specific application, but such implementations should not be considered to be beyond the scope of the present invention.

In the embodiments provided by the present invention, it should be understood that the disclosed apparatus/terminal equipment and methods can be implemented in other ways. For example, the apparatus/terminal equipment embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units. Or components can be combined or can be integrated into another system, or some features can be omitted, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or software functional units.

If the integrated module/unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can be stored in a computer-readable storage medium. When the program is executed by the processor, the steps of each of the above method embodiments can be implemented. Wherein, the computer program includes computer program code, which may be in the form of source code, object code, executable file or some intermediate form.

The present invention is not limited to the above-described embodiments. As long as various changes or deformations can be made to the present invention without departing from the spirit and scope of the present invention, and if these changes and deformations fall within the claims and equivalent technical scope of the present invention, the present invention will also be considered. These modifications and variations are intended to be included.

Claims (10)

1. An Internet of Things resource allocation method, characterized by including the following steps: Obtain signal transmission data of the Internet of Things network to be distributed, wherein the Internet of Things network includes a hybrid access point, a smart reflective surface, and several Internet of Things devices; Construct optimization conditions with the goal of maximizing throughput, and construct a throughput iteration model according to the optimization conditions, wherein the throughput iteration model includes a data optimization module, an antenna field response vector calculation module, an energy consumption module, and a throughput calculation module; Input the signal transmission data into the throughput iteration model to obtain resource configuration parameters corresponding to the current number of iterations output by the data optimization module; wherein the resource configuration parameters are used to indicate the hybrid access point, intelligent Optimization results of resource configuration parameters of reflective surfaces and several IoT devices; According to the resource configuration parameters and signal transmission data corresponding to the current iteration number, the antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module is obtained, wherein the antenna field response vector includes the hybrid interface The antenna field response vector of the entry point and the mobile antenna field response vector of each of the Internet of Things devices; According to the resource configuration parameters and signal transmission data corresponding to the current iteration number, obtain the energy collection data of each of the Internet of Things devices corresponding to the current iteration number output by the energy consumption module; According to the signal transmission data, the resource configuration parameters corresponding to the current iteration number, the antenna field response vector, and the energy collection data of each of the Internet of Things devices, the throughput data corresponding to the current iteration number output by the throughput calculation module is obtained; Obtain the throughput data corresponding to the last iteration number, and determine whether convergence is based on the current iteration number and the throughput data corresponding to the previous iteration number. If convergence is achieved, obtain the resource configuration parameters corresponding to the current iteration number as the to-be-allocated Resource allocation strategies for IoT networks. 2. The Internet of Things resource allocation method according to claim 1, characterized in that the optimization condition for maximizing the throughput is: In the formula, is the time parameter,/> It is a column vector with no subscript indication. Its elements include the energy transfer time parameters of the hybrid access point and the information transfer time parameters of K IoT devices. Its specific expression is as follows: , where the superscript T is the transpose symbol, /> is the energy transfer time parameter of the hybrid access point,/> is the information transmission time parameter of the k -th IoT device,/> is the information transmission time parameter of the Kth IoT device,/> is the phase shift matrix of the smart reflective surface, r is the coordinate parameter, which is a column of vectors without subscript indication. Its elements include the coordinate parameters of K IoT devices, and their specific expression is as shown below:/> ,/> is the coordinate parameter of the k -th IoT device, which is a column of vectors. Its elements include the abscissa parameter and ordinate parameter of the k- th IoT device. Its specific expression is as shown below:/> ,/> is the abscissa parameter in the coordinate parameters of the k -th IoT device,/> is the ordinate parameter among the coordinate parameters of the k -th IoT device,/> is the energy efficiency parameter of the k -th IoT device,/> is the mobile antenna field response vector of the k -th IoT device, P is the transmission power of the hybrid access point,/> is the antenna field response vector of the hybrid access point,/> is the transpose of the channel response matrix from the hybrid access point to the k -th IoT device, T is the transpose symbol,/> is the channel response matrix from the hybrid access point to the intelligent reflective surface,/> is the noise power, is the phase shift offset coefficient of the nth reflection element of the smart reflective surface,/> Represents the reflection phase constraints of all reflection elements of the smart reflection surface reflection matrix,/> Represents the overall constraint of transmission time, a is a preset positive integer,/> Represents the coordinate constraints of mobile antennas for all IoT devices. 3. The Internet of Things resource allocation method according to claim 2, characterized in that: the signal transmission data includes the channel matrix of the Internet of Things network, the transmission power of the hybrid access point, the antenna channel path and each The channel data of the Internet of Things device, wherein the channel matrix includes a channel response matrix from the hybrid access point to each Internet of Things device and a channel response matrix from the hybrid access point to the intelligent reflective surface, the channel data Including the mobile antenna channel path and the elevation and horizontal angles of several channel paths. 4. The Internet of Things resource allocation method according to claim 3, characterized in that: the resource configuration parameters include a phase shift matrix of the intelligent reflective surface, coordinate parameters and time parameters of each of the Internet of Things devices; Inputting the signal transmission data into the throughput iteration model and obtaining resource configuration parameters corresponding to the current iteration number output by the data optimization module includes the steps: According to the signal transmission data and the preset sub-optimization condition in which the maximum phase shift vector is the target, a phase shift vector is obtained, a diagonal calculation is performed on the phase shift vector, and a phase shift matrix is obtained, wherein the phase shift vector The sub-optimization condition with the maximum goal is: In the formula, is the first auxiliary variable,/> is the auxiliary row vector of the kth IoT device, , T is the transpose symbol, /> for/> The transposition of /> is the Hadamard product, v is the auxiliary column vector, /> ,/> is the phase shift offset coefficient of the Nth reflection element of the smart reflective surface,/> To relax constraints,/> is the nth element in the column vector, w is the preset local point, /> Represents taking the real part of a complex number,/> for/> The conjugate of is the conjugate transpose of w ; According to the signal transmission data and the preset sub-optimization condition that the maximum coordinate parameter is the target, the coordinate parameters of each of the Internet of Things devices are obtained, wherein the sub-optimization condition that the coordinate parameter is the maximum target is: In the formula, and/> Indicates the introduction of auxiliary vectors/> ,/> To loosen constraints, ; Time parameters are obtained according to the signal transmission data and the preset sub-optimization condition with the maximum time parameter as the target, where the time parameters include the energy transfer time parameter of the hybrid access point and the information transfer time parameter of the Internet of Things device, The sub-optimization condition where the maximum time parameter is the target is: In the formula, is the second auxiliary variable,/> ; The time parameters are: In the formula, is the energy transfer time parameter of the hybrid access point,/> is the third auxiliary variable and is the formula solution,/> is the information transmission time parameter of the Internet of Things device. 5. The Internet of Things resource allocation method according to claim 4, characterized in that the current iteration output by the antenna field response vector calculation module is obtained according to the resource configuration parameters and signal transmission data corresponding to the current iteration number. The antenna field response vector corresponding to the order, including steps: According to the signal transmission data and the preset first antenna field response vector calculation algorithm, the antenna field response vector of the hybrid access point is obtained, wherein the first antenna field response vector calculation algorithm is: In the formula, is the antenna field response vector of the hybrid access point,/> is the antenna channel path of the hybrid access point,/> is the first-dimensional channel response matrix from the hybrid access point to the smart reflective surface; According to the signal transmission data, resource configuration parameters and the preset second antenna field response vector calculation algorithm, the mobile antenna field response vector of each of the Internet of Things devices is obtained, wherein the second antenna field response vector calculation algorithm is : In the formula, is the mobile antenna field response vector corresponding to the coordinate parameters of the k -th IoT device,/> is the coordinate parameter of the k -th IoT device, K is the total number of IoT devices,/> is the carrier wavelength,/> is the /> of the k -th IoT device The influence of channel phase,/> is the mobile antenna channel path of the k -th IoT device. 6. The Internet of Things resource allocation method according to claim 5, characterized in that, according to the resource configuration parameters and signal transmission data corresponding to the current iteration number, the energy consumption module output corresponding to the current iteration number is obtained. The energy collection data of each of the Internet of Things devices includes steps: According to the signal transmission data, resource configuration parameters and the preset energy collection calculation algorithm, the energy collection data of each of the Internet of Things devices is obtained, wherein the energy collection calculation algorithm is: In the formula, Collect energy data for the kth IoT device,/> is the phase shift matrix of the smart reflective surface. 7. The Internet of Things resource allocation method according to claim 6, characterized in that the resource configuration parameters corresponding to the signal transmission data, the current iteration number, the antenna field response vector and the energy of each of the Internet of Things devices Collecting data to obtain throughput data corresponding to the current number of iterations output by the throughput calculation module includes the steps: According to the signal transmission data, resource configuration parameters, antenna field response vectors, energy collection data of each of the Internet of Things devices, and a preset throughput calculation algorithm, the throughput data of each of the Internet of Things devices is obtained, where, The throughput calculation algorithm is: In the formula, is the throughput data of the k -th IoT device,/> is the time parameter, . 8. An Internet of Things resource allocation device, characterized by including: A data acquisition module, used to obtain signal transmission data of the Internet of Things network to be distributed, wherein the Internet of Things network includes a hybrid access point, an intelligent reflective surface, and several Internet of Things devices; A model construction module, used to construct optimization conditions with the goal of maximizing throughput, and construct a throughput iteration model according to the optimization conditions, wherein the throughput iteration model includes a data optimization module, an antenna field response vector calculation module, and an energy consumption module and throughput calculation module; A resource configuration parameter calculation module, configured to input the signal transmission data into the throughput iteration model and obtain the resource configuration parameters corresponding to the current number of iterations output by the data optimization module, where the resource configuration parameters are used to indicate Optimization results of resource configuration parameters of the hybrid access point, intelligent reflective surface and several Internet of Things devices; The antenna field response vector calculation module is configured to obtain the antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module according to the resource configuration parameters and signal transmission data corresponding to the current iteration number, wherein, The antenna field response vector includes the antenna field response vector of the hybrid access point and the mobile antenna field response vector of each of the Internet of Things devices; An energy collection calculation module, configured to obtain the energy collection data of each of the Internet of Things devices corresponding to the current iteration number output by the energy consumption module based on the resource configuration parameters and signal transmission data corresponding to the current iteration number; A throughput calculation module, configured to obtain the current iteration output by the throughput calculation module based on the signal transmission data, the resource configuration parameters corresponding to the current iteration number, the antenna field response vector, and the energy collection data of each of the Internet of Things devices. Throughput data corresponding to times; The resource allocation strategy output module is used to obtain the throughput data corresponding to the last iteration number. Based on the current iteration number and the throughput data corresponding to the previous iteration number, determine whether convergence occurs. If convergence occurs, obtain the resources corresponding to the current iteration number. Configuration parameters serve as the resource allocation strategy of the Internet of Things network to be allocated. 9. A computer device, characterized in that it includes: a processor, a memory, and a computer program stored on the memory and executable on the processor; when the computer program is executed by the processor, the following is implemented: The steps of the Internet of Things resource allocation method according to any one of claims 1 to 7. 10. A storage medium, characterized in that: the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the Internet of Things resource allocation method according to any one of claims 1 to 7 are implemented.