CN116867090A - An Internet of Things resource allocation method, device, equipment and storage medium - Google Patents
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Abstract
本发明涉及无线通信技术领域,涉及一种物联网资源分配方法,针对物联网网络,构建以吞吐量最大为目标的优化条件,根据所述优化条件构建吞吐量迭代模型,结合物联网网络的信号传输数据,获得吞吐量迭代模型输出的相邻迭代次数对应的吞吐量数据,以获得目标迭代次数对应的资源配置参数,作为所述待分配的物联网网络的资源分配策略,通过联合优化传输能量和信息的时间分配,智能反射面的相移和移动天线的坐标的方式,实现了物联网网络的吞吐量最大化,提高系统的性能,进一步实现了物联网资源的合理分配。
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
技术领域Technical field
本发明涉及无线通信技术领域,特别涉及一种物联网资源分配方法、装置、设备以及存储介质。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.
背景技术Background technique
物联网的迅速发展带来接入物联网设备的激增,而通过射频电路为物联网设备远程提供能量被视为一种实现物联网可持续发展的解决方案。具体地,先在下行能量传输链路,借助混合接入点为广播范围内的物联网设备广播能量信号;然后再上行信息传输链路,物联网设备借助收集到的能量信号作为能量来源向混合接入点传输信息。而通过将智能反射面整合到物联网无线供能信息传输网络中,并在每个物联网设备端配备移动天线,可以有效缓解传输能量过程中的能量衰减和提高传输信息过程中的接收端信噪比,从而提高系统的和吞吐量,然而,如何联合设计接入物联网网络中的智能反射面的反射相位、物联网设备处的移动天线坐标和能量信息传输时间来实现物联网网络的吞吐量最大化尚未得到有效研究。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.
发明内容Contents of the invention
基于此,本发明的目的在于,提供一种物联网资源分配方法、装置、设备以及存储介质,针对物联网网络,构建以吞吐量最大为目标的优化条件,根据所述优化条件构建吞吐量迭代模型,结合物联网网络的信号传输数据,获得吞吐量迭代模型输出的相邻迭代次数对应的吞吐量数据,以获得目标迭代次数对应的资源配置参数,作为所述待分配的物联网网络的资源分配策略,通过联合优化传输能量和信息的时间分配,智能反射面的相移和移动天线的坐标的方式,实现了物联网网络的吞吐量最大化,提高系统的性能,进一步实现了物联网资源的合理分配。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.
第一方面,本申请实施例提供了一种物联网资源分配方法,包括以下步骤:In the first aspect, embodiments of this application provide an Internet of Things resource allocation method, including the following steps:
获得待分配的物联网网络的信号传输数据,其中,所述物联网网络包括混合接入点、智能反射面以及若干个物联网设备;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;
构建吞吐量最大为目标的优化条件,根据所述优化条件构建吞吐量迭代模型,其中,所述吞吐量迭代模型包括数据优化模块、天线场响应向量计算模块、能耗模块以及吞吐量计算模块;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;
将所述信号传输数据输入至所述吞吐量迭代模型,获得所述数据优化模块输出的当前迭代次数对应的资源配置参数,其中,所述资源配置参数用于指示所述混合接入点、智能反射面以及若干个物联网设备的资源配置参数的优化结果;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;
根据所述当前迭代次数对应的资源配置参数以及信号传输数据,获得所述天线场响应向量计算模块输出的当前迭代次数对应的天线场响应向量,其中,所述天线场响应向量包括所述混合接入点的天线场响应向量以及各个所述物联网设备的移动天线场响应向量;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;
根据所述当前迭代次数对应的资源配置参数以及信号传输数据,获得所述能耗模块输出的当前迭代次数对应的各个所述物联网设备的能量采集数据;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;
根据所述信号传输数据、当前迭代次数对应的资源配置参数、天线场响应向量以及各个所述物联网设备的能量采集数据,获得所述吞吐量计算模块输出的当前迭代次数对应的吞吐量数据;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;
获得上一次迭代次数对应的吞吐量数据,根据所述当前迭代次数以及上一次迭代次数对应的吞吐量数据,判断是否收敛,若收敛,获得当前迭代次数对应的资源配置参数,作为所述待分配的物联网网络的资源分配策略。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.
第二方面,本申请实施例提供了一种物联网资源分配装置,包括:In the second aspect, embodiments of the present application provide an Internet of Things resource allocation device, including:
数据获得模块,用于获得待分配的物联网网络的信号传输数据,其中,所述物联网网络包括混合接入点、智能反射面以及若干个物联网设备;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;
模型构建模块,用于构建吞吐量最大为目标的优化条件,根据所述优化条件构建吞吐量迭代模型,其中,所述吞吐量迭代模型包括数据优化模块、天线场响应向量计算模块、能耗模块以及吞吐量计算模块;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;
资源配置参数计算模块,用于将所述信号传输数据输入至所述吞吐量迭代模型,获得所述数据优化模块输出的当前迭代次数对应的资源配置参数,其中,所述资源配置参数用于指示所述混合接入点、智能反射面以及若干个物联网设备的资源配置参数的优化结果;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;
天线场响应向量计算模块,用于根据所述当前迭代次数对应的资源配置参数以及信号传输数据,获得所述天线场响应向量计算模块输出的当前迭代次数对应的天线场响应向量,其中,所述天线场响应向量包括所述混合接入点的天线场响应向量以及各个所述物联网设备的移动天线场响应向量;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;
能量采集计算模块,用于根据所述当前迭代次数对应的资源配置参数以及信号传输数据,获得所述能耗模块输出的当前迭代次数对应的各个所述物联网设备的能量采集数据;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;
吞吐量计算模块,用于根据所述信号传输数据、当前迭代次数对应的资源配置参数、天线场响应向量以及各个所述物联网设备的能量采集数据,获得所述吞吐量计算模块输出的当前迭代次数对应的吞吐量数据;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;
资源分配策略输出模块,用于获得上一次迭代次数对应的吞吐量数据,根据所述当前迭代次数以及上一次迭代次数对应的吞吐量数据,判断是否收敛,若收敛,获得当前迭代次数对应的资源配置参数,作为所述待分配的物联网网络的资源分配策略。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.
第三方面,本申请实施例提供了一种计算机设备,包括:处理器、存储器以及存储在所述存储器上并可在所述处理器上运行的计算机程序;所述计算机程序被所述处理器执行时实现如第一方面所述物联网资源分配方法的步骤。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.
第四方面,本申请实施例提供了一种存储介质,所述存储介质存储有计算机程序,所述计算机程序被处理器执行时实现如第一方面所述的物联网资源分配方法的步骤。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.
在本申请实施例中,提供一种物联网资源分配方法、装置、设备以及存储介质,针对物联网网络,构建以吞吐量最大为目标的优化条件,根据所述优化条件构建吞吐量迭代模型,结合物联网网络的信号传输数据,获得吞吐量迭代模型输出的相邻迭代次数对应的吞吐量数据,以获得目标迭代次数对应的资源配置参数,作为所述待分配的物联网网络的资源分配策略,通过联合优化传输能量和信息的时间分配,智能反射面的相移和移动天线的坐标的方式,实现了物联网网络的吞吐量最大化,提高系统的性能,进一步实现了物联网资源的合理分配。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.
为了更好地理解和实施,下面结合附图详细说明本发明。For better understanding and implementation, the present invention will be described in detail below with reference to the accompanying drawings.
附图说明Description of the drawings
图1为本申请一个实施例提供的物联网资源分配方法的流程示意图;Figure 1 is a schematic flow chart of an Internet of Things resource allocation method provided by an embodiment of the present application;
图2为本申请一个实施例提供的物联网资源分配方法中S3的流程示意图;Figure 2 is a schematic flowchart of S3 in the Internet of Things resource allocation method provided by an embodiment of the present application;
图3为本申请一个实施例提供的物联网资源分配方法中S4的流程示意图;Figure 3 is a schematic flowchart of S4 in the Internet of Things resource allocation method provided by an embodiment of the present application;
图4为本申请一个实施例提供的物联网资源分配方法中S5的流程示意图;Figure 4 is a schematic flowchart of S5 in the Internet of Things resource allocation method provided by an embodiment of the present application;
图5为本申请一个实施例提供的物联网资源分配方法中S6的流程示意图;Figure 5 is a schematic flowchart of S6 in the Internet of Things resource allocation method provided by an embodiment of the present application;
图6为本申请一个实施例提供的物联网资源分配装置的结构示意图;Figure 6 is a schematic structural diagram of an Internet of Things resource allocation device provided by an embodiment of the present application;
图7为本申请一个实施例提供的计算机设备的结构示意图。Figure 7 is a schematic structural diagram of a computer device provided by an embodiment of the present application.
具体实施方式Detailed ways
这里将详细地对示例性实施例进行说明,其示例表示在附图中。下面的描述涉及附图时,除非另有表示,不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本申请相一致的所有实施方式。相反,它们仅是与如所附权利要求书中所详述的、本申请的一些方面相一致的装置和方法的例子。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.
在本申请使用的术语是仅仅出于描述特定实施例的目的,而非旨在限制本申请。在本申请和所附权利要求书中所使用的单数形式的“一种”、“所述”和“该”也旨在包括多数形式,除非上下文清楚地表示其他含义。还应当理解,本文中使用的术语“和/或”是指并包含一个或多个相关联的列出项目的任何或所有可能组合。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.
应当理解,尽管在本申请可能采用术语第一、第二、第三等来描述各种信息,但这些信息不应限于这些术语。这些术语仅用来将同一类型的信息彼此区分开。例如,在不脱离本申请范围的情况下,第一信息也可以被称为第二信息,类似地,第二信息也可以被称为第一信息。取决于语境,如在此所使用的词语“如果”/“若”可以被解释成为“在……时”或“当……时”或“响应于确定”。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."
请参阅图1,图1为本申请一个实施例提供的物联网资源分配方法的流程示意图,方法包括如下步骤: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:获得待分配的物联网网络的信号传输数据。S1: Obtain the signal transmission data of the IoT network to be allocated.
物联网资源分配方法的执行主体为物联网资源分配方法的分配设备(以下简称分配设备)。分配设备可以通过软件和/或硬件的方式实现,可以通过软件和/或硬件的方式实现物联网资源分配方法,该分配设备可以是两个或多个物理实体构成,也可以是一个物理实体构成。分配设备所指向的硬件,本质上均是指计算机设备,例如,分配设备可以是电脑、手机、平板或交互平板等设备。在一个可选的实施例中,分配设备具体可以是服务器,或是多台计算机设备联合而成的服务器机群。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.
在本实施例中,分配设备获得待分配的物联网网络的信号传输数据,其中,所述物联网网络包括混合接入点、智能反射面以及若干个物联网设备。所有物联网设备都配置单个移动天线,各个物联网设备首先收集混合接入点辐射的能量,然后通过利用收集的能量将其收集到的信息传递到混合接入点,与此同时智能反射面通过对能量和信息的反射来增强系统的能量收集和数据传输能力。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.
所述信号传输数据包括所述物联网网络的信道矩阵、所述混合接入点的传输功率、天线信道路径以及各个所述物联网设备的信道数据,其中,所述信道矩阵包括所述混合接入点到各个物联网设备的信道响应矩阵以及所述混合接入点到智能反射面的信道响应矩阵,所述信道数据包括移动天线信道路径以及若干条信道路径的仰角以及水平角。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:构建吞吐量最大为目标的优化条件,根据所述优化条件构建吞吐量迭代模型。S2: Construct optimization conditions with the maximum throughput as the goal, and construct a throughput iteration model based on the optimization conditions.
通过对时间参数,相移矩阵/>和k个物联网设备的坐标参数/>的联合优化达到和吞吐量最大,在本实施例中,分配设备构建吞吐量最大为目标的优化条件,根据所述优化条件构建吞吐量迭代模型,其中,所述吞吐量迭代模型包括数据优化模块、天线场响应向量计算模块、能耗模块以及吞吐量计算模块。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.
具体地,所述吞吐量最大为目标的优化条件为:Specifically, the optimization condition for maximizing the throughput is:
式中,为时间参数,/>为一列向量且无下标指示,其元素包括混合接入点的能量传递时间参数以及K个物联网设备的信息传递时间参数,其具体表示如后所示:,其中,上标T为转置符号,/>为所述混合接入点的能量传递时间参数,/>为第k个物联网设备的信息传递时间参数,/>为第K个物联网设备的信息传递时间参数,/>为智能反射面的相移矩阵,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,
为矩阵构建函数,/>为智能反射面第N个反射元素的振幅反射系数,/>为智能反射面第N个反射元素的相移偏移系数。 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为坐标参数,为一列向量且无下标指示,其元素包括K个物联网设备的的坐标参数,其具体表示如后所示:,/>为/>的转置,/>为第k个物联网设备的坐标参数,为一列向量,其元素包括第k个物联网设备的横坐标参数以及纵坐标参数,其具体表示入后所示:/>,/>为第k个物联网设备的坐标参数中的横坐标参数,/>为第k个物联网设备的坐标参数中的纵坐标参数,/>为第k个物联网设备的能量效率参数,为第k个物联网设备的移动天线场响应向量,P为混合接入点的传输功率,/>为混合接入点的天线场响应向量,/>为混合接入点到第k个物联网设备的信道响应矩阵的转置,T为转置符号,/>为所述混合接入点到智能反射面的信道响应矩阵,/>以及/>可以使用对应维数的瑞利衰落信道模型进行建模。 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.
为噪声功率,/>为智能反射面第n个反射元素的相移偏移系数,/>表示智能反射面反射矩阵所有反射元素的反射相位约束,/>表示传输时间的整体约束,/>为第k个物联网设备的坐标参数中的横坐标参数,a为预设的正整数,/>为第k个物联网设备的坐标参数中的纵坐标参数,/>表示所有物联网设备的移动天线的坐标约束。 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.
在本实施例中,分配设备根据所述优化模型建立所述优化模型对应的若干个子优化条件,作为所述数据优化模块,其中,所述子优化条件包括智能反射面的相移向量最大为目标的子优化条件、坐标参数最大为目标的子优化条件以及时间参数最大为目标的子优化条件。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.
对于智能反射面的相移矩阵最大为目标的子优化条件,分配设备根据所述优化条件,固定时间参数,坐标参数r求解相移矩阵/>,首先引入辅助行向量/>,,T为转置符号,/>为/>的转置,/>为哈达玛积,然后引入辅助列向量v,/>,将智能反射面反射矩阵对角元素的列向量化,再用/>代替代入所述吞吐量最大为目标的优化条件,构建所述吞吐量最大为目标的优化条件对应的智能反射面的相移向量最大为目标的子优化条件,其中,所述相移向量最大为目标的子优化条件为: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:
式中,为第一辅助变量,/>为第k个物联网设备的辅助行向量,,/>为哈达玛积,v为辅助列向量,/>,/>为智能反射面第N个反射元素的相移偏移系数,/>为放松约束,/>为列向量中的第n个元素,w为预设的局部点,/>表示取复数实部,/>为/>的共轭,/>为w的共轭转置。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 .
对于坐标参数最大为目标的子优化条件,分配设备根据所述优化条件,固定时间参数以及相移矩阵/>,求解坐标参数r,首先引入辅助行变量/>,/>,构建所述吞吐量最大为目标的优化条件对应的坐标参数最大为目标的子优化条件,其中,所述坐标参数最大为目标的子优化条件为: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:
式中,以及/>表示引入辅助向量的/>,/>为放松约束,。In the formula, and/> Indicates the introduction of auxiliary vectors/> ,/> To loosen constraints, .
对于时间参数最大为目标的子优化条件,分配设备根据所述优化条件,固定相移矩阵,坐标参数r求解时间参数/>,构建所述吞吐量最大为目标的优化条件对应的时间参数最大为目标的子优化条件,其中,所述时间参数最大为目标的子优化条件为: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:
式中,为第二辅助变量,/>。In the formula, is the second auxiliary variable,/> .
S3:将所述信号传输数据输入至所述吞吐量迭代模型,获得所述数据优化模块输出的当前迭代次数对应的资源配置参数。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.
所述资源配置参数用于指示所述混合接入点、智能反射面以及若干个物联网设备的资源配置参数的优化结果。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.
在本实施例中,分配设备将所述信号传输数据输入至所述吞吐量迭代模型,获得所述数据优化模块输出的当前迭代次数对应的资源配置参数,其中,所述资源配置参数包括所述智能反射面的相移矩阵,各个所述物联网设备的坐标参数以及时间参数。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.
请参阅图2,图2为本申请一个实施例提供的物联网资源分配方法中S3的流程示意图,包括步骤S31~S33,具体如下: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:根据所述信号传输数据以及预设的相移向量最大为目标的子优化条件,获得相移向量,对所述相移向量进行对角化计算,获得相移矩阵。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.
在本实施例中,分配设备采用逐次凸逼近算法,根据所述信号传输数据以及预设的相移向量最大为目标的子优化条件,获得相移向量,对所述相移向量/>进行归一化处理,获得归一化处理结果,再对该归一化处理结果进行对角化计算,获得相移矩阵。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:根据所述信号传输数据以及预设的坐标参数最大为目标的子优化条件,获得各个所述物联网设备的坐标参数。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.
遗传算法适合求解仅有单实数变量且仅有定义域约束的多峰函数优化条件。遗传算法通过种群中的多个个体在搜索空间内同时探索多个解,能够有效地克服局部最优解的困扰。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.
在本实施例中,分配设备采用遗传算法,根据所述信号传输数据以及预设的坐标参数最大为目标的子优化条件,获得各个所述物联网设备的坐标参数。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:根据所述信号传输数据以及预设的时间参数最大为目标的子优化条件,获得时间参数。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.
在本实施例中,分配设备采用拉格朗日乘子法,根据所述信号传输数据以及预设的时间参数最大为目标的子优化条件,获得时间参数,其中,所述时间参数包括混合接入点的能量传递时间参数以及物联网设备的信息传递时间参数,具体如下: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:
式中,为所述混合接入点的能量传递时间参数,/>为第三辅助变量,为公式的解,/>为所述物联网设备的信息传递时间参数。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:根据所述当前迭代次数对应的资源配置参数以及信号传输数据,获得所述天线场响应向量计算模块输出的当前迭代次数对应的天线场响应向量。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.
在本实施例中,分配设备根据所述当前迭代次数对应的资源配置参数以及信号传输数据,获得所述天线场响应向量计算模块输出的当前迭代次数对应的天线场响应向量,其中,所述天线场响应向量包括所述混合接入点的天线场响应向量以及各个所述物联网设备的移动天线场响应向量。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.
请参阅图3,图3为本申请一个实施例提供的物联网资源分配方法中S4的流程示意图,包括步骤S41~S42,具体如下: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:根据所述信号传输数据以及预设的第一天线场响应向量计算算法,获得所述混合接入点的天线场响应向量。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.
所述第一天线场响应向量计算算法为:The first antenna field response vector calculation algorithm is:
式中,为混合接入点的天线场响应向量,/>为混合接入点的天线信道路径,为混合接入点到智能反射面的第一维的信道响应矩阵。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.
在本实施例中,分配设备根据所述信号传输数据以及预设的第一天线场响应向量计算算法,获得所述混合接入点的天线场响应向量。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:根据所述信号传输数据、资源配置参数以及预设的第二天线场响应向量计算算法,获得各个所述物联网设备的移动天线场响应向量。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.
所述第二天线场响应向量计算算法为:The second antenna field response vector calculation algorithm is:
式中,为第k个物联网设备的坐标参数对应的移动天线场响应向量,/>为第k个物联网设备的坐标参数,K为物联网设备的总数,/>为载波波长,/>为第k个物联网设备的第/>条信道相位的影响,/>为第k个物联网设备的移动天线信道路径。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.
在本实施例中,分配设备根据所述信号传输数据、资源配置参数以及预设的第二天线场响应向量计算算法,获得各个所述物联网设备的移动天线场响应向量。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:根据所述当前迭代次数对应的资源配置参数以及信号传输数据,获得所述能耗模块输出的当前迭代次数对应的各个所述物联网设备的能量采集数据。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.
在本实施例中,分配设备根据所述当前迭代次数对应的资源配置参数以及信号传输数据,获得所述能耗模块输出的当前迭代次数对应的各个所述物联网设备的能量采集数据。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.
请参阅图4,图4为本申请一个实施例提供的物联网资源分配方法中S5的流程示意图,包括步骤S51,具体如下: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:根据所述信号传输数据、资源配置参数以及预设的能量采集计算算法,获得各个所述物联网设备的能量采集数据。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.
所述能量采集计算算法为:The energy collection calculation algorithm is:
式中,为第k个物联网设备的能量采集数据,/>为所述智能反射面的相移矩阵。In the formula, Collect energy data for the kth IoT device,/> is the phase shift matrix of the smart reflective surface.
在本实施例中,分配设备根据所述信号传输数据、资源配置参数以及预设的能量采集计算算法,获得各个所述物联网设备的能量采集数据。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:根据所述信号传输数据、当前迭代次数对应的资源配置参数、天线场响应向量以及各个所述物联网设备的能量采集数据,获得所述吞吐量计算模块输出的当前迭代次数对应的吞吐量数据。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.
在本实施例中,分配设备根据所述信号传输数据、当前迭代次数对应的资源配置参数、天线场响应向量以及各个所述物联网设备的能量采集数据,获得所述吞吐量计算模块输出的当前迭代次数对应的吞吐量数据。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.
请参阅图5,图5为本申请一个实施例提供的物联网资源分配方法中S6的流程示意图,包括步骤S61,具体如下: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:根据所述信号传输数据、资源配置参数、天线场响应向量、各个所述物联网设备的能量采集数据以及预设的吞吐量计算算法,获得各个所述物联网设备的吞吐量数据。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.
所述吞吐量计算算法为:The throughput calculation algorithm is:
式中,为第k个物联网设备的吞吐量数据,/>为时间参数,。In the formula, is the throughput data of the k -th IoT device,/> is the time parameter, .
在本实施例中,分配设备根据所述信号传输数据、资源配置参数、天线场响应向量、各个所述物联网设备的能量采集数据以及预设的吞吐量计算算法,获得各个所述物联网设备的吞吐量数据。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:获得上一次迭代次数对应的吞吐量数据,根据所述当前迭代次数以及上一次迭代次数对应的吞吐量数据,判断是否收敛,若收敛,获得当前迭代次数对应的资源配置参数,作为所述待分配的物联网网络的资源分配策略。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.
在本实施例中,分配设备获得上一次迭代次数对应的吞吐量数据,根据所述当前迭代次数、上一次迭代次数对应的吞吐量数据以及预设的阈值门限,判断是否收敛。具体地,分配设备获得所述当前迭代次数、上一次迭代次数对应的吞吐量数据的差值的绝对值,若所述绝对值小于所述阈值门限,判断收敛,若所述绝对值大于或等于所述阈值门限,判断不收敛。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.
若不收敛,分配设备获得下一次迭代次数对应的吞吐量数据,重复进行收敛判断,直至所述迭代次数,停止计算;若收敛,分配设备获得当前迭代次数对应的资源配置参数,作为所述待分配的物联网网络的资源分配策略。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.
请参考图6,图6为本申请一个实施例提供的物联网资源分配装置的结构示意图,该装置可以通过软件、硬件或两者的结合实现物联网资源分配装置的全部或一部分,该装置6包括: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:
数据获得模块61,用于获得待分配的物联网网络的信号传输数据,其中,所述物联网网络包括混合接入点、智能反射面以及若干个物联网设备;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;
模型构建模块62,用于构建吞吐量最大为目标的优化条件,根据所述优化条件构建吞吐量迭代模型,其中,所述吞吐量迭代模型包括数据优化模块、天线场响应向量计算模块、能耗模块以及吞吐量计算模块;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;
资源配置参数计算模块63,用于将所述信号传输数据输入至所述吞吐量迭代模型,获得所述数据优化模块输出的当前迭代次数对应的资源配置参数,其中,所述资源配置参数用于指示所述混合接入点、智能反射面以及若干个物联网设备的资源配置参数的优化结果;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;
天线场响应向量计算模块64,用于根据所述当前迭代次数对应的资源配置参数以及信号传输数据,获得所述天线场响应向量计算模块输出的当前迭代次数对应的天线场响应向量,其中,所述天线场响应向量包括所述混合接入点的天线场响应向量以及各个所述物联网设备的移动天线场响应向量;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;
能量采集计算模块65,用于根据所述当前迭代次数对应的资源配置参数以及信号传输数据,获得所述能耗模块输出的当前迭代次数对应的各个所述物联网设备的能量采集数据;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;
吞吐量计算模块66,用于根据所述信号传输数据、当前迭代次数对应的资源配置参数、天线场响应向量以及各个所述物联网设备的能量采集数据,获得所述吞吐量计算模块输出的当前迭代次数对应的吞吐量数据;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;
资源分配策略输出模块67,用于获得上一次迭代次数对应的吞吐量数据,根据所述当前迭代次数以及上一次迭代次数对应的吞吐量数据,判断是否收敛,若收敛,获得当前迭代次数对应的资源配置参数,作为所述待分配的物联网网络的资源分配策略。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.
在本实施例中,通过数据获得模块,获得待分配的物联网网络的信号传输数据,其中,所述物联网网络包括混合接入点、智能反射面以及若干个物联网设备;通过模型构建模块,构建吞吐量最大为目标的优化条件,根据所述优化条件构建吞吐量迭代模型,其中,所述吞吐量迭代模型包括数据优化模块、天线场响应向量计算模块、能耗模块以及吞吐量计算模块;通过资源配置参数计算模块,将所述信号传输数据输入至所述吞吐量迭代模型,获得所述数据优化模块输出的当前迭代次数对应的资源配置参数,其中,所述资源配置参数用于指示所述混合接入点、智能反射面以及若干个物联网设备的资源配置参数的优化结果;通过天线场响应向量计算模块,根据所述当前迭代次数对应的资源配置参数以及信号传输数据,获得所述天线场响应向量计算模块输出的当前迭代次数对应的天线场响应向量,其中,所述天线场响应向量包括所述混合接入点的天线场响应向量以及各个所述物联网设备的移动天线场响应向量;通过能量采集计算模块,根据所述当前迭代次数对应的资源配置参数以及信号传输数据,获得所述能耗模块输出的当前迭代次数对应的各个所述物联网设备的能量采集数据;通过吞吐量计算模块,根据所述信号传输数据、当前迭代次数对应的资源配置参数、天线场响应向量以及各个所述物联网设备的能量采集数据,获得所述吞吐量计算模块输出的当前迭代次数对应的吞吐量数据;通过资源分配策略输出模块,获得上一次迭代次数对应的吞吐量数据,根据所述当前迭代次数以及上一次迭代次数对应的吞吐量数据,判断是否收敛,若收敛,获得当前迭代次数对应的资源配置参数,作为所述待分配的物联网网络的资源分配策略。针对物联网网络,构建以吞吐量最大为目标的优化条件,根据所述优化条件构建吞吐量迭代模型,结合物联网网络的信号传输数据,获得吞吐量迭代模型输出的相邻迭代次数对应的吞吐量数据,以获得目标迭代次数对应的资源配置参数,作为所述待分配的物联网网络的资源分配策略,通过联合优化传输能量和信息的时间分配,智能反射面的相移和移动天线的坐标的方式,实现了物联网网络的吞吐量最大化,提高系统的性能,进一步实现了物联网资源的合理分配。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.
请参考图7,图7为本申请一个实施例提供的计算机设备的结构示意图,计算机设备7包括:处理器71、存储器72以及存储在存储器72上并可在处理器71上运行的计算机程序73;计算机设备可以存储有多条指令,指令适用于由处理器71加载并执行上述图1至图6所示实施例的方法步骤,具体执行过程可以参见图1至图6的具体说明,在此不进行赘述。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.
其中,处理器71可以包括一个或多个处理核心。处理器71利用各种接口和线路连接服务器内的各个部分,通过运行或执行存储在存储器72内的指令、程序、代码集或指令集,以及调用存储器72内的数据,执行物联网资源分配装置6的各种功能和处理数据,可选的,处理器71可以采用数字信号处理(Digital Signal Processing,DSP)、现场可编程门阵列(Field-Programmable Gate Array,FPGA)、可编程逻辑阵列(Programble Logic Array,PLA)中的至少一个硬件形式来实现。处理器71可集成中央处理器71(Central ProcessingUnit,CPU)、图像处理器71(Graphics Processing Unit,GPU)和调制解调器等中的一个或几种的组合。其中,CPU主要处理操作系统、用户界面和应用程序等;GPU用于负责触摸显示屏所需要显示的内容的渲染和绘制;调制解调器用于处理无线通信。可以理解的是,上述调制解调器也可以不集成到处理器71中,单独通过一块芯片进行实现。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.
其中,存储器72可以包括随机存储器72(Random Access Memory,RAM),也可以包括只读存储器72(Read-Only Memory)。可选的,该存储器72包括非瞬时性计算机可读介质(non-transitory computer-readable storage medium)。存储器72可用于存储指令、程序、代码、代码集或指令集。存储器72可包括存储程序区和存储数据区,其中,存储程序区可存储用于实现操作系统的指令、用于至少一个功能的指令(比如触控指令等)、用于实现上述各个方法实施例的指令等;存储数据区可存储上面各个方法实施例中涉及到的数据等。存储器72可选的还可以是至少一个位于远离前述处理器71的存储装置。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 .
本申请实施例还提供了一种存储介质,所述存储介质可以存储有多条指令,所述指令适用于由处理器加载并执行上述图1至图6所示实施例的方法步骤,具体执行过程可以参见图1至图6的具体说明,在此不进行赘述。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.
所属领域的技术人员可以清楚地了解到,为了描述的方便和简洁,仅以上述各功能单元、模块的划分进行举例说明,实际应用中,可以根据需要而将上述功能分配由不同的功能单元、模块完成,即将所述装置的内部结构划分成不同的功能单元或模块,以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以两个或两个以上单元集成在一个单元中,上述集成的单元既可以采用硬件的形式实现,也可以采用软件功能单元的形式实现。另外,各功能单元、模块的具体名称也只是为了便于相互区分,并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程,可以参考前述方法实施例中的对应过程,在此不再赘述。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.
在上述实施例中,对各个实施例的描述都各有侧重,某个实施例中没有详述或记载的部分,可以参见其它实施例的相关描述。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.
本领域普通技术人员可以意识到,结合本文中所公开的实施例描述的各示例的单元及算法步骤,能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行,取决于技术方案的特定应用和设计约束算法。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本发明的范围。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.
在本发明所提供的实施例中,应该理解到,所揭露的装置/终端设备和方法,可以通过其它的方式实现。例如,以上所描述的装置/终端设备实施例仅是示意性的,例如,所述模块或单元的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式,例如多个单元或组件可以结合或者可以集成到另一个系统,或一些特征可以忽略,或不执行。另一点,所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口,装置或单元的间接耦合或通讯连接,可以是电性,机械或其它的形式。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.
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。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.
另外,在本发明各个实施例中的各功能单元可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现,也可以采用软件功能单元的形式实现。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.
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个计算机可读取存储介质中。基于这样的理解,本发明实现上述实施例方法中的全部或部分流程,也可以通过计算机程序来指令相关的硬件来完成,所述的计算机程序可存储于一计算机可读存储介质中,该计算机程序在被处理器执行时,可实现上述各个方法实施例的步骤。其中,所述计算机程序包括计算机程序代码,所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。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.
本发明并不局限于上述实施方式,如果对本发明的各种改动或变形不脱离本发明的精神和范围,倘若这些改动和变形属于本发明的权利要求和等同技术范围之内,则本发明也意图包含这些改动和变形。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.
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