CN116867090A - Internet of things resource allocation method, device, equipment and storage medium - Google Patents
Internet of things resource allocation method, device, equipment and storage medium Download PDFInfo
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- H04W72/535—Allocation or scheduling criteria for wireless resources based on resource usage policies
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract
The invention relates to the technical field of wireless communication, in particular to a resource allocation method of the Internet of things, which aims at an Internet of things network, an optimization condition aiming at the maximum throughput is established, a throughput iteration model is established according to the optimization condition, throughput data corresponding to adjacent iteration times output by the throughput iteration model is obtained by combining signal transmission data of the Internet of things network, resource allocation parameters corresponding to the target iteration times are obtained, and the resource allocation strategy of the Internet of things network to be allocated is used for realizing the maximization of the throughput of the Internet of things network and further realizing the reasonable allocation of resources of the Internet of things by combining the mode of optimizing the time allocation of transmission energy and information and the phase shift of an intelligent reflecting surface and the coordinates of a mobile antenna.
Description
Technical Field
The present invention relates to the field of wireless communications technologies, and in particular, to a method, an apparatus, a device, and a storage medium for allocating resources of an internet of things.
Background
The rapid development of the internet of things brings about the proliferation of accessing internet of things equipment, and the remote energy supply of the internet of things equipment through a radio frequency circuit is regarded as a solution for realizing the sustainable development of the internet of things. Specifically, firstly, broadcasting energy signals for the Internet of things equipment in a broadcasting range on a downlink energy transmission link by means of a hybrid access point; and then, an uplink information transmission link is used for transmitting information to the hybrid access point by the Internet of things equipment by taking the collected energy signal as an energy source. And through integrating the intelligent reflecting surface into the wireless energy supply information transmission network of the Internet of things, and providing a mobile antenna at each equipment end of the Internet of things, the energy attenuation in the energy transmission process can be effectively relieved, and the signal to noise ratio of a receiving end in the information transmission process can be improved, so that the sum throughput of the system is improved, however, how to jointly design the reflecting phase of the intelligent reflecting surface in the Internet of things, the coordinates of the mobile antenna at the equipment of the Internet of things and the energy information transmission time to realize the throughput maximization of the Internet of things has not been studied effectively.
Disclosure of Invention
Based on this, the application aims to provide a method, a device, equipment and a storage medium for allocating resources of the internet of things, aiming at the internet of things, an optimization condition aiming at the maximum throughput is established, a throughput iteration model is established according to the optimization condition, throughput data corresponding to the adjacent iteration times output by the throughput iteration model is obtained by combining signal transmission data of the internet of things, resource configuration parameters corresponding to the target iteration times are obtained, and the resource configuration parameters are used as a resource allocation strategy of the internet of things to be allocated, and the throughput maximization of the internet of things is realized by combining the mode of optimizing the time allocation of transmission energy and information, the phase shift of an intelligent reflecting surface and the coordinate of a mobile antenna, the performance of the system is improved, and the reasonable allocation of the resources of the internet of things is further realized.
In a first aspect, an embodiment of the present application provides a method for allocating resources of an internet of things, including the following steps:
acquiring signal transmission data of an Internet of things network to be distributed, wherein the Internet of things network comprises a hybrid access point, an intelligent reflecting surface and a plurality of Internet of things devices;
constructing an optimization condition with the maximum throughput as a target, and constructing a throughput iteration model according to the optimization condition, wherein the throughput iteration model comprises a data optimization module, an antenna field response vector calculation module, an energy consumption module and a throughput calculation module;
Inputting the signal transmission data to the throughput iterative model to obtain a resource configuration parameter corresponding to the current iteration number output by the data optimization module, wherein the resource configuration parameter is used for indicating the optimization results of the resource configuration parameters of the hybrid access point, the intelligent reflection surface and the plurality of internet of things devices;
according to the resource configuration parameters corresponding to the current iteration times and the signal transmission data, obtaining an antenna field response vector corresponding to the current iteration times output by the antenna field response vector calculation module, wherein the antenna field response vector comprises an antenna field response vector of the hybrid access point and a mobile antenna field response vector of each Internet of things device;
according to the resource configuration parameters corresponding to the current iteration times and the signal transmission data, obtaining energy acquisition data of each Internet of things device corresponding to the current iteration times output by the energy consumption module;
obtaining throughput data corresponding to the current iteration times output by the throughput calculation module according to the signal transmission data, the resource configuration parameters corresponding to the current iteration times, the antenna field response vector and the energy acquisition data of each Internet of things device;
And obtaining throughput data corresponding to the last iteration number, judging whether convergence is carried out according to the current iteration number and the throughput data corresponding to the last iteration number, and if so, obtaining a resource configuration parameter corresponding to the current iteration number as a resource allocation strategy of the Internet of things network to be allocated.
In a second aspect, an embodiment of the present application provides an apparatus for allocating resources of an internet of things, including:
the system comprises a data acquisition module, a data distribution module and a data distribution module, wherein the data acquisition module is used for acquiring signal transmission data of an Internet of things network to be distributed, and the Internet of things network comprises a hybrid access point, an intelligent reflecting surface and a plurality of Internet of things devices;
the model construction module is used for constructing an optimization condition with the maximum throughput as a target, and constructing a throughput iteration model according to the optimization condition, wherein the throughput iteration model comprises a data optimization module, an antenna field response vector calculation module, an energy consumption module and a throughput calculation module;
the resource configuration parameter calculation module is used for inputting the signal transmission data into the throughput iteration model to obtain a resource configuration parameter corresponding to the current iteration number output by the data optimization module, wherein the resource configuration parameter is used for indicating the optimization results of the resource configuration parameters of the hybrid access point, the intelligent reflecting surface and the plurality of internet of things devices;
The antenna field response vector calculation module is used for obtaining an antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module according to the resource configuration parameter corresponding to the current iteration number and signal transmission data, wherein the antenna field response vector comprises an antenna field response vector of the hybrid access point and a mobile antenna field response vector of each Internet of things device;
the energy acquisition calculation module is used for acquiring energy acquisition data of each Internet of things device corresponding to the current iteration number output by the energy consumption module according to the resource configuration parameters corresponding to the current iteration number and the signal transmission data;
the throughput computing module is used for obtaining throughput data corresponding to the current iteration times output by the throughput computing module according to the signal transmission data, the resource configuration parameters corresponding to the current iteration times, the antenna field response vector and the energy acquisition data of each Internet of things device;
and the resource allocation strategy output module is used for obtaining throughput data corresponding to the last iteration number, judging whether the current iteration number and the throughput data corresponding to the last iteration number are converged or not according to the current iteration number and the throughput data corresponding to the last iteration number, and if so, obtaining a resource allocation parameter corresponding to the current iteration number as a resource allocation strategy of the Internet of things network to be allocated.
In a third aspect, an embodiment of the present application provides a computer apparatus, including: a processor, a memory, and a computer program stored on the memory and executable on the processor; the computer program when executed by the processor implements the steps of the internet of things resource allocation method according to the first aspect.
In a fourth aspect, an embodiment of the present application provides a storage medium storing a computer program, where the computer program implements the steps of the method for allocating resources of the internet of things according to the first aspect when executed by a processor.
In the embodiment of the application, an internet of things resource allocation method, device, equipment and storage medium are provided, aiming at an internet of things network, an optimization condition aiming at the maximum throughput is established, a throughput iteration model is established according to the optimization condition, throughput data corresponding to the adjacent iteration times output by the throughput iteration model is obtained by combining signal transmission data of the internet of things network, resource allocation parameters corresponding to the target iteration times are obtained, and as a resource allocation strategy of the internet of things network to be allocated, the throughput maximization of the internet of things network is realized by combining the mode of optimizing the transmission energy and the time allocation of information, and the phase shift of an intelligent reflecting surface and the coordinates of a mobile antenna, so that the performance of the system is improved, and the reasonable allocation of internet of things resources is further realized.
For a better understanding and implementation, the present application is described in detail below with reference to the drawings.
Drawings
Fig. 1 is a flow chart of a method for allocating resources of the internet of things according to an embodiment of the present application;
fig. 2 is a schematic flow chart of S3 in the method for allocating resources of the internet of things according to an embodiment of the present application;
fig. 3 is a schematic flow chart of S4 in the method for allocating resources of the internet of things according to an embodiment of the present application;
fig. 4 is a schematic flow chart of S5 in the method for allocating resources of the internet of things according to an embodiment of the present application;
fig. 5 is a schematic flow chart of S6 in the method for allocating resources of the internet of things according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an internet of things resource allocation device according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or 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 herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the application. The word "if"/"if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination", depending on the context.
Referring to fig. 1, fig. 1 is a flowchart of a method for allocating resources of the internet of things according to an embodiment of the present application, where the method includes the following steps:
S1: and obtaining signal transmission data of the Internet of things network to be distributed.
The execution subject of the internet of things resource allocation method is allocation equipment (hereinafter referred to as allocation equipment) of the internet of things resource allocation method. The allocation device can be realized by software and/or hardware, and the resource allocation method of the internet of things can be realized by software and/or hardware, and the allocation device can be formed by two or more physical entities or one physical entity. The hardware to which the distribution device is directed is essentially a computer device, for example, the distribution device may be a computer, a mobile phone, a tablet, or an interactive tablet. In an alternative 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 signal transmission data of an internet of things network to be distributed, where the internet of things network includes a hybrid access point, an intelligent reflection surface, and a plurality of internet of things devices. All the internet of things devices are provided with a single mobile antenna, each internet of things device firstly collects energy radiated by the hybrid access point, then transmits the collected information to the hybrid access point by utilizing the collected energy, and meanwhile, the intelligent reflecting surface enhances the energy collection and data transmission capacity of the system through reflection of the energy and the information.
The signal transmission data comprises a channel matrix of the Internet of things network, transmission power of the hybrid access point, antenna channel paths and channel data of each Internet of things device, wherein the channel matrix comprises 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 an intelligent reflecting surface, and the channel data comprises elevation angles and horizontal angles of the mobile antenna channel paths and a plurality of channel paths.
S2: and constructing an optimization condition with the maximum throughput as a target, and constructing a throughput iteration model according to the optimization condition.
By time parametersPhase shift matrix->Andkcoordinate parameter of personal Internet of things equipment>In this embodiment, the allocation device constructs an optimization condition with the maximum throughput as a target, and constructs a throughput iteration model according to the optimization condition, where 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.
Specifically, the optimization condition that the throughput is at most the target is:
in the method, in the process of the invention, For the time parameter->Is a column of vectors and has no subscript indication, and its elements include energy transfer time parameter and of hybrid access pointKThe information transfer time parameter of the internet of things equipment specifically shows as follows:wherein, superscriptTFor transposed symbol +.>For the energy transfer time parameter of the hybrid access point,/for the hybrid access point>Is the firstkInformation transfer time parameter of personal internet of things equipment, < ->Is the firstKInformation transfer time parameter of personal internet of things equipment, < ->For a phase shift matrix of the intelligent reflective surface,
constructing a function for the matrix->Is the intelligent reflecting surfaceNAmplitude reflection coefficient of individual reflection elements, +.>Is the intelligent reflecting surfaceNPhase shift offset coefficients of the individual reflective elements.
rIs a coordinate parameter, is a column vector and has no subscript indication, and the elements compriseKThe specific representation of the coordinate parameters of the internet of things equipment is as follows:，/>is->Transpose of->Is the firstkThe coordinate parameters of the equipment of the Internet of things are a list of vectors, and the elements of the coordinate parameters comprise the firstkThe abscissa parameters and ordinate parameters of the internet of things equipment specifically represent the following: />，/>Is the firstkAbscissa parameter in coordinate parameters of personal internet of things equipment,/->Is the firstkOrdinate parameter in the coordinate parameters of the individual internet of things device,/- >Is the firstkThe energy efficiency parameters of the individual internet of things devices,is the firstkA mobile antenna field response vector for the individual internet of things device,Pfor the transmission power of the hybrid access point, +.>Antenna field response vector for hybrid access point, < >>To mix access points to the firstkTranspose of the channel response matrix of the individual internet of things device,Tfor transposed symbol +.>For the channel response matrix of the hybrid access point to the smart reflecting surface +.>And +.>Modeling may be performed using rayleigh fading channel models of corresponding dimensions.
Is noise power +.>Is the intelligent reflecting surfacenPhase shift offset coefficient of the individual reflection elements, +.>Reflection phase constraint representing all reflection elements of the intelligent reflection plane reflection matrix, +.>An overall constraint representing the transmission time,/->Is the firstkThe abscissa parameters among the coordinate parameters of the individual internet of things devices,ais a preset positive integer, +.>Is the firstkOrdinate parameter in the coordinate parameters of the individual internet of things device,/->And representing the coordinate constraint of the mobile antennas of all the Internet of things devices.
In this embodiment, the distribution device establishes a plurality of sub-optimization conditions corresponding to the optimization model according to the optimization model, and uses the sub-optimization conditions as the data optimization module, where the sub-optimization conditions include a sub-optimization condition in which a phase shift vector of the intelligent reflection surface is the largest target, a sub-optimization condition in which a coordinate parameter is the largest target, and a sub-optimization condition in which a time parameter is the largest target.
For sub-optimal conditions with maximum target of phase shift matrix of intelligent reflecting surface, the distribution equipment fixes time parameters according to the optimal conditionsCoordinate parametersrSolving the phase shift matrix +.>First of all, the auxiliary line vector is introduced->，，TFor transposed symbol +.>Is->Transpose of->Is Hadamard product, and then auxiliary column vector is introducedv，/>Column vectorization of diagonal elements of the intelligent reflection matrix of the reflecting surface, and then +.>Instead ofSubstituting the optimization condition with the maximum throughput as a target, and constructing a sub-optimization condition with the maximum phase shift vector as the target of the intelligent reflecting surface corresponding to the optimization condition with the maximum throughput as the target, wherein the sub-optimization condition with the maximum phase shift vector as the target is as follows:
in the method, in the process of the invention,for the first auxiliary variable, +.>Is the firstkAuxiliary row vectors of the individual internet of things devices,，/>for the Hadamard product,vfor auxiliary column vector, ++>，/>Is the intelligent reflecting surfaceNPhase shift offset coefficient of the individual reflection elements, +.>To relax the constraint, the->Is the first in the column vectornThe number of elements to be added to the composition,wfor a preset local point +.>Representing the complex real part->Is->Conjugation of->Is thatwIs a conjugate transpose of (a).
For sub-optimal conditions with maximum coordinate parameters as targets, the distribution equipment fixes time parameters according to the optimal conditions Phase shift matrix->Solving the coordinate parametersrFirst of all, the auxiliary variable +.>，/>Constructing a sub-optimization condition with a maximum target coordinate parameter corresponding to the optimization condition with the maximum throughput as the target, wherein the sub-optimization condition with the maximum target coordinate parameter is as follows:
in the method, in the process of the invention,and +.>Representing +.>，/>In order to relax the constraints of the device,。
for sub-optimal conditions for which the time parameter is maximally targeted, the allocation device fixes the phase shift matrix according to said optimal conditionsCoordinate parametersrSolving for the time parameter->Constructing a sub-optimization condition with a maximum target time parameter corresponding to the optimization condition with the maximum target throughput, wherein the sub-optimization condition with the maximum target time parameter is as follows:
in the method, in the process of the invention,for the second auxiliary variable, +.>。
S3: and inputting the signal transmission data into the throughput iterative model to obtain the resource configuration parameters corresponding to the current iteration times output by the data optimization module.
The resource configuration parameters are used for indicating the optimization results of the resource configuration parameters of the hybrid access point, the intelligent reflecting surface and the plurality of internet of things devices.
In this embodiment, the allocation device inputs the signal transmission data to the throughput iteration model, and obtains a resource configuration parameter corresponding to the current iteration number output by the data optimization module, where the resource configuration parameter includes a phase shift matrix of the intelligent reflection surface, and coordinate parameters and time parameters of each of the internet of things devices.
Referring to fig. 2, fig. 2 is a schematic flow chart of step S3 in the method for allocating resources of the internet of things according to an embodiment of the present application, including steps S31 to S33, specifically as follows:
s31: and obtaining a phase shift vector according to the signal transmission data and a sub-optimization condition that a preset phase shift vector is the target at maximum, and performing diagonalization calculation on the phase shift vector to obtain a phase shift matrix.
In this embodiment, the distribution device uses a successive approximation algorithm to obtain a phase shift vector according to the signal transmission data and a sub-optimization condition that a preset phase shift vector is the maximum targetFor the phase shift vector +.>Normalization processing is carried out to obtain a normalization processing result, and diagonalization calculation is carried out on the normalization processing result to obtain a phase shift matrix。
S32: and obtaining the coordinate parameters of the Internet of things equipment according to the signal transmission data and the sub-optimization condition of which the preset coordinate parameters are the maximum targets.
The genetic algorithm is suitable for solving multimodal function optimization conditions with only single real variables and only domain constraints. The genetic algorithm can effectively overcome the trouble of local optimal solution by simultaneously exploring multiple solutions in the search space by multiple individuals in the population.
In this embodiment, the dispensing device employs a genetic algorithm to transmit data and preset settings based on the signalsSub-optimization conditions with maximum target parameters are obtained, and coordinate parameters of all the Internet of things equipment are obtained。
S33: and obtaining the time parameter according to the signal transmission data and the sub-optimal condition of which the preset time parameter is the target at maximum.
In this embodiment, the distribution device adopts a lagrangian multiplier method, and obtains a time parameter according to the signal transmission data and a sub-optimization condition with a preset time parameter as a target, where the time parameter includes an energy transfer time parameter of a hybrid access point and an information transfer time parameter of an internet of things device, and specifically includes the following steps:
in the method, in the process of the invention,for the energy transfer time parameter of the hybrid access point,/for the hybrid access point>Is a third auxiliary variable, is a formulaIs (are) a solution of->And the information transmission time parameter is the information transmission time parameter of the Internet of things equipment.
S4: and obtaining an antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module according to the resource configuration parameter corresponding to the current iteration number and the signal transmission data.
In this embodiment, the allocation device obtains, according to the resource configuration parameter corresponding to the current iteration number and the signal transmission data, an antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module, where the antenna field response vector includes an antenna field response vector of the hybrid access point and a mobile antenna field response vector of each of the internet of things devices.
Referring to fig. 3, fig. 3 is a schematic flow chart of step S4 in the method for allocating resources of the internet of things according to an embodiment of the present application, including steps S41 to S42, specifically as follows:
s41: and obtaining the antenna field response vector of the hybrid access point according to the signal transmission data and a preset first antenna field response vector calculation algorithm.
The first antenna field response vector calculation algorithm is as follows:
in the method, in the process of the application,antenna field response vector for hybrid access point, < >>For the antenna channel path of the hybrid access point,a channel response matrix for a first dimension of the hybrid access point to the smart reflective surface.
In this embodiment, the allocation device obtains the antenna field response vector of the hybrid access point according to the signal transmission data and a preset first antenna field response vector calculation algorithm.
S42: and obtaining the mobile antenna field response vector of each Internet of things device according to the signal transmission data, the resource configuration parameters and a preset second antenna field response vector calculation algorithm.
The second antenna field response vector calculation algorithm is as follows:
in the method, in the process of the application,is the firstkMobile antenna field response vector corresponding to coordinate parameters of personal Internet of things equipment, < >>Is the first kThe coordinate parameters of the individual internet of things devices,Kis the total number of the devices of the Internet of things>For carrier wavelength, +.>Is the firstkThe (th) of the personal Internet of things device>Influence of the stripe channel phase, +.>Is the firstkMobile antenna channel path for personal internet of things devices.
In this embodiment, the allocation device obtains the mobile antenna field response vector of each of the devices of the internet of things according to the signal transmission data, the resource allocation parameters and a preset second antenna field response vector calculation algorithm.
S5: and obtaining energy acquisition data of each Internet of things device corresponding to the current iteration number output by the energy consumption module according to the resource configuration parameters corresponding to the current iteration number and the signal transmission data.
In this embodiment, the allocation device obtains the energy collection data of each of the devices of the internet of things corresponding to the current iteration number output by the energy consumption module according to the resource configuration parameter corresponding to the current iteration number and the signal transmission data.
Referring to fig. 4, fig. 4 is a schematic flow chart of step S5 in the method for allocating resources of the internet of things according to an embodiment of the present application, including step S51, specifically as follows:
s51: and obtaining energy acquisition data of each Internet of things device according to the signal transmission data, the resource configuration parameters and a preset energy acquisition calculation algorithm.
The energy acquisition and calculation algorithm is as follows:
in the method, in the process of the application,is the firstkEnergy collection data of individual internet of things devices, < >>A phase shift matrix for the intelligent reflecting surface.
In this embodiment, the distribution device obtains the energy collection data of each of the devices of the internet of things according to the signal transmission data, the resource configuration parameters and a preset energy collection calculation algorithm.
S6: and obtaining throughput data corresponding to the current iteration times output by the throughput computing module according to the signal transmission data, the resource configuration parameters corresponding to the current iteration times, the antenna field response vector and the energy acquisition data of each Internet of things device.
In this embodiment, the allocation device obtains throughput data corresponding to the current iteration number output by the throughput calculation module according to the signal transmission data, the resource configuration parameter corresponding to the current iteration number, the antenna field response vector, and the energy acquisition data of each of the internet of things devices.
Referring to fig. 5, fig. 5 is a schematic flow chart of step S6 in the method for allocating resources of the internet of things according to an embodiment of the present application, including step S61, which specifically includes the following steps:
s61: and obtaining throughput data of each Internet of things device according to the signal transmission data, the resource configuration parameters, the antenna field response vector, the energy acquisition data of each Internet of things device and a preset throughput calculation algorithm.
The throughput computing algorithm is as follows:
in the method, in the process of the invention,is the firstkThroughput data of individual internet of things devices +.>As a function of the time parameter,。
in this embodiment, the distribution device obtains throughput data of each of the internet of things devices according to the signal transmission data, the resource configuration parameters, the antenna field response vector, the energy collection data of each of the internet of things devices, and a preset throughput calculation algorithm.
S7: and obtaining throughput data corresponding to the last iteration number, judging whether convergence is carried out according to the current iteration number and the throughput data corresponding to the last iteration number, and if so, obtaining a resource configuration parameter corresponding to the current iteration number as a resource allocation strategy of the Internet of things network to be allocated.
In this embodiment, the allocation device obtains throughput data corresponding to the last iteration count, and according to the current iteration count, the throughput data corresponding to the last iteration count, and a preset threshold value thresholdAnd judging whether convergence is achieved. Specifically, the distribution device obtains the absolute value of the difference value of the throughput data corresponding to the current iteration times and the last iteration times, if the absolute value is smaller than the threshold value, the convergence is judged, and if the absolute value is larger than or equal to the threshold value, the non-convergence is judged.
If the iteration number is not converged, the distribution equipment acquires throughput data corresponding to the next iteration number, and the convergence judgment is repeated until the iteration number is reached, and the calculation is stopped; if the resource allocation parameters are converged, the allocation equipment obtains the resource allocation parameters corresponding to the current iteration times and takes the resource allocation parameters as the resource allocation strategy of the Internet of things network to be allocated.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an internet of things resource allocation device according to an embodiment of the present application, where the device may implement all or a part of the internet of things resource allocation device through software, hardware or a combination of both, and the device 6 includes:
the data obtaining module 61 is configured to obtain signal transmission data of an internet of things network to be allocated, where the internet of things network includes a hybrid access point, an intelligent reflection surface, and a plurality of internet of things devices;
the model construction module 62 is configured to construct an optimization condition with a throughput being the maximum target, and construct a throughput iteration model according to the optimization condition, where 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;
a resource configuration parameter calculation module 63, configured to input the signal transmission data to the throughput iteration model, and obtain a resource configuration parameter corresponding to the current iteration number output by the data optimization module, where the resource configuration parameter is used to indicate an optimization result of the resource configuration parameters of the hybrid access point, the intelligent reflection surface, and the plurality of internet of things devices;
The antenna field response vector calculation module 64 is configured to obtain an antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module according to the resource configuration parameter corresponding to the current iteration number and signal transmission data, where the antenna field response vector includes an antenna field response vector of the hybrid access point and a mobile antenna field response vector of each of the internet of things devices;
the energy collection and calculation module 65 is configured to obtain energy collection data of each of the devices of the internet of things corresponding to the current iteration number output by the energy consumption module according to the resource configuration parameter corresponding to the current iteration number and the signal transmission data;
the throughput computing module 66 is configured to obtain throughput data corresponding to the current iteration number output by the throughput computing module according to the signal transmission data, the resource configuration parameter corresponding to the current iteration number, the antenna field response vector, and the energy acquisition data of each of the internet of things devices;
the resource allocation policy output module 67 is configured to obtain throughput data corresponding to a previous iteration number, determine whether to converge according to the current iteration number and the throughput data corresponding to the previous iteration number, and if so, obtain a resource allocation parameter corresponding to the current iteration number as a resource allocation policy of the internet of things network to be allocated.
In this embodiment, signal transmission data of an internet of things network to be allocated is obtained through a data obtaining module, where the internet of things network includes a hybrid access point, an intelligent reflection surface and a plurality of internet of things devices; constructing an optimization condition with the maximum throughput as a target through a model construction module, and constructing a throughput iteration model according to the optimization condition, wherein the throughput iteration model comprises a data optimization module, an antenna field response vector calculation module, an energy consumption module and a throughput calculation module; inputting the signal transmission data to the throughput iterative model through a resource configuration parameter calculation module, and obtaining a resource configuration parameter corresponding to the current iteration number output by the data optimization module, wherein the resource configuration parameter is used for indicating the optimization results of the resource configuration parameters of the hybrid access point, the intelligent reflecting surface and the plurality of Internet of things devices; obtaining an antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module according to the resource configuration parameter corresponding to the current iteration number and signal transmission data, wherein the antenna field response vector comprises an antenna field response vector of the hybrid access point and a mobile antenna field response vector of each Internet of things device; the energy acquisition data of each Internet of things device corresponding to the current iteration number output by the energy consumption module is obtained through an energy acquisition calculation module according to the resource configuration parameters corresponding to the current iteration number and the signal transmission data; the throughput computing module is used for obtaining throughput data corresponding to the current iteration times output by the throughput computing module according to the signal transmission data, the resource configuration parameters corresponding to the current iteration times, the antenna field response vector and the energy acquisition data of each Internet of things device; and obtaining throughput data corresponding to the last iteration number through a resource allocation strategy output module, judging whether the current iteration number and the throughput data corresponding to the last iteration number are converged or not according to the current iteration number and the throughput data corresponding to the last iteration number, and if so, obtaining a resource allocation parameter corresponding to the current iteration number as a resource allocation strategy of the Internet of things network to be allocated. Aiming at the Internet of things network, an optimization condition with the maximum throughput as a target is constructed, a throughput iteration model is constructed according to the optimization condition, throughput data corresponding to adjacent iteration times output by the throughput iteration model is obtained by combining signal transmission data of the Internet of things network, so that resource configuration parameters corresponding to the target iteration times are obtained, and as a resource allocation strategy of the Internet of things network to be allocated, the throughput maximization of the Internet of things network is realized by combining optimization of time allocation of transmission energy and information, phase shift of an intelligent reflecting surface and coordinates of a mobile antenna, the performance of the system is improved, and reasonable allocation of Internet of things resources is further realized.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present application, where 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 may store a plurality of instructions adapted to be loaded by the processor 71 and to execute the steps of the method according to the embodiments shown in fig. 1 to 6, and the specific execution process may be referred to in the specific description of fig. 1 to 6, which is not repeated here.
Wherein processor 71 may include one or more processing cores. The processor 71 performs various functions of the internet of things resource allocation device 6 and processes data by executing or executing instructions, programs, code sets or instruction sets stored in the memory 72 and invoking data in the memory 72 using various interfaces and various parts within the wired connection server, alternatively the processor 71 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field-programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programble Logic Array, PLA). The processor 71 may integrate one or a combination of several of a central processing unit 71 (Central Processing Unit, CPU), an image processor 71 (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the touch display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 71 and may be implemented by a single 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 a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 72 may be used to store instructions, programs, code sets, or instruction sets. The memory 72 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the various method embodiments described above, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 72 may optionally be at least one memory device located remotely from the aforementioned processor 71.
The embodiment of the present application further provides a storage medium, where the storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executed by the processor, and the specific execution process may refer to the specific description of fig. 1 to 6, and details are not repeated herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc.
The present invention is not limited to the above-described embodiments, but, if various modifications or variations of the present invention are not departing from the spirit and scope of the present invention, the present invention is intended to include such modifications and variations as fall within the scope of the claims and the equivalents thereof.
Claims (10)
1. The resource allocation method of the Internet of things is characterized by comprising the following steps of:
acquiring signal transmission data of an Internet of things network to be distributed, wherein the Internet of things network comprises a hybrid access point, an intelligent reflecting surface and a plurality of Internet of things devices;
constructing an optimization condition with the maximum throughput as a target, and constructing a throughput iteration model according to the optimization condition, wherein the throughput iteration model comprises a data optimization module, an antenna field response vector calculation module, an energy consumption module and a throughput calculation module;
inputting the signal transmission data into the throughput iterative model to obtain a resource configuration parameter corresponding to the current iteration number output by the data optimization module; the resource configuration parameters are used for indicating the optimization results of the resource configuration parameters of the hybrid access point, the intelligent reflecting surface and the plurality of internet of things devices;
According to the resource configuration parameters corresponding to the current iteration times and the signal transmission data, obtaining an antenna field response vector corresponding to the current iteration times output by the antenna field response vector calculation module, wherein the antenna field response vector comprises an antenna field response vector of the hybrid access point and a mobile antenna field response vector of each Internet of things device;
according to the resource configuration parameters corresponding to the current iteration times and the signal transmission data, obtaining energy acquisition data of each Internet of things device corresponding to the current iteration times output by the energy consumption module;
obtaining throughput data corresponding to the current iteration times output by the throughput calculation module according to the signal transmission data, the resource configuration parameters corresponding to the current iteration times, the antenna field response vector and the energy acquisition data of each Internet of things device;
and obtaining throughput data corresponding to the last iteration number, judging whether convergence is carried out according to the current iteration number and the throughput data corresponding to the last iteration number, and if so, obtaining a resource configuration parameter corresponding to the current iteration number as a resource allocation strategy of the Internet of things network to be allocated.
2. The method for allocating resources of the internet of things according to claim 1, wherein the optimization condition that the throughput is the maximum target is:
in the method, in the process of the invention,for the time parameter->Is a column of vectors and has no subscript indication, and its elements include energy transfer time parameter and of hybrid access pointKThe information transfer time parameter of the internet of things equipment specifically shows as follows:wherein, superscriptTFor transposed symbol +.>For the energy transfer time parameter of the hybrid access point,/for the hybrid access point>Is the firstkInformation transfer time parameter of personal internet of things equipment, < ->Is the firstKInformation transfer time parameter of personal internet of things equipment, < ->For a phase shift matrix of the intelligent reflective surface,ris a coordinate parameter, is a column vector and has no subscript indication, and the elements compriseKThe specific representation of the coordinate parameters of the internet of things equipment is as follows: />，/>Is the firstkThe coordinate parameters of the equipment of the Internet of things are a list of vectors, and the elements of the coordinate parameters comprise the firstkThe abscissa parameters and ordinate parameters of the internet of things equipment specifically represent the following: />，/>Is the firstkAbscissa parameter in coordinate parameters of personal internet of things equipment,/->Is the firstkOrdinate parameter in the coordinate parameters of the individual internet of things device,/- >Is the firstkEnergy efficiency parameters of individual internet of things devices, < ->Is the firstkA mobile antenna field response vector for the individual internet of things device,Pfor the transmission power of the hybrid access point, +.>Antenna field response vector for hybrid access point, < >>To mix access points to the firstkTranspose of the channel response matrix of the individual internet of things device,Tfor transposed symbol +.>Channel response moment for the hybrid access point to smart reflective surfaceArray (S)>In order for the noise power to be high,is the intelligent reflecting surfacenPhase shift offset coefficient of the individual reflection elements, +.>Reflection phase constraint representing all reflection elements of the intelligent reflection plane reflection matrix, +.>Representing the overall constraint of the transmission time,ais a preset positive integer, +.>And representing the coordinate constraint of the mobile antennas of all the Internet of things devices.
3. The internet of things resource allocation method according to claim 2, wherein: the signal transmission data comprises a channel matrix of the Internet of things network, transmission power of the hybrid access point, antenna channel paths and channel data of each Internet of things device, wherein the channel matrix comprises 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 an intelligent reflecting surface, and the channel data comprises elevation angles and horizontal angles of the mobile antenna channel paths and a plurality of channel paths.
4. The internet of things resource allocation method according to claim 3, wherein: the resource configuration parameters comprise phase shift matrixes of the intelligent reflecting surfaces, and coordinate parameters and time parameters of the internet of things equipment;
inputting the signal transmission data to the throughput iterative model to obtain a resource configuration parameter corresponding to the current iteration number output by the data optimization module, wherein the method comprises the following steps:
according to the signal transmission data and a preset sub-optimization condition that the maximum phase shift vector is a target, a phase shift vector is obtained, diagonalization calculation is carried out on the phase shift vector, and a phase shift matrix is obtained, wherein the sub-optimization condition that the maximum phase shift vector is the target is as follows:
in the method, in the process of the invention,for the first auxiliary variable, +.>Is the firstkAuxiliary row vectors of the individual internet of things devices,，Tfor transposed symbol +.>Is->Transpose of->For the Hadamard product,vfor auxiliary column vector, ++>，/>Is the intelligent reflecting surfaceNPhase shift offset coefficient of the individual reflection elements, +.>To relax the constraint, the->Is the first in the column vectornThe number of elements to be added to the composition,wfor a preset local point +.>Representing the complex real part->Is->Conjugation of->Is thatwIs a conjugate transpose of (2);
obtaining the coordinate parameters of each Internet of things device according to the signal transmission data and the preset sub-optimization condition with the maximum coordinate parameters as targets, wherein the sub-optimization condition with the maximum coordinate parameters as targets is as follows:
In the method, in the process of the invention,and +.>Representing +.>，/>In order to relax the constraints of the device,；
obtaining a time parameter according to the signal transmission data and a sub-optimization condition with a preset time parameter as a target at maximum, wherein the time parameter comprises an energy transfer time parameter of a hybrid access point and an information transfer time parameter of Internet of things equipment, and the sub-optimization condition with the maximum time parameter as the target is as follows:
in the method, in the process of the invention,for the second auxiliary variable, +.>；
The time parameters are as follows:
in the method, in the process of the invention,for the energy transfer time parameter of the hybrid access point,/for the hybrid access point>Is a third auxiliary variable, is a formulaIs (are) a solution of->And the information transmission time parameter is the information transmission time parameter of the Internet of things equipment.
5. The method for allocating resources of the internet of things according to claim 4, wherein the obtaining 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 parameter corresponding to the current iteration number and the signal transmission data includes the steps of:
according to the signal transmission data and a preset first antenna field response vector calculation algorithm, an antenna field response vector of the hybrid access point is obtained, wherein the first antenna field response vector calculation algorithm is as follows:
In the method, in the process of the invention,antenna field response vector for hybrid access point, < >>Antenna channel path for hybrid access point, +.>A channel response matrix of a first dimension from the hybrid access point to the intelligent reflecting surface;
obtaining a mobile antenna field response vector of each internet of things device according to the signal transmission data, the resource configuration parameters and a preset second antenna field response vector calculation algorithm, wherein the second antenna field response vector calculation algorithm is as follows:
in the method, in the process of the invention,is the firstkMobile antenna field response vector corresponding to coordinate parameters of personal Internet of things equipment, < >>Is the firstkCoordinate parameters of individual internet of things devices，KIs the total number of the devices of the Internet of things>For carrier wavelength, +.>Is the firstkThe (th) of the personal Internet of things device>Influence of the stripe channel phase, +.>Is the firstkMobile antenna channel path for personal internet of things devices.
6. The method for allocating resources of the internet of things according to claim 5, wherein the obtaining the energy collection data of each of the devices of the internet of things corresponding to the current iteration number output by the energy consumption module according to the resource configuration parameter corresponding to the current iteration number and the signal transmission data includes the steps of:
according to the signal transmission data, the resource configuration parameters and a preset energy collection calculation algorithm, energy collection data of each Internet of things device are obtained, wherein the energy collection calculation algorithm is as follows:
In the method, in the process of the invention,is the firstkEnergy collection data of individual internet of things devices, < >>A phase shift matrix for the intelligent reflecting surface.
7. The method for allocating resources of the internet of things according to claim 6, wherein the obtaining throughput data corresponding to the current iteration number output by the throughput calculation module according to the signal transmission data, the resource configuration parameter corresponding to the current iteration number, the antenna field response vector, and the energy collection data of each of the internet of things devices includes the steps of:
according to the signal transmission data, the resource configuration parameters, the antenna field response vector, the energy acquisition data of each Internet of things device and a preset throughput computing algorithm, throughput data of each Internet of things device are obtained, wherein the throughput computing algorithm is as follows:
in the method, in the process of the invention,is the firstkThroughput data of individual internet of things devices +.>As a function of the time parameter,。
8. the utility model provides an thing networking resource allocation device which characterized in that includes:
the system comprises a data acquisition module, a data distribution module and a data distribution module, wherein the data acquisition module is used for acquiring signal transmission data of an Internet of things network to be distributed, and the Internet of things network comprises a hybrid access point, an intelligent reflecting surface and a plurality of Internet of things devices;
The model construction module is used for constructing an optimization condition with the maximum throughput as a target, and constructing a throughput iteration model according to the optimization condition, wherein the throughput iteration model comprises a data optimization module, an antenna field response vector calculation module, an energy consumption module and a throughput calculation module;
the resource configuration parameter calculation module is used for inputting the signal transmission data into the throughput iteration model to obtain a resource configuration parameter corresponding to the current iteration number output by the data optimization module, wherein the resource configuration parameter is used for indicating the optimization results of the resource configuration parameters of the hybrid access point, the intelligent reflecting surface and the plurality of internet of things devices;
the antenna field response vector calculation module is used for obtaining an antenna field response vector corresponding to the current iteration number output by the antenna field response vector calculation module according to the resource configuration parameter corresponding to the current iteration number and signal transmission data, wherein the antenna field response vector comprises an antenna field response vector of the hybrid access point and a mobile antenna field response vector of each Internet of things device;
the energy acquisition calculation module is used for acquiring energy acquisition data of each Internet of things device corresponding to the current iteration number output by the energy consumption module according to the resource configuration parameters corresponding to the current iteration number and the signal transmission data;
The throughput computing module is used for obtaining throughput data corresponding to the current iteration times output by the throughput computing module according to the signal transmission data, the resource configuration parameters corresponding to the current iteration times, the antenna field response vector and the energy acquisition data of each Internet of things device;
and the resource allocation strategy output module is used for obtaining throughput data corresponding to the last iteration number, judging whether the current iteration number and the throughput data corresponding to the last iteration number are converged or not according to the current iteration number and the throughput data corresponding to the last iteration number, and if so, obtaining a resource allocation parameter corresponding to the current iteration number as a resource allocation strategy of the Internet of things network to be allocated.
9. A computer device, comprising: a processor, a memory, and a computer program stored on the memory and executable on the processor; the computer program, when executed by the processor, implements the steps of the internet of things resource allocation method according to any one of claims 1 to 7.
10. A storage medium, characterized by: the storage medium stores a computer program which, when executed by a processor, implements the steps of the internet of things resource allocation method according to any one of claims 1 to 7.
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