CN115002783A - A dynamic allocation method for industrial IoT resources based on network slicing - Google Patents

A dynamic allocation method for industrial IoT resources based on network slicing Download PDF

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CN115002783A
CN115002783A CN202210418589.1A CN202210418589A CN115002783A CN 115002783 A CN115002783 A CN 115002783A CN 202210418589 A CN202210418589 A CN 202210418589A CN 115002783 A CN115002783 A CN 115002783A
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CN115002783B (en
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袁亚洲
孙明昊
孙明月
马锴
关新平
朱明增
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W16/00Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/02Resource partitioning among network components, e.g. reuse partitioning
    • H04W16/10Dynamic resource partitioning
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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    • H04L41/0893Assignment of logical groups to network elements
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W16/00Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22Traffic simulation tools or models
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W28/00Network traffic management; Network resource management
    • H04W28/16Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/18Negotiating wireless communication parameters
    • H04W28/20Negotiating bandwidth
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W28/00Network traffic management; Network resource management
    • H04W28/16Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/24Negotiating SLA [Service Level Agreement]; Negotiating QoS [Quality of Service]
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02DCLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

本发明公开了一种基于网络切片的工业物联网资源动态分配方法,本发明利用作为中继的MEC节点提供的计算能力和服务,在保障mMTC切片大规模连接需求及底层设备数据实时上传的前提下,将其上传的大规模数据在边缘侧按需适时进行计算处理,有效的减少了数据上传到云端的带宽占用,并将释放的带宽资源提供给对时延要求较高的URLLC切片,降低了其数据上传时延并提高了网络中的带宽利用率,同时优化了mMTC设备在MEC节点进行数据处理的计算功耗,以满足工业中低时延业务及海量连接业务的QoS需求。

Figure 202210418589

The invention discloses a dynamic allocation method for industrial Internet of Things resources based on network slicing. The invention utilizes the computing capabilities and services provided by MEC nodes serving as relays, on the premise of ensuring large-scale connection requirements of mMTC slices and real-time uploading of underlying device data. In this way, the large-scale data uploaded by it is calculated and processed on the edge side in a timely manner on demand, which effectively reduces the bandwidth occupancy of data uploading to the cloud, and provides the released bandwidth resources to URLLC slices with high latency requirements. It reduces the data upload delay and improves the bandwidth utilization in the network. At the same time, it optimizes the computing power consumption of the mMTC equipment for data processing at the MEC node, so as to meet the QoS requirements of low-latency services and massive connection services in the industry.

Figure 202210418589

Description

一种基于网络切片的工业物联网资源动态分配方法A dynamic allocation method for industrial IoT resources based on network slicing

技术领域technical field

本发明涉及工业场景中差异化业务通信技术领域,尤其是一种基于网络切片的工业物联网资源动态分配方法。The invention relates to the technical field of differentiated service communication in industrial scenarios, in particular to a method for dynamic allocation of industrial Internet of Things resources based on network slicing.

背景技术Background technique

网络切片是如今高速无线网络的一项关键技术,可将现实存在的物理网络划分为多个彼此独立且类型不同的虚拟网络,依照不同业务的QoS(服务质量)需求,诸如时延高低、带宽大小等指标为其分配相应的网络功能和网络资源,从而实现为差异化业务提供定制化服务的目标。根据应用场景中业务分类,网络切片可分为三大类,包括大连接需求mMTC(海量机器类通信)、超低时延需求URLLC(超可靠低时延通信)和大容量需求eMBB(大带宽通信)。为了实现不同网络切片的特定功能,可以通过融合边缘计算技术来解决这项难题。Network slicing is a key technology in today's high-speed wireless networks. It can divide the existing physical network into multiple virtual networks that are independent of each other and of different types. The size and other indicators are allocated corresponding network functions and network resources, so as to achieve the goal of providing customized services for differentiated services. According to the business classification in the application scenario, network slicing can be divided into three categories, including mMTC (mass machine-type communication) with large connection requirements, URLLC (ultra-reliable and low-latency communication) with ultra-low latency requirements, and eMBB (large bandwidth) with large-capacity requirements communication). In order to realize the specific functions of different network slices, this difficult problem can be solved by fusing edge computing technology.

边缘计算是一种在物理上靠近数据源头的网络边缘侧,融合网络、计算、存储、应用核心能力的开放平台,就近提供边缘智能服务的计算模式。物联网在各个领域蓬勃发展,万物互联的时代渐行渐近。Edge computing is an open platform that integrates the core capabilities of network, computing, storage, and application on the edge of the network, which is physically close to the data source, and provides edge intelligent services nearby. The Internet of Things is booming in various fields, and the era of the Internet of Everything is gradually approaching.

在物联网时代,随着业务的发展,将有大量的设备接入网络,这些设备分属于不同的领域且具有差异化QoS需求,比如在工业物联网领域,自动化设备运动控制、故障报警等任务要求进行实时可靠数据传输,网络时延需达到毫秒级;而工业测控、过程感知等任务要求进行大规模、低功耗传输。In the Internet of Things era, with the development of business, a large number of devices will be connected to the network. These devices belong to different fields and have differentiated QoS requirements. For example, in the field of industrial Internet of Things, automation equipment motion control, fault alarm and other tasks Real-time reliable data transmission is required, and the network delay needs to reach the millisecond level; while tasks such as industrial measurement and control and process perception require large-scale, low-power transmission.

随着物联网的发展,现在几乎所有的电子设备都可以连接到互联网并且会产生海量的数据,传输如此海量的数据从本地到云端,这对于网络带宽是个巨大的挑战。因此,利用网络切片技术将不同QoS需求业务进行分片,并通过融合边缘计算技术,利用其计算、存储服务动态调整切片间的网络带宽资源分配十分必要。With the development of the Internet of Things, almost all electronic devices can now be connected to the Internet and generate massive amounts of data. It is a huge challenge for network bandwidth to transmit such massive amounts of data from local to cloud. Therefore, it is necessary to use network slicing technology to segment services with different QoS requirements, and to use edge computing technology to dynamically adjust the network bandwidth resource allocation between slices by using its computing and storage services.

发明内容SUMMARY OF THE INVENTION

本发明需要解决的技术问题是提供一种基于网络切片的工业物联网资源动态分配方法,满足URLLC和mMTC两种机器类通信业务的QoS需求,并根据其动态QoS需求,将网络中的有限带宽资源在两种业务间进行动态的按需分配,使网络中有限的带宽资源利用率最大化。The technical problem to be solved by the present invention is to provide a dynamic allocation method for industrial Internet of Things resources based on network slicing, which can meet the QoS requirements of URLLC and mMTC machine-type communication services, and according to its dynamic QoS requirements, the limited bandwidth in the network is allocated. Resources are dynamically allocated between the two services on demand to maximize the utilization of limited bandwidth resources in the network.

为解决上述技术问题,本发明所采用的技术方案是:一种基于网络切片的工业物联网资源动态分配方法,包括如下步骤:In order to solve the above technical problems, the technical solution adopted in the present invention is: a method for dynamic allocation of industrial Internet of Things resources based on network slicing, comprising the following steps:

步骤S1、搭建三层双向通信网络架构:所述三层双向通信网络架构包括综合管理终端、云中心及由不同QoS需求建立的彼此相互独立、隔离的URLLC切片和mMTC切片,所述URLLC切片包括U个URLLC设备和K个FAP;所述mMTC切片包括M个mMTC设备、N个子载波和一个MEC节点;Step S1, build a three-layer two-way communication network architecture: the three-layer two-way communication network architecture includes a comprehensive management terminal, a cloud center, and mutually independent and isolated URLLC slices and mMTC slices established by different QoS requirements, and the URLLC slices include U URLLC devices and K FAPs; the mMTC slice includes M mMTC devices, N subcarriers and one MEC node;

步骤S2、U个URLLC设备集合表示为P={1,2,3...,U},K个FAP集合表示为Q={1,2,3...,K},其中u∈P,k∈Q,计算当第u个URLLC设备选择第k个FAP进行数据传输时的上传速率Ru,k,从而计算得出每个URLLC设备的数据上传时延tuStep S2, U URLLC device sets are expressed as P={1, 2, 3..., U}, K FAP sets are expressed as Q={1, 2, 3..., K}, where u∈P , k∈Q, calculate the upload rate R u,k when the u-th URLLC device selects the k-th FAP for data transmission, so as to calculate the data upload delay t u of each URLLC device;

步骤S3、M个mMTC设备集合表示为T={1,2,3...,M},N个子载波集合表示为Y={1,2,3...,N},其中m∈T,n∈Y,计算每个mMTC设备在任一子载波n上的数据上传速率Rm,n,从而计算得出每个mMTC设备通过第n个子载波在MEC节点中的计算能耗Em并得到总计算能耗∑m∈MEmStep S3, M mMTC device sets are represented as T={1, 2, 3..., M}, and N subcarrier sets are represented as Y={1, 2, 3..., N}, where m∈T , n∈Y, calculate the data upload rate R m,n of each mMTC device on any subcarrier n, so as to calculate the computational energy consumption E m of each mMTC device in the MEC node through the nth subcarrier and obtain Total computing energy ∑ m∈M E m ;

步骤S4、在mMTC切片中,利用MEC节点的计算能力,建立一个带宽释放模型;Step S4, in the mMTC slice, use the computing power of the MEC node to establish a bandwidth release model;

步骤S5、在经过MEC节点的计算处理后,每个mMTC设备所承载利用的子载波带宽为

Figure BDA0003605870580000021
由此得到每个mMTC设备此时的数据上传速率R* m,n;Step S5, after the calculation processing of the MEC node, the sub-carrier bandwidth carried and utilized by each mMTC device is:
Figure BDA0003605870580000021
Thus obtain the data upload rate R * m, n of each mMTC device at this time;

步骤S6、在系统总带宽有限的情况下,根据URLLC切片和mMTC切片分别向综合管理终端发送的服务需求,将带宽资源在URLLC切片和mMTC切片间进行动态的按需调整;Step S6, under the situation that the total system bandwidth is limited, according to the service requirements sent to the comprehensive management terminal respectively by the URLLC slice and the mMTC slice, the bandwidth resources are dynamically adjusted on demand between the URLLC slice and the mMTC slice;

步骤S7、根据URLLC切片和mMTC切片的QoS需求,分别给出优化目标函数,并将URLLC切片和mMTC切片间形成的资源分配问题转化为一个分层博弈问题,通过研究该博弈,得到博弈均衡解。Step S7: According to the QoS requirements of the URLLC slice and the mMTC slice, the optimization objective function is given respectively, and the resource allocation problem formed between the URLLC slice and the mMTC slice is transformed into a hierarchical game problem, and the game equilibrium solution is obtained by studying the game. .

本发明技术方案的进一步改进在于:所述步骤S2中当第u个URLLC设备选择第k个FAP进行数据传输时的上传速率Ru,k表示为:The further improvement of the technical solution of the present invention is: in the step S2, when the u-th URLLC device selects the k-th FAP for data transmission, the upload rate R u, k is expressed as:

Figure BDA0003605870580000031
Figure BDA0003605870580000031

其中,σ2为噪声功率,Buk、puk、huk分别为第u个URLLC设备选择第k个FAP进行数据上传时的带宽总量、传输功率、信道增益;Among them, σ 2 is the noise power, and B uk , p uk , and h uk are the total bandwidth, transmission power, and channel gain when the u-th URLLC device selects the k-th FAP for data uploading;

每个URLLC设备的数据上传速率表达式为:The data upload rate expression for each URLLC device is:

Ru(xuk)=xukRu,kR u (x uk )=x uk R u,k ,

其中,定义xuk∈{0,1}为一个布尔变量,当第u个URLLC设备选择第k个FAP进行数据传输时,xuk=1,否则xuk=0;Among them, define x uk ∈ {0, 1} as a Boolean variable, when the u-th URLLC device selects the k-th FAP for data transmission, x uk =1, otherwise x uk =0;

每个URLLC设备的数据上传时延tu表达式为:The data upload delay t u expression of each URLLC device is:

Figure BDA0003605870580000032
Figure BDA0003605870580000032

其中,su为URLLC设备上传数据内容的大小。Among them, s u is the size of the data content uploaded by the URLLC device.

本发明技术方案的进一步改进在于:所述步骤S3中每个mMTC设备在任一子载波n上的数据上传速率Rm,n的表达式为:The further improvement of the technical solution of the present invention is: in the step S3, the data upload rate R m of each mMTC device on any sub-carrier n, the expression of n is:

Figure BDA0003605870580000033
Figure BDA0003605870580000033

其中,Bn为每个子载波的带宽,pmn和hmn分别为第m个mMTC设备在任一子载波n上进行数据上传时的传输功率、信道增益;Wherein, B n is the bandwidth of each sub-carrier, p mn and h mn are the transmission power and channel gain when the mth mMTC device performs data uploading on any sub-carrier n;

第m个mMTC设备的数据上传速率表达式为:The data upload rate expression of the mth mMTC device is:

Rm(xmn)=xmnRm,nR m (x mn )=x mn R m,n ,

其中,定义xmn∈{0,1}为一个布尔变量,xmn=1表示第m个mMTC设备是分配在任意第n个子载波的标志变量,否则xmn=0;Among them, define x mn ∈ {0, 1} as a Boolean variable, x mn =1 indicates that the m-th mMTC device is a flag variable allocated to any n-th sub-carrier, otherwise x mn =0;

每个mMTC设备通过第n个子载波在MEC节点中的计算能耗Em表达式为:The computational energy consumption E m of each mMTC device through the nth subcarrier in the MEC node is expressed as:

Em=xmnsmcmαfi 2 E m =x mn s m c m αf i 2

sm表示为第m个mMTC设备需要上传的数据量大小,cm表示为每比特数据的计算复杂度,α为MEC节点计算能力的调整参数,fi 2为MEC节点计算能力。s m represents the amount of data that the mth mMTC device needs to upload, c m represents the computational complexity of each bit of data, α is an adjustment parameter for the computing capability of the MEC node, and f i 2 is the computing capability of the MEC node.

本发明技术方案的进一步改进在于:所述步骤S4中带宽释放模型表达式为:The further improvement of the technical solution of the present invention is: in the step S4, the bandwidth release model expression is:

Figure BDA0003605870580000041
Figure BDA0003605870580000041

其中,BnM为每个mMTC设备经过MEC节点处理后释放的带宽,K和ω是模型参数,θ为MEC节点带宽的调整参数。Among them, B nM is the bandwidth released by each mMTC device after being processed by the MEC node, K and ω are the model parameters, and θ is the adjustment parameter of the MEC node bandwidth.

本发明技术方案的进一步改进在于:所述步骤S5中经过MEC节点的计算处理后的每个mMTC设备此时的数据上传速率R* m,n表达式为:The further improvement of the technical solution of the present invention is: the data upload rate R * m of each mMTC device after the calculation processing of the MEC node in the step S5, and the expression of n is:

Figure BDA0003605870580000042
Figure BDA0003605870580000042

本发明技术方案的进一步改进在于:所述步骤S7中URLLC切片的优化目标为最小化数据上传时延,对应的优化目标函数的表达式为:The further improvement of the technical solution of the present invention is: in the step S7, the optimization objective of the URLLC slice is to minimize the data upload time delay, and the expression of the corresponding optimization objective function is:

Figure BDA0003605870580000043
Figure BDA0003605870580000043

Figure BDA0003605870580000044
Figure BDA0003605870580000044

约束表示URLLC设备通过选择的FAP进行数据上传时的时延不得超过其所能容忍的最大时延

Figure BDA0003605870580000045
The constraint means that the URLLC device's delay in uploading data through the selected FAP must not exceed the maximum delay it can tolerate
Figure BDA0003605870580000045

mMTC切片的优化目标为保障设备数据实时上传的前提下,最小化设备在MEC节点进行数据处理的计算功耗,对应的优化目标函数的表达式为:The optimization goal of the mMTC slice is to minimize the computing power consumption of the device for data processing in the MEC node under the premise of ensuring the real-time upload of device data. The expression of the corresponding optimization objective function is:

Figure BDA0003605870580000046
Figure BDA0003605870580000046

Figure BDA0003605870580000047
Figure BDA0003605870580000047

Figure BDA0003605870580000048
Figure BDA0003605870580000048

约束(b1)表示MEC节点的数据处理能耗不得超过系统所能容忍的最大能耗

Figure BDA0003605870580000051
约束(b2)表示各mMTC设备经MEC节点数据处理后上传数据的传输速率不得低于其能容忍的最低传输速率
Figure BDA0003605870580000052
Constraint (b1) means that the data processing energy consumption of the MEC node must not exceed the maximum energy consumption that the system can tolerate
Figure BDA0003605870580000051
Constraint (b2) means that the transmission rate of data uploaded by each mMTC device after data processing by the MEC node shall not be lower than the minimum transmission rate that it can tolerate
Figure BDA0003605870580000052

本发明技术方案的进一步改进在于:所述步骤S7中将URLLC切片和mMTC切片之间的互动建模为Stackelberg博弈,将URLLC切片作为跟随者,mMTC切片作为领导者,作为跟随者的URLLC切片决定从mMTC切片获取的释放带宽数量,以最小化其数据上传时延,而mMTC切片根据URLLC切片发送的数据上传时延请求做出决策,在保障mMTC切片中各设备数据上传速率大于其最低速率限制的前提下,决定其上传的数据在MEC节点中的计算能力同时优化计算功耗,通过研究URLLC切片和mMTC切片之间的博弈,得到最优的带宽分配解,在该博弈均衡解下,使得URLLC切片和mMTC切片的Qos需求达到均衡。A further improvement of the technical solution of the present invention is: in the step S7, the interaction between the URLLC slice and the mMTC slice is modeled as a Stackelberg game, the URLLC slice is used as a follower, the mMTC slice is used as a leader, and the URLLC slice as a follower is determined. The amount of released bandwidth obtained from the mMTC slice to minimize its data upload delay, and the mMTC slice makes a decision based on the data upload delay request sent by the URLLC slice to ensure that the data upload rate of each device in the mMTC slice is greater than its minimum rate limit Under the premise of determining the computing power of the uploaded data in the MEC node and optimizing the computing power consumption, by studying the game between the URLLC slice and the mMTC slice, the optimal bandwidth allocation solution is obtained. Under the equilibrium solution of the game, the The QoS requirements of URLLC slice and mMTC slice are balanced.

由于采用了上述技术方案,本发明取得的技术进步是:Owing to having adopted the above-mentioned technical scheme, the technical progress that the present invention obtains is:

1、本发明突破了传统移动通信面向“大带宽通信”的单一技术路线,利用网络切片技术和边缘计算技术,制定了一种针对如今工业高速无线网络如5G中面向垂直行业的“海量机器类通信”和“超可靠低时延通信”业务间的资源分配方案,降低了经济成本并提高了资源利用率;1. The present invention breaks through the single technical route of traditional mobile communication oriented to "large bandwidth communication", and uses network slicing technology and edge computing technology to formulate a "mass machine class" for vertical industries in today's industrial high-speed wireless networks such as 5G. The resource allocation scheme between "communication" and "ultra-reliable and low-latency communication" services reduces economic costs and improves resource utilization;

2、本发明利用作为中继的MEC节点提供的计算能力和服务,在保障mMTC切片大规模连接需求及底层设备数据实时上传的前提下,将其上传的大规模数据在边缘侧按需适时进行计算处理,有效的减少了数据上传到云端的带宽占用,并将释放的带宽资源提供给对时延要求较高的URLLC切片,降低了其数据上传时延并提高了网络中的带宽利用率,同时优化了mMTC设备在MEC节点进行数据处理的计算功耗,以满足工业中低时延业务及海量连接业务的QoS需求。2. The present invention utilizes the computing power and services provided by the MEC node as a relay, and under the premise of ensuring the large-scale connection requirements of mMTC slices and the real-time upload of underlying device data, the large-scale data uploaded by it is timely performed on the edge side as needed. Computational processing effectively reduces the bandwidth occupancy of data uploading to the cloud, and provides the released bandwidth resources to URLLC slices with high latency requirements, reducing the data upload latency and improving the bandwidth utilization in the network. At the same time, the computing power consumption of mMTC equipment for data processing at the MEC node is optimized to meet the QoS requirements of low-latency services and massive connection services in the industry.

附图说明Description of drawings

图1是本发明构造的三层双向通信网络切片架构图;Fig. 1 is a three-layer bidirectional communication network slice architecture diagram constructed by the present invention;

图2是本发明带宽资源分配流程图。FIG. 2 is a flow chart of bandwidth resource allocation according to the present invention.

具体实施方式Detailed ways

下面结合实施例对本发明做进一步详细说明:Below in conjunction with embodiment, the present invention is described in further detail:

本发明是在系统带宽资源有限的情况下,针对工业场景中各个切片的差异化QoS需求,通过动态的虚拟资源分配来满足用户的业务需求并提升网络经济收益,而研发的一种联合边缘计算技术的网络切片间资源动态分配方法。In the case of limited system bandwidth resources, the invention is based on the differentiated QoS requirements of each slice in the industrial scene, through dynamic virtual resource allocation to meet the business needs of users and improve the economic benefits of the network, and developed a joint edge computing A method for dynamic resource allocation between network slices of technology.

下面结合附图对本发明做进一步说明:The present invention will be further described below in conjunction with the accompanying drawings:

步骤S1、搭建三层双向通信网络架构:如图1所示,三层双向通信网络架构包括综合管理终端、云中心及由不同QoS需求建立的彼此相互独立、隔离的URLLC切片和mMTC切片,所述URLLC切片包括U个URLLC设备和K个FAP,通过将带有缓存、计算能力的FAP下沉到网络边缘,部署在靠近底层设备的区域,改善了设备直接向综合管理终端远距离上传数据造成的时延较高的情况。所述mMTC切片包括M个mMTC设备、N个子载波和一个MEC节点,MEC节点用来压缩大规模设备上传数据时所需的超高带宽占用,将MEC节点作为数据转发中继,通过对底层设备上传的数据进行计算处理后只上传少量数据,有效的减少了数据上传到云端的带宽占用。Step S1, build a three-layer two-way communication network architecture: as shown in Figure 1, the three-layer two-way communication network architecture includes an integrated management terminal, a cloud center, and URLLC slices and mMTC slices that are independent and isolated from each other based on different QoS requirements. The URLLC slice described above includes U URLLC devices and K FAPs. By sinking the FAPs with cache and computing capabilities to the edge of the network and deploying them in the area close to the underlying devices, the problems caused by the devices directly uploading data to the comprehensive management terminal over long distances are improved. with high latency. The mMTC slice includes M mMTC devices, N subcarriers and one MEC node. The MEC node is used to compress the ultra-high bandwidth occupied by large-scale devices when uploading data, and the MEC node is used as a data forwarding relay. After the uploaded data is calculated and processed, only a small amount of data is uploaded, which effectively reduces the bandwidth consumption of data uploaded to the cloud.

步骤S2、U个URLLC设备集合表示为P={1,2,3...,U},K个FAP集合表示为Q={1,2,3...,K},其中u∈P,k∈Q,计算当第u个URLLC设备选择第k个FAP进行数据传输时的上传速率Ru,k,从而计算得出每个URLLC设备的数据上传时延tuStep S2, U URLLC device sets are expressed as P={1, 2, 3..., U}, K FAP sets are expressed as Q={1, 2, 3..., K}, where u∈P , k∈Q, calculate the upload rate R u,k when the u-th URLLC device selects the k-th FAP for data transmission, so as to calculate the data upload delay t u of each URLLC device;

其中当第u个URLLC设备选择第k个FAP进行数据传输时的上传速率Ru,k表示为:The upload rate R u when the u-th URLLC device selects the k-th FAP for data transmission, k is expressed as:

Figure BDA0003605870580000061
Figure BDA0003605870580000061

其中,σ2为噪声功率,Buk、puk、huk分别为第u个URLLC设备选择第k个FAP进行数据上传时的带宽总量、传输功率、信道增益;Among them, σ 2 is the noise power, and B uk , p uk , and h uk are the total bandwidth, transmission power, and channel gain when the u-th URLLC device selects the k-th FAP for data uploading;

每个URLLC设备的数据上传速率表达式为:The data upload rate expression for each URLLC device is:

Ru(xuk)=xukRu,kR u (x uk )=x uk R u,k ,

由于在URLLC切片中包含多个FAP,且每个设备可选择任一合适的FAP作为中继节点,因此,定义xuk∈{0,1}为一个布尔变量,当第u个URLLC设备选择第k个FAP进行数据传输时,xuk=1,否则xuk=0;Since there are multiple FAPs in the URLLC slice, and each device can choose any suitable FAP as the relay node, therefore, define x uk ∈ {0, 1} as a Boolean variable, when the u-th URLLC device selects the When k FAPs perform data transmission, x uk =1, otherwise x uk =0;

每个URLLC设备的数据上传时延tu表达式为:The data upload delay t u expression of each URLLC device is:

Figure BDA0003605870580000071
Figure BDA0003605870580000071

其中,su为URLLC设备上传数据内容的大小。Among them, s u is the size of the data content uploaded by the URLLC device.

步骤S3、M个mMTC设备集合表示为T={1,2,3...,M},N个子载波集合表示为Y={1,2,3...,N},其中m∈T,n∈Y,计算每个mMTC设备在任一子载波n上的数据上传速率Rm,n,从而计算得出每个mMTC设备通过第n个子载波在MEC节点中的计算能耗Em并得到总计算能耗∑m∈MEmStep S3, M mMTC device sets are represented as T={1, 2, 3..., M}, and N subcarrier sets are represented as Y={1, 2, 3..., N}, where m∈T , n∈Y, calculate the data upload rate R m,n of each mMTC device on any subcarrier n, so as to calculate the computational energy consumption Em of each mMTC device in the MEC node through the nth subcarrier and obtain the total Calculate energy consumption ∑ m∈ME m ;

其中,每个mMTC设备在任一子载波n上的数据上传速率Rm,n的表达式为:Among them, the data upload rate R m of each mMTC device on any sub-carrier n, the expression of n is:

Figure BDA0003605870580000072
Figure BDA0003605870580000072

其中,Bn为每个子载波的带宽,pmn和hmn分别为第m个mMTC设备在任一子载波n上进行数据上传时的传输功率、信道增益;Wherein, B n is the bandwidth of each sub-carrier, p mn and h mn are the transmission power and channel gain when the mth mMTC device performs data uploading on any sub-carrier n;

第m个mMTC设备的数据上传速率表达式为:The data upload rate expression of the mth mMTC device is:

Rm(xmn)=xmnRm,nR m (x mn )=x mn R m,n ,

其中,定义xmn∈{0,1}为一个布尔变量,xmn=1表示第m个mMTC设备是分配在任意第n个子载波的标志变量,否则xmn=0;Among them, define x mn ∈ {0, 1} as a Boolean variable, x mn =1 indicates that the m-th mMTC device is a flag variable allocated to any n-th sub-carrier, otherwise x mn =0;

每个mMTC设备通过第n个子载波在MEC节点中的计算能耗Em表达式为:The computational energy consumption E m of each mMTC device through the nth subcarrier in the MEC node is expressed as:

Em=xmnsmcmαfi 2 E m =x mn s m c m αf i 2

sm表示为第m个mMTC设备需要上传的数据量大小,cm表示为每比特数据的计算复杂度,α为MEC节点计算能力的调整参数,fi 2为MEC节点计算能力。s m represents the amount of data that the mth mMTC device needs to upload, c m represents the computational complexity of each bit of data, α is an adjustment parameter for the computing capability of the MEC node, and f i 2 is the computing capability of the MEC node.

步骤S4、在mMTC切片中,利用MEC节点的计算能力,建立一个带宽释放模型,带宽释放模型表达式为:Step S4, in the mMTC slice, use the computing power of the MEC node to establish a bandwidth release model, and the expression of the bandwidth release model is:

Figure BDA0003605870580000073
Figure BDA0003605870580000073

其中,BnM为每个mMTC设备经过MEC节点处理后释放的带宽,K和ω是模型参数,θ为MEC节点带宽的调整参数。Among them, B nM is the bandwidth released by each mMTC device after being processed by the MEC node, K and ω are the model parameters, and θ is the adjustment parameter of the MEC node bandwidth.

步骤S5、在经过MEC节点的计算处理后,每个mMTC设备所承载利用的子载波带宽为

Figure BDA0003605870580000088
由此得到每个mMTC设备此时的数据上传速率R* m,n,表达式为:Step S5, after the calculation processing of the MEC node, the sub-carrier bandwidth carried and utilized by each mMTC device is:
Figure BDA0003605870580000088
From this, the data upload rate R * m, n of each mMTC device at this time is obtained, and the expression is:

Figure BDA0003605870580000081
Figure BDA0003605870580000081

步骤S6、在系统总带宽有限的情况下,根据URLLC切片和mMTC切片分别向综合管理终端发送的服务需求,将带宽资源在URLLC切片和mMTC切片间进行动态的按需调整,如图2所示,假设预先分配给URLLC切片的带宽总量为B1,分配给mMTC切片的带宽总量为B2,经过mMTC切片中作为中继的MEC节点计算处理后,在保障mMTC切片底层设备的最低上传速率前提下,释放的带宽总量为B3,并根据URLLC切片发送的服务请求,将释放的带宽资源提供给URLLC切片利用,以降低其数据上传时延并提高网络中的带宽利用率。Step S6, in the case of limited total system bandwidth, according to the service requirements sent to the comprehensive management terminal respectively by the URLLC slice and the mMTC slice, dynamically adjust the bandwidth resources between the URLLC slice and the mMTC slice as needed, as shown in Figure 2. , assuming that the total amount of bandwidth pre-allocated to the URLLC slice is B1, and the total amount of bandwidth allocated to the mMTC slice is B2, after the calculation and processing of the MEC node as a relay in the mMTC slice, the minimum upload rate of the underlying device of the mMTC slice is guaranteed. The total amount of released bandwidth is B3, and according to the service request sent by the URLLC slice, the released bandwidth resources are provided to the URLLC slice for utilization to reduce its data upload delay and improve the bandwidth utilization in the network.

步骤S7、根据URLLC切片和mMTC切片的QoS需求,分别给出优化目标函数,URLLC切片的优化目标为最小化数据上传时延,对应的优化目标函数的表达式为:Step S7, according to the QoS requirements of the URLLC slice and the mMTC slice, the optimization objective function is given respectively. The optimization objective of the URLLC slice is to minimize the data upload delay, and the expression of the corresponding optimization objective function is:

Figure BDA0003605870580000082
Figure BDA0003605870580000082

Figure BDA0003605870580000083
Figure BDA0003605870580000083

约束表示URLLC设备通过选择的FAP进行数据上传时的时延不得超过其所能容忍的最大时延

Figure BDA0003605870580000084
The constraint means that the URLLC device's delay in uploading data through the selected FAP must not exceed the maximum delay it can tolerate
Figure BDA0003605870580000084

mMTC切片的优化目标为保障设备数据实时上传的前提下,最小化设备在MEC节点进行数据处理的计算功耗,对应的优化目标函数的表达式为:The optimization goal of the mMTC slice is to minimize the computing power consumption of the device for data processing in the MEC node under the premise of ensuring the real-time upload of device data. The expression of the corresponding optimization objective function is:

Figure BDA0003605870580000085
Figure BDA0003605870580000085

Figure BDA0003605870580000086
Figure BDA0003605870580000086

Figure BDA0003605870580000087
Figure BDA0003605870580000087

约束(b1)表示MEC节点的数据处理能耗不得超过系统所能容忍的最大能耗

Figure BDA0003605870580000091
约束(b2)表示各mMTC设备经MEC节点数据处理后上传数据的传输速率不得低于其能容忍的最低传输速率
Figure BDA0003605870580000092
Constraint (b1) means that the data processing energy consumption of the MEC node must not exceed the maximum energy consumption that the system can tolerate
Figure BDA0003605870580000091
Constraint (b2) means that the transmission rate of data uploaded by each mMTC device after data processing by the MEC node shall not be lower than the minimum transmission rate that it can tolerate
Figure BDA0003605870580000092

将URLLC切片和mMTC切片间形成的资源分配问题转化为一个分层博弈问题,将URLLC切片和mMTC切片之间的互动建模为Stackelberg博弈,将URLLC切片作为跟随者,mMTC切片作为领导者,作为跟随者的URLLC切片决定从mMTC切片获取的释放带宽数量,以最小化其数据上传时延,而mMTC切片根据URLLC切片发送的数据上传时延请求做出决策,在保障mMTC切片中各设备数据上传速率大于其最低速率限制的前提下,决定其上传的数据在MEC节点中的计算能力同时优化计算功耗,通过研究URLLC切片和mMTC切片之间的博弈,得到最优的带宽分配解,在该博弈均衡解下,使得URLLC切片和mMTC切片的Qos需求达到均衡。The resource allocation problem formed between URLLC slices and mMTC slices is transformed into a hierarchical game problem, and the interaction between URLLC slices and mMTC slices is modeled as a Stackelberg game, with URLLC slices as followers, mMTC slices as leaders, as The follower's URLLC slice determines the amount of released bandwidth obtained from the mMTC slice to minimize its data upload delay, and the mMTC slice makes decisions based on the data upload delay request sent by the URLLC slice, ensuring data uploading of each device in the mMTC slice. Under the premise that the rate is greater than its minimum rate limit, determine the computing power of the uploaded data in the MEC node and optimize the computing power consumption. By studying the game between URLLC slices and mMTC slices, the optimal bandwidth allocation solution is obtained. Under the game equilibrium solution, the QoS requirements of URLLC slice and mMTC slice are balanced.

Claims (7)

1.一种基于网络切片的工业物联网资源动态分配方法,其特征在于:包括如下步骤:1. a method for dynamic allocation of industrial Internet of Things resources based on network slicing, is characterized in that: comprise the steps: 步骤S1、搭建三层双向通信网络架构:所述三层双向通信网络架构包括综合管理终端、云中心及由不同QoS需求建立的彼此相互独立、隔离的URLLC切片和mMTC切片,所述URLLC切片包括U个URLLC设备和K个FAP;所述mMTC切片包括M个mMTC设备、N个子载波和一个MEC节点;Step S1, build a three-layer two-way communication network architecture: the three-layer two-way communication network architecture includes a comprehensive management terminal, a cloud center, and mutually independent and isolated URLLC slices and mMTC slices established by different QoS requirements, and the URLLC slices include U URLLC devices and K FAPs; the mMTC slice includes M mMTC devices, N subcarriers and one MEC node; 步骤S2、U个URLLC设备集合表示为P={1,2,3...,U},K个FAP集合表示为Q={1,2,3...,K},其中u∈P,k∈Q,计算当第u个URLLC设备选择第k个FAP进行数据传输时的上传速率Ru,k,从而计算得出每个URLLC设备的数据上传时延tuStep S2, U URLLC device sets are expressed as P={1, 2, 3..., U}, K FAP sets are expressed as Q={1, 2, 3..., K}, where u∈P , k∈Q, calculate the upload rate R u,k when the u-th URLLC device selects the k-th FAP for data transmission, so as to calculate the data upload delay t u of each URLLC device; 步骤S3、M个mMTC设备集合表示为T={1,2,3...,M},N个子载波集合表示为Y={1,2,3...,N},其中m∈T,n∈Y,计算每个mMTC设备在任一子载波n上的数据上传速率Rm,n,从而计算得出每个mMTC设备通过第n个子载波在MEC节点中的计算能耗Em并得到总计算能耗∑m∈ MEmStep S3, M mMTC device sets are represented as T={1, 2, 3..., M}, and N subcarrier sets are represented as Y={1, 2, 3..., N}, where m∈T , n∈Y, calculate the data upload rate R m,n of each mMTC device on any subcarrier n, so as to calculate the computational energy consumption E m of each mMTC device in the MEC node through the nth subcarrier and obtain Total computing energy ∑ m∈ M E m ; 步骤S4、在mMTC切片中,利用MEC节点的计算能力,建立一个带宽释放模型;Step S4, in the mMTC slice, use the computing power of the MEC node to establish a bandwidth release model; 步骤S5、在经过MEC节点的计算处理后,每个mMTC设备所承载利用的子载波带宽为
Figure FDA0003605870570000011
由此得到每个mMTC设备此时的数据上传速率R* m,n
Step S5, after the calculation processing of the MEC node, the sub-carrier bandwidth carried and utilized by each mMTC device is:
Figure FDA0003605870570000011
Thus obtain the data upload rate R * m, n of each mMTC device at this time;
步骤S6、在系统总带宽有限的情况下,根据URLLC切片和mMTC切片分别向综合管理终端发送的服务需求,将带宽资源在URLLC切片和mMTC切片间进行动态的按需调整;Step S6, under the situation that the total system bandwidth is limited, according to the service requirements sent to the comprehensive management terminal respectively by the URLLC slice and the mMTC slice, the bandwidth resources are dynamically adjusted on demand between the URLLC slice and the mMTC slice; 步骤S7、根据URLLC切片和mMTC切片的QoS需求,分别给出优化目标函数,并将URLLC切片和mMTC切片间形成的资源分配问题转化为一个分层博弈问题,通过研究该博弈,得到博弈均衡解。Step S7: According to the QoS requirements of the URLLC slice and the mMTC slice, the optimization objective function is given respectively, and the resource allocation problem formed between the URLLC slice and the mMTC slice is transformed into a hierarchical game problem, and the game equilibrium solution is obtained by studying the game. .
2.根据权利要求1所述的一种基于网络切片的工业物联网资源动态分配方法,其特征在于:所述步骤S2中当第u个URLLC设备选择第k个FAP进行数据传输时的上传速率Ru.k表示为:2. a kind of industrial Internet of Things resource dynamic allocation method based on network slice according to claim 1, is characterized in that: in described step S2, when the u th URLLC device selects the k th FAP to carry out the upload rate of data transmission Ru.k is expressed as:
Figure FDA0003605870570000021
Figure FDA0003605870570000021
其中,σ2为噪声功率,Buk、puk、huk分别为第u个URLLC设备选择第k个FAP进行数据上传时的带宽总量、传输功率、信道增益;Among them, σ 2 is the noise power, and B uk , p uk , and h uk are the total bandwidth, transmission power, and channel gain when the u-th URLLC device selects the k-th FAP for data uploading; 每个URLLC设备的数据上传速率表达式为:The data upload rate expression for each URLLC device is: Ru(xuk)=xukRu,kR u (x uk )=x uk R u,k , 其中,定义xuk∈{0,1}为一个布尔变量,当第u个URLLC设备选择第k个FAP进行数据传输时,xuk=1,否则xuk=0;Among them, define x uk ∈ {0, 1} as a Boolean variable, when the u-th URLLC device selects the k-th FAP for data transmission, x uk =1, otherwise x uk =0; 每个URLLC设备的数据上传时延tu表达式为:The data upload delay t u expression of each URLLC device is:
Figure FDA0003605870570000022
Figure FDA0003605870570000022
其中,su为URLLC设备上传数据内容的大小。Among them, s u is the size of the data content uploaded by the URLLC device.
3.根据权利要求2所述的一种基于网络切片的工业物联网资源动态分配方法,其特征在于:所述步骤S3中每个mMTC设备在任一子载波n上的数据上传速率Rm,n的表达式为:3. a kind of industrial Internet of Things resource dynamic allocation method based on network slice according to claim 2, is characterized in that: in described step S3, the data upload rate R m of each mMTC device on any sub-carrier n, n The expression is:
Figure FDA0003605870570000023
Figure FDA0003605870570000023
其中,Bn为每个子载波的带宽,pmn和hmn分别为第m个mMTC设备在任一子载波n上进行数据上传时的传输功率、信道增益;Wherein, B n is the bandwidth of each sub-carrier, p mn and h mn are the transmission power and channel gain when the mth mMTC device performs data uploading on any sub-carrier n; 第m个mMTC设备的数据上传速率表达式为:The data upload rate expression of the mth mMTC device is: Rm(xmn)=xmnRm,nR m (x mn )=x mn R m,n , 其中,定义xmn∈{0,1}为一个布尔变量,xmn=1表示第m个mMTC设备是分配在任意第n个子载波的标志变量,否则xmn=0;Among them, define x mn ∈ {0, 1} as a Boolean variable, x mn =1 indicates that the m-th mMTC device is a flag variable allocated to any n-th sub-carrier, otherwise x mn =0; 每个mMTC设备通过第n个子载波在MEC节点中的计算能耗Em表达式为:The computational energy consumption Em of each mMTC device through the nth subcarrier in the MEC node is expressed as: Em=xmnsmcmαfi 2E m =x mn s m c m αf i 2 , sm表示为第m个mMTC设备需要上传的数据量大小,cm表示为每比特数据的计算复杂度,α为MEC节点计算能力的调整参数,fi 2为MEC节点计算能力。s m represents the amount of data that the mth mMTC device needs to upload, c m represents the computational complexity of each bit of data, α is an adjustment parameter for the computing capability of the MEC node, and f i 2 is the computing capability of the MEC node.
4.根据权利要求3所述的一种基于网络切片的工业物联网资源动态分配方法,其特征在于:所述步骤S4中带宽释放模型表达式为:4. a kind of industrial Internet of Things resource dynamic allocation method based on network slice according to claim 3, is characterized in that: in described step S4, bandwidth release model expression is:
Figure FDA0003605870570000031
Figure FDA0003605870570000031
其中,BnM为每个mMTC设备经过MEC节点处理后释放的带宽,K和ω是模型参数,θ为MEC节点带宽的调整参数。Among them, B nM is the bandwidth released by each mMTC device after being processed by the MEC node, K and ω are the model parameters, and θ is the adjustment parameter of the MEC node bandwidth.
5.根据权利要求4所述的一种基于网络切片的工业物联网资源动态分配方法,其特征在于:所述步骤S5中经过MEC节点的计算处理后的每个mMTC设备此时的数据上传速率R* m,n表达式为:5. The method for dynamic allocation of industrial Internet of Things resources based on network slicing according to claim 4, characterized in that: the data upload rate of each mMTC device at this time after the calculation processing of the MEC node in the step S5 R * m,n expression is:
Figure FDA0003605870570000032
Figure FDA0003605870570000032
6.根据权利要求5所述的一种基于网络切片的工业物联网资源动态分配方法,其特征在于:所述步骤S7中URLLC切片的优化目标为最小化数据上传时延,对应的优化目标函数的表达式为:6. a kind of industrial Internet of Things resource dynamic allocation method based on network slice according to claim 5, is characterized in that: in described step S7, the optimization target of URLLC slice is to minimize data upload time delay, and the corresponding optimization target function The expression is:
Figure FDA0003605870570000033
Figure FDA0003605870570000033
Figure FDA0003605870570000034
Figure FDA0003605870570000034
约束表示URLLC设备通过选择的FAP进行数据上传时的时延不得超过其所能容忍的最大时延
Figure FDA0003605870570000035
The constraint means that the URLLC device's delay in uploading data through the selected FAP must not exceed the maximum delay it can tolerate
Figure FDA0003605870570000035
mMTC切片的优化目标为保障设备数据实时上传的前提下,最小化设备在MEC节点进行数据处理的计算功耗,对应的优化目标函数的表达式为:The optimization goal of the mMTC slice is to minimize the computing power consumption of the device for data processing in the MEC node under the premise of ensuring the real-time upload of device data. The expression of the corresponding optimization objective function is:
Figure FDA0003605870570000036
Figure FDA0003605870570000036
Figure FDA0003605870570000037
Figure FDA0003605870570000037
Figure FDA0003605870570000038
Figure FDA0003605870570000038
约束(b1)表示MEC节点的数据处理能耗不得超过系统所能容忍的最大能耗
Figure FDA0003605870570000041
约束(b2)表示各mMTC设备经MEC节点数据处理后上传数据的传输速率不得低于其能容忍的最低传输速率
Figure FDA0003605870570000042
Constraint (b1) means that the data processing energy consumption of the MEC node must not exceed the maximum energy consumption that the system can tolerate
Figure FDA0003605870570000041
Constraint (b2) means that the transmission rate of data uploaded by each mMTC device after data processing by the MEC node shall not be lower than the minimum transmission rate that it can tolerate
Figure FDA0003605870570000042
7.根据权利要求6所述的一种基于网络切片的工业物联网资源动态分配方法,其特征在于:所述步骤S7中将URLLC切片和mMTC切片之间的互动建模为Stackelberg博弈,将URLLC切片作为跟随者,mMTC切片作为领导者,作为跟随者的URLLC切片决定从mMTC切片获取的释放带宽数量,以最小化其数据上传时延,而mMTC切片根据URLLC切片发送的数据上传时延请求做出决策,在保障mMTC切片中各设备数据上传速率大于其最低速率限制的前提下,决定其上传的数据在MEC节点中的计算能力同时优化计算功耗,通过研究URLLC切片和mMTC切片之间的博弈,得到最优的带宽分配解,在该博弈均衡解下,使得URLLC切片和mMTC切片的Qos需求达到均衡。7. a kind of industrial Internet of Things resource dynamic allocation method based on network slice according to claim 6, is characterized in that: in described step S7, the interaction between URLLC slice and mMTC slice is modeled as Stackelberg game, URLLC The slice acts as a follower, the mMTC slice acts as a leader, and the URLLC slice as a follower determines the amount of released bandwidth obtained from the mMTC slice to minimize its data upload delay, and the mMTC slice is based on the data upload delay request sent by the URLLC slice. On the premise of ensuring that the data upload rate of each device in the mMTC slice is greater than its minimum rate limit, determine the computing power of the uploaded data in the MEC node and optimize the computing power consumption. By studying the relationship between URLLC slice and mMTC slice The optimal bandwidth allocation solution is obtained through the game, and the QoS requirements of the URLLC slice and the mMTC slice are balanced under the equilibrium solution of the game.
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