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CN114245426B - Heterogeneous network switching method based on fuzzy logic and oriented to service type - Google Patents

Heterogeneous network switching method based on fuzzy logic and oriented to service type Download PDF

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CN114245426B
CN114245426B CN202111371555.3A CN202111371555A CN114245426B CN 114245426 B CN114245426 B CN 114245426B CN 202111371555 A CN202111371555 A CN 202111371555A CN 114245426 B CN114245426 B CN 114245426B
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刘旭
胡俊华
朱晓荣
杨龙祥
朱洪波
江婷
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Nanjing University of Posts and Telecommunications
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Abstract

本发明公开了一种面向业务类型的基于模糊逻辑的异构网络切换方法,包括:终端获得当前异构网络场景下各个候选网络的参数信息;将应用业务类型分为四类,分别是会话类应用、交互类应用、流类应用和后台类应用,并针对每类应用业务类型,以网络带宽、时延和误码率为元素,分别设计会话类输入隶属度函数、交互类输入隶属度函数、流类输入隶属度函数和后台类输入隶属度函数;根据终端当前的应用业务类型选择相应的输入隶属度函数输入到模糊推理模块中进行模糊推理;根据模糊推理结果选择最佳网络进行切换。本发明不仅可以根据终端的应用类型合理地选择最佳切换网络,满足终端地个性化服务需求,还可以有效地降低平均切换次数。

Figure 202111371555

The invention discloses a fuzzy logic-based heterogeneous network switching method oriented to service types, comprising: a terminal obtains parameter information of each candidate network in the current heterogeneous network scene; and classifies application service types into four categories, namely, session applications, interactive applications, streaming applications, and background applications, and for each type of application business type, use network bandwidth, delay, and bit error rate as elements to design session-type input membership functions and interactive-type input membership functions , stream class input membership function and background class input membership function; select the corresponding input membership function according to the current application business type of the terminal and input it into the fuzzy reasoning module for fuzzy reasoning; select the best network to switch according to the fuzzy reasoning result. The present invention can not only rationally select the best switching network according to the application type of the terminal, meet the personalized service requirement of the terminal, but also effectively reduce the average switching times.

Figure 202111371555

Description

一种面向业务类型的基于模糊逻辑的异构网络切换方法A Fuzzy Logic-Based Heterogeneous Network Handover Method Oriented to Service Type

技术领域technical field

本发明涉及通信网络技术领域,具体而言涉及一种面向业务类型的基于模糊逻辑的异构网络切换方法。The invention relates to the technical field of communication networks, in particular to a fuzzy logic-based heterogeneous network switching method oriented to service types.

背景技术Background technique

随着无线通信技术的飞速发展,海量终端设备以及新兴业务需求也随之增长。网络中用户数量的迅猛增加使得网络流量也在爆发式增长,移动用户需要更高的传输速率,用户对于有限网络资源的竞争也变得愈加严峻。未来网络场景将面临海量物联,高流量大带宽,高可靠低时延等挑战。所以未来网络的发展趋势必定是不同特点的无线网络的融合,比如由UMTS、LTE、WIMAX、WLAN、5G等不同网络融合组成的异构无线网络,通过异构无线网络间的融合,各个网络可以最大程度地发挥自身地优势,从而满足移动用户对网络的需求。With the rapid development of wireless communication technology, the demand for massive terminal equipment and emerging services also increases. The rapid increase in the number of users in the network has led to explosive growth in network traffic. Mobile users require higher transmission rates, and users' competition for limited network resources has become increasingly severe. Future network scenarios will face challenges such as massive Internet of Things, high traffic and large bandwidth, high reliability and low latency. Therefore, the development trend of the future network must be the integration of wireless networks with different characteristics, such as heterogeneous wireless networks composed of different networks such as UMTS, LTE, WIMAX, WLAN, and 5G. Through the integration of heterogeneous wireless networks, each network can Maximize its own advantages, so as to meet the needs of mobile users for the network.

但是由于异构无线网络中引入了多种类型的小基站,比如5G移动通信中为了应对无线流量的一种重要方法就是除了部署传统的宏基站外,还部署了大量的微基站,所以未来网络的切换场景与传统的同构蜂窝网络的切换场景有很大的不同。移动用户需要在各种不同类型的基站之间进行切换,不仅网络的复杂度明显提高,而且给网络管理带来了极大的考验。以前传统的切换算法和切换流程可能会造成切换失败以及切换次数的增加,发生不必要的切换或乒乓效应等问题,并且这些传统的切换算法没有考虑到终端的个性化服务需求,所以对传统的切换算法提出改进或者提出一些新的切换判决算法式未来网络发展的一个重要方向。However, due to the introduction of various types of small base stations in heterogeneous wireless networks, for example, in 5G mobile communications, an important method to deal with wireless traffic is to deploy a large number of micro base stations in addition to traditional macro base stations, so the future network The handover scenario of the network is very different from that of the traditional homogeneous cellular network. Mobile users need to switch between various types of base stations, which not only significantly increases the complexity of the network, but also brings great challenges to network management. Previous traditional handover algorithms and handover procedures may cause handover failures, increased handover times, unnecessary handovers or ping-pong effects, and these traditional handover algorithms do not take into account the individual service requirements of terminals, so the traditional The improvement of the handover algorithm or some new handover decision algorithms is an important direction for the future network development.

近年来,基于模糊逻辑和人工智能的垂直切换算法发展迅速,这类算法中人工智能是研究计算机来模拟人的某些思维过程和智能行为(如学习、推理、思考、规划等)的学科,主要包括计算机实现智能的原理,制造类似于人脑智能的计算机,使计算机能实现更高层次的应用。模糊理论是由美国加州大学伯克利分校扎德教授在探讨人类主观过程中定量化处理的方法时所提出的,同时引出了隶属函数的概念。很多垂直切换决策算法研究都是基于这样一个条件:约束条件、决策因素等都是清晰的、可确定的。但是在现实异构网络环境中,用户对切换决策中的属性的认识通常是不确定的,如丢包率、吞吐量、和信号质量等信息。而模糊逻辑提供了使用一段特定范围内数据值的能力。切换的属性可以代表一个模糊的术语,如“高(high)”、“低(low)”、“大(big)”、“小(small)”,这避免了需要选择一个特定的值。基于模糊逻辑的切换决策算法主要包括两个部分:一是通过隶属函数处理各输入参量,二是利用模糊推理规则和解模糊函数选择合适的切换网络。与其他算法相比,基于模糊逻辑的切换决策具有较高的有效性和可靠性。In recent years, vertical switching algorithms based on fuzzy logic and artificial intelligence have developed rapidly. In this type of algorithm, artificial intelligence is a discipline that studies computers to simulate certain human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It mainly includes the principle of computer realizing intelligence, making computers similar to human brain intelligence, so that computers can realize higher-level applications. The fuzzy theory was proposed by Professor Zade of the University of California, Berkeley, when he was discussing the method of quantitative processing in the human subjective process, and at the same time led to the concept of membership function. Many studies on vertical handover decision-making algorithms are based on such a condition: constraint conditions and decision-making factors are clear and determinable. However, in the real heterogeneous network environment, the user's understanding of the attributes in the handover decision is usually uncertain, such as packet loss rate, throughput, and signal quality and other information. Whereas fuzzy logic provides the ability to use a specific range of data values. A toggled property can represent a vague term such as "high", "low", "big", "small", which avoids the need to choose a specific value. The switching decision algorithm based on fuzzy logic mainly includes two parts: one is to process each input parameter through membership function, and the other is to use fuzzy reasoning rules and defuzzification function to select a suitable switching network. Compared with other algorithms, the switching decision based on fuzzy logic has higher validity and reliability.

专利号为202011040317.X的发明中提及一种卫星通信中基于模糊逻辑的分级策略的网络切换方法,能够使控制设备切换到当前通信质量最优的波束网络,保证上行多波束卫星通信,解决了卫星通信中断在多波束交叉覆盖下准确合理地选择最优波束覆盖网络的通信链路建链的问题。然而,该发明的应用场景局限于卫星通信,对于更全面的异构网络则不能应用,也没有考虑用户终端的业务类型,无法满足终端的个性化需求。The invention with the patent No. 202011040317.X mentions a network switching method based on fuzzy logic hierarchical strategy in satellite communication, which can make the control equipment switch to the beam network with the best current communication quality, ensure uplink multi-beam satellite communication, and solve the problem of The problem of accurately and rationally selecting the communication link of the network with the optimal beam coverage under the multi-beam cross coverage under the interruption of satellite communication is solved. However, the application scenario of the invention is limited to satellite communication, and cannot be applied to a more comprehensive heterogeneous network, and does not consider the service type of the user terminal, and cannot meet the individual needs of the terminal.

专利号为202010014344.3的发明中提及一种自适应多准则模糊逻辑的5G异构网络切换决策方法,该发明只是简单地考虑了会话类业务和非会话类业务,不能适应当前业务类型复杂多变的情况;并且采用了五个模糊推理引擎,系统复杂度较高,处理时间较长。The invention with the patent number 202010014344.3 mentions an adaptive multi-criteria fuzzy logic 5G heterogeneous network handover decision-making method. This invention simply considers conversational services and non-conversational services, and cannot adapt to the complex and changeable types of current services. The situation; and five fuzzy inference engines are used, the system complexity is high, and the processing time is long.

发明内容Contents of the invention

本发明针对现有技术中的不足,提供一种面向业务类型的基于模糊逻辑的异构网络切换方法,不仅可以根据终端的应用类型合理地选择最佳切换网络,满足终端地个性化服务需求,还可以有效地降低平均切换次数。Aiming at the deficiencies in the prior art, the present invention provides a fuzzy logic-based heterogeneous network switching method oriented to business types, which can not only reasonably select the best switching network according to the application type of the terminal, but also meet the personalized service requirements of the terminal. It can also effectively reduce the average switching times.

为实现上述目的,本发明采用以下技术方案:To achieve the above object, the present invention adopts the following technical solutions:

本发明实施例提出了一种面向业务类型的基于模糊逻辑的异构网络切换方法,所述异构网络切换方法包括以下步骤:The embodiment of the present invention proposes a fuzzy logic-based heterogeneous network switching method oriented to service types, and the heterogeneous network switching method includes the following steps:

S1,终端获得当前异构网络场景下各个候选网络的参数信息,包括接收信号强度RSS、网络带宽、时延和误码率;S1, the terminal obtains parameter information of each candidate network in the current heterogeneous network scenario, including received signal strength RSS, network bandwidth, delay and bit error rate;

S2,根据各个网络的接收信号强度RSS值对候选网络进行筛选,剔除强度值低于预设强度阈值的候选网络;S2, screening candidate networks according to the received signal strength RSS value of each network, and eliminating candidate networks whose strength values are lower than a preset strength threshold;

S3,将应用业务类型分为四类,分别是会话类应用、交互类应用、流类应用和后台类应用,并针对每类应用业务类型,以网络带宽、时延和误码率为元素,分别设计会话类输入隶属度函数、交互类输入隶属度函数、流类输入隶属度函数和后台类输入隶属度函数;S3 divides application business types into four categories, namely conversational applications, interactive applications, streaming applications, and background applications. For each type of application business type, network bandwidth, delay, and bit error rate are used as elements. Design the session class input membership function, the interactive class input membership function, the flow class input membership function and the background class input membership function respectively;

S4,根据终端当前的应用业务类型选择相应的输入隶属度函数输入到模糊推理模块中进行模糊推理;S4. According to the current application service type of the terminal, select a corresponding input membership function and input it into the fuzzy reasoning module to perform fuzzy reasoning;

S5,将模糊推理模块输出的结果导入去模糊化模块进行去模糊化处理,得到模糊推理结果,根据模糊推理结果选择最佳网络进行切换。S5, import the result output by the fuzzy reasoning module into the defuzzification module for defuzzification processing, obtain the fuzzy reasoning result, and select the best network for switching according to the fuzzy reasoning result.

进一步地,步骤S1中,终端获得当前异构网络场景下各个候选网络的参数信息的过程包括以下步骤:Further, in step S1, the process for the terminal to obtain parameter information of each candidate network in the current heterogeneous network scenario includes the following steps:

终端接收到来自第i个网络的RSS表示为:The RSS received by the terminal from the i-th network is expressed as:

RSS(i)=Pi-Li lg(d(xi,yi))+u(x)RSS(i)=P i -L i lg(d(x i ,y i ))+u(x)

其中,d(xi,yi)表示UE与第i个网络之间的视距,Pi表示第i个网络的传输功率,Li表示第i个网络的路径损耗,u(x)为服从(0,σ)高斯随机分布函数;Among them, d(x i , y i ) represents the line-of-sight between UE and the i-th network, P i represents the transmission power of the i-th network, L i represents the path loss of the i-th network, and u(x) is Obey (0,σ) Gaussian random distribution function;

终端从第i个网络获得的传输速率表示为:The transmission rate obtained by the terminal from the i-th network is expressed as:

Figure BDA0003362426990000021
Figure BDA0003362426990000021

其中,Bi为第i个网络为UE所分配的带宽,σ2为加性高斯白噪声功率;Among them, B i is the bandwidth allocated by the i-th network to the UE, and σ 2 is the additive white Gaussian noise power;

时延τ表示为:The time delay τ is expressed as:

τ=dtran+dproc+dprop τ=d tran +d proc +d prop

其中,dtran是传输时延,dproc是处理时延,dprop是传播时延;Among them, d tran is the transmission delay, d proc is the processing delay, and d prop is the propagation delay;

误码率BER表示为:The bit error rate BER is expressed as:

Figure BDA0003362426990000031
Figure BDA0003362426990000031

Figure BDA0003362426990000032
Figure BDA0003362426990000032

其中,I(k)为干扰信号强度,

Figure BDA0003362426990000033
Among them, I(k) is the interference signal strength,
Figure BDA0003362426990000033

进一步地,步骤S3中,所述隶属度函数包括三角型隶属度函数和梯型隶属度函数;Further, in step S3, the membership function includes a triangular membership function and a ladder membership function;

所述三角型隶属度函数公式表示为:The triangular membership function formula is expressed as:

Figure BDA0003362426990000034
Figure BDA0003362426990000034

其中,α和γ分别为模糊集的上、下限,β为隶属度函数u(x)峰值所对应的输入参数x的取值;Among them, α and γ are the upper and lower limits of the fuzzy set respectively, and β is the value of the input parameter x corresponding to the peak value of the membership function u(x);

所述梯型隶属度函数公式表示为:The ladder membership function formula is expressed as:

Figure BDA0003362426990000035
Figure BDA0003362426990000035

其中,a和h分别为模糊集合的上、下限,b和g分别为隶属度函数u(x)峰值所对应的x取值的上、下限。Among them, a and h are the upper and lower limits of the fuzzy set respectively, b and g are the upper and lower limits of the value of x corresponding to the peak value of the membership function u(x) respectively.

进一步地,步骤S4中,根据终端当前的应用业务类型选择相应的输入隶属度函数输入到模糊推理模块中进行模糊推理的过程包括以下步骤:Further, in step S4, the process of selecting a corresponding input membership function according to the current application business type of the terminal and inputting it into the fuzzy reasoning module for fuzzy reasoning includes the following steps:

根据应用特点选定若干个网络参数,选定的网络参数包括带宽、时延和误码率,对选定的网络属性参数的取值区间进行了规范;Select several network parameters according to the application characteristics, the selected network parameters include bandwidth, delay and bit error rate, and standardize the value range of the selected network attribute parameters;

针对选定的网络参数,通过梯型和三角型隶属度函数相结合的方式设计不同应用业务类型的隶属度函数,将选定的网络参数进行模糊化处理,输出隶属度函数采用三角型隶属度函数;According to the selected network parameters, the membership function of different application business types is designed by combining the ladder type and the triangular type of membership function, and the selected network parameters are fuzzy, and the output membership function adopts the triangular type of membership function;

其中,选定的网络参数定义有3个模糊逻辑等级{L、M、H},将模糊推理后得到的输出定义有5个模糊等级{VL、L、M、H、VH},式中,VL是指Very Low,L是指Low,M是指Middle,H是指High,VH是指Very High;根据专家推理模糊规则,设计相应的规则。Among them, the selected network parameters are defined with 3 fuzzy logic levels {L, M, H}, and the output obtained after fuzzy reasoning is defined with 5 fuzzy levels {VL, L, M, H, VH}, where, VL refers to Very Low, L refers to Low, M refers to Middle, H refers to High, and VH refers to Very High; according to the fuzzy rules of expert reasoning, the corresponding rules are designed.

进一步地,步骤S5中,将模糊推理模块输出的结果导入去模糊化模块进行去模糊化处理,得到模糊推理结果的过程包括以下步骤:Further, in step S5, the result output by the fuzzy reasoning module is imported into the defuzzification module for defuzzification processing, and the process of obtaining the fuzzy reasoning result includes the following steps:

采用重心法,通过计算隶属度函数曲线与根坐标所围成面积的重心对应的横坐标得到精确值:Using the center of gravity method, the exact value is obtained by calculating the abscissa corresponding to the center of gravity of the area enclosed by the membership function curve and the root coordinates:

Figure BDA0003362426990000041
Figure BDA0003362426990000041

其中,ui表示[0,1]区间重心点的横坐标,u(ui)表示重心点对应的隶属度,n表示条件参数个数;Among them, u i represents the abscissa of the center of gravity point in the [0,1] interval, u(u i ) represents the degree of membership corresponding to the center of gravity point, and n represents the number of conditional parameters;

根据每个候选网络去模糊化后的结果得到得分值最高的网络,进行终端和网络的切换。According to the defuzzification results of each candidate network, the network with the highest score is obtained, and the terminal and network are switched.

本发明的有益效果是:The beneficial effects of the present invention are:

本发明提出了一种面向业务类型的基于模糊逻辑的异构网络切换方法,该方法解决了传统的异构网络切换方法可能会造成切换失败以及切换次数的增加,发生不必要的切换或乒乓效应等问题。本发明利用一种改进式的模糊逻辑推理方法,对于选定的QoS参数,根据不同的应用业务类型对QoS参数的具体需求范围设计了不同的隶属度函数,不仅能够满足终端的个性化需求,而且能够降低平均切换次数。The present invention proposes a fuzzy-logic-based heterogeneous network switching method oriented to business types, which solves the problem that the traditional heterogeneous network switching method may cause switching failure and increase the number of switching times, and unnecessary switching or ping-pong effects occur And other issues. The present invention uses an improved fuzzy logic reasoning method to design different membership functions for the selected QoS parameters according to the specific demand ranges of different application service types for the QoS parameters, which can not only meet the individual needs of the terminal, Furthermore, the average number of switching times can be reduced.

附图说明Description of drawings

图1是异构无线网络场景示意图。FIG. 1 is a schematic diagram of a heterogeneous wireless network scenario.

图2是本发明提供的面向业务类型的基于模糊逻辑的异构网络切换系统流程图。Fig. 2 is a flow chart of the fuzzy logic-based heterogeneous network handover system oriented to service types provided by the present invention.

图3是本发明设计的模糊推理系统框架图。Fig. 3 is a frame diagram of the fuzzy reasoning system designed by the present invention.

图4是本发明模糊推理系统中专家推理的部分模糊规则。Fig. 4 is part of fuzzy rules of expert reasoning in the fuzzy reasoning system of the present invention.

图5是带宽在各类应用中的隶属度函数示意图。Fig. 5 is a schematic diagram of membership functions of bandwidth in various applications.

图6是时延在各类应用中的隶属度函数示意图。Fig. 6 is a schematic diagram of membership function of time delay in various applications.

图7是误码率在各类应用中的隶属度函数示意图。Fig. 7 is a schematic diagram of the membership function of the bit error rate in various applications.

具体实施方式Detailed ways

现在结合附图对本发明作进一步详细的说明。The present invention is described in further detail now in conjunction with accompanying drawing.

需要注意的是,发明中所引用的如“上”、“下”、“左”、“右”、“前”、“后”等的用语,亦仅为便于叙述的明了,而非用以限定本发明可实施的范围,其相对关系的改变或调整,在无实质变更技术内容下,当亦视为本发明可实施的范畴。It should be noted that terms such as "upper", "lower", "left", "right", "front", and "rear" quoted in the invention are only for clarity of description, not for Limiting the practicable scope of the present invention, and the change or adjustment of the relative relationship shall also be regarded as the practicable scope of the present invention without substantive changes in the technical content.

基于图1所示的异构无线网络场景,本发明提出了一种面向业务类型的基于模糊逻辑的异构网络切换方法,如图2所示,具体步骤如下:Based on the heterogeneous wireless network scenario shown in Figure 1, the present invention proposes a service type-oriented fuzzy logic-based heterogeneous network switching method, as shown in Figure 2, and the specific steps are as follows:

步骤一、终端获得当前异构网络场景下各个网络的参数信息,包括接收信号强度RSS,网络带宽B,时延τ和误码率BER,其实现过程为:Step 1. The terminal obtains parameter information of each network in the current heterogeneous network scenario, including received signal strength RSS, network bandwidth B, delay τ and bit error rate BER. The implementation process is as follows:

关于接收信号强度RSS,终端接收到来自第i个网络的RSS可以表示为:Regarding the received signal strength RSS, the RSS received by the terminal from the i-th network can be expressed as:

RSS(i)=Pi-Li lg(d(xi,yi))+u(x)RSS(i)=P i -L i lg(d(x i ,y i ))+u(x)

其中d(xi,yi)表示UE与第i个网络之间的视距,Pi表示第i个网络的传输功率,Li表示第i个网络的路径损耗,u(x)为服从(0,σ)高斯随机分布函数。where d(xi , y i ) represents the line-of-sight distance between the UE and the i-th network, P i represents the transmission power of the i-th network, L i represents the path loss of the i-th network, and u(x) is subject to (0,σ) Gaussian random distribution function.

关于网络带宽B,终端从第i个网络获得的传输速率可以表示为:Regarding the network bandwidth B, the transmission rate obtained by the terminal from the i-th network can be expressed as:

Figure BDA0003362426990000051
Figure BDA0003362426990000051

其中Bi为第i个网络为UE所分配的带宽,σ2为加性高斯白噪声功率。Among them, B i is the bandwidth allocated by the i-th network to the UE, and σ 2 is the additive Gaussian white noise power.

关于时延τ,可以表示为:Regarding the time delay τ, it can be expressed as:

τ=dtran+dproc+dprop τ=d tran +d proc +d prop

其中,dtran是传输时延,dproc是处理时延,dprop是传播时延。Among them, d tran is the transmission delay, d proc is the processing delay, and d prop is the propagation delay.

关于误码率BER,BER是关于信噪比(SNR)的函数,可以表示为:Regarding the bit error rate BER, BER is a function of the signal-to-noise ratio (SNR), which can be expressed as:

Figure BDA0003362426990000052
Figure BDA0003362426990000052

Figure BDA0003362426990000053
Figure BDA0003362426990000053

其中,I(k)为干扰信号强度,

Figure BDA0003362426990000054
Among them, I(k) is the interference signal strength,
Figure BDA0003362426990000054

步骤二、根据各个网络的接收信号强度RSS值对候选网络进行筛选,其实现过程为:Step 2. Screen candidate networks according to the received signal strength RSS value of each network. The implementation process is as follows:

对于候选网络i,如果其接收信号强度RSS小于绝对门限RSSth,即网络i此时不满足A2事件,则将其从候选网络中进行删除。For the candidate network i, if its received signal strength RSS is smaller than the absolute threshold RSS th , that is, the network i does not meet the A2 event at this time, it will be deleted from the candidate network.

步骤三、根据3GPP协议标准将应用业务类型分为四类,分别是会话类应用、交互类应用、流类应用和后台类应用,并为每类应用设计了不同的隶属度函数。其实现过程为:Step 3. Divide application service types into four categories according to 3GPP protocol standards, namely conversational applications, interactive applications, stream applications and background applications, and design different membership functions for each type of application. Its implementation process is:

3GPP标准在进行应用类型的定义和划分时,为保证每类应用端到端的QoS,便根据这些应用的特点对某些网络属性参数的取值区间进行了规范,这些参数包括带宽、时延、误码率等。通常不同类型的应用对QoS参数有不同的需求,如64kbps的带宽能满足会话类应用的需求,但是完全不能满足流类应用的需求,因此,本发明通过模糊逻辑的方法为每类应用设计了不同的隶属度函数,模糊化的具体表示如下:When defining and classifying application types, the 3GPP standard regulates the value range of some network attribute parameters according to the characteristics of these applications in order to ensure the end-to-end QoS of each type of application. These parameters include bandwidth, delay, bit error rate etc. Usually different types of applications have different requirements for QoS parameters. For example, the bandwidth of 64kbps can meet the requirements of conversational applications, but cannot meet the requirements of streaming applications at all. For different membership functions, the specific expression of fuzzification is as follows:

在模糊决策中,为了处理具有模糊性的参数信息,通常需要将这些参数采用模糊集合的形式表示。模糊集合A的定义如下:In fuzzy decision-making, in order to deal with fuzzy parameter information, it is usually necessary to express these parameters in the form of fuzzy sets. The definition of fuzzy set A is as follows:

A={(x,uA(x)),x∈X}A={(x, u A (x)), x∈X}

uA(x)就是元素x属于模糊集合A的隶属度,X就是元素x的论域。隶属度函数就是这些模糊集合的定量描述。待处理的参数通过隶属度函数可以映射为区间[0,1]上的一个值,该值称为参数属于此模糊集合的隶属度。隶属度越大,表示参数属于此集合的程度越高。由于带宽,误码率和时延都可以通过隶属度函数进行模糊化。从而可以设计相应的隶属度函数。输入隶属度函数有很多种,常用的是三角型和梯型。u A (x) is the membership degree of element x belonging to fuzzy set A, and X is the discourse domain of element x. The membership function is the quantitative description of these fuzzy sets. The parameters to be processed can be mapped to a value on the interval [0,1] through the membership function, and this value is called the membership degree of the parameter belonging to this fuzzy set. The larger the degree of membership, the higher the degree that the parameter belongs to this set. Due to bandwidth, bit error rate and delay can all be fuzzy by membership function. Therefore, the corresponding membership function can be designed. There are many kinds of input membership functions, and the commonly used ones are triangular and trapezoidal.

三角型隶属度函数的隶属度计算式为:The membership calculation formula of the triangular membership function is:

Figure BDA0003362426990000061
Figure BDA0003362426990000061

其中,α和γ分别为模糊集的上、下限,β为隶属度函数u(x)峰值所对应的输入参数x的取值。Among them, α and γ are the upper and lower limits of the fuzzy set respectively, and β is the value of the input parameter x corresponding to the peak value of the membership function u(x).

梯型隶属度函数的隶属度计算式为:The membership calculation formula of the ladder membership function is:

Figure BDA0003362426990000062
Figure BDA0003362426990000062

其中,a和h分别为模糊集合的上、下限,b和g分别为隶属度函数u(x)峰值所对应的x取值的上、下限。Among them, a and h are the upper and lower limits of the fuzzy set respectively, b and g are the upper and lower limits of the value of x corresponding to the peak value of the membership function u(x) respectively.

步骤四、根据终端当前的应用业务类型选择相应的输入隶属度函数输入到模糊推理系统进行模糊推理,其实现过程为:Step 4. According to the current application business type of the terminal, select the corresponding input membership function and input it to the fuzzy reasoning system for fuzzy reasoning. The implementation process is as follows:

根据3GPP划分的应用业务类型,本发明设计了会话类输入隶属度函数、交互类输入隶属度函数、流类输入隶属度函数和后台类输入隶属度函数,根据终端当前的应用业务类型选择相应的输入隶属度函数输入到模糊推理系统进行模糊推理,模糊推理的实现过程为:According to the application service types classified by 3GPP, the present invention designs the session-type input membership function, the interaction-type input membership function, the stream-type input membership function and the background-type input membership function, and selects the corresponding one according to the current application service type of the terminal. The input membership function is input to the fuzzy reasoning system for fuzzy reasoning. The realization process of fuzzy reasoning is as follows:

根据输入隶属度和推理规则,经过模糊运算,得到输出隶属度。模糊推理模块基于模糊概念进行推理,是模糊推理系统的核心,模块中的模糊规则表示了从输入模糊集合到输出模糊集合的一种映射。模糊规则通常由“IF-THEN”条件句组成,其中“IF”部分称为规则前件,“THEN”部分称为规则后件。可表示如下:According to the input membership degree and reasoning rules, the output membership degree is obtained through fuzzy operation. The fuzzy reasoning module performs reasoning based on fuzzy concepts, and is the core of the fuzzy reasoning system. The fuzzy rules in the module represent a mapping from input fuzzy sets to output fuzzy sets. Fuzzy rules are usually composed of "IF-THEN" conditional sentences, where the "IF" part is called the antecedent of the rule, and the "THEN" part is called the postcondition of the rule. Can be expressed as follows:

IF x is A and…and y is B THEN z is CIF x is A and...and y is B THEN z is C

步骤五、经过模糊推理模块和去模糊化模块,根据模糊推理结果选择最佳网络进行切换,其实现过程为:Step 5. After the fuzzy reasoning module and the defuzzification module, select the best network for switching according to the fuzzy reasoning result. The implementation process is as follows:

对于选定的网络属性参数带宽、时延和误码率的模糊集合为{L,M,H},输出隶属度函数的模糊集合为{VL,L,M,H,VH},根据专家推理模糊规则,一共设计了27条规则。然后通过去模糊化模块将模糊推理后得到的模糊值转换为精确值,转换的方法有本发明采用了重心法,即通过计算隶属度函数曲线与很坐标所围成面积的重心对应的横坐标得到精确值,计算方法如下:For the selected network attribute parameters bandwidth, delay and bit error rate, the fuzzy set is {L, M, H}, and the fuzzy set of the output membership function is {VL, L, M, H, VH}, according to expert reasoning Fuzzy rules, a total of 27 rules are designed. Then the fuzzy value obtained after the fuzzy reasoning is converted into an accurate value by the defuzzification module. The method of conversion has adopted the center of gravity method in the present invention, that is, by calculating the abscissa corresponding to the center of gravity of the area surrounded by the membership function curve and the very coordinates To get the exact value, the calculation method is as follows:

Figure BDA0003362426990000071
Figure BDA0003362426990000071

最后根据去模糊化后的结果选择最佳网络进行切换。Finally, according to the defuzzification results, the best network is selected for switching.

为了说明本发明所提方法的有效性,下面给出一个实例。搭建的异构网络场景如图1所示,由宏基站和很多个微基站组成的异构网络,用户UE从左侧向右侧移动,根据当前终端的业务类型分为会话类、交互类、流类和后台类四类应用,利用模糊逻辑的方法选择最佳的网络进行接入,包括以下步骤:In order to illustrate the effectiveness of the proposed method of the present invention, an example is given below. The heterogeneous network scenario built is shown in Figure 1. The heterogeneous network consists of macro base stations and many micro base stations. User UEs move from left to right. According to the current service types of terminals, they are divided into conversational, interactive, Four types of streaming and background applications use fuzzy logic to select the best network for access, including the following steps:

步骤一:终端获得当前异构网络场景下各个网络的参数信息,包括接收信号强度RSS,网络带宽B,时延τ和误码率BER,然后根据各个网络的接收信号强度RSS值对候选网络进行筛选,其实现过程为:Step 1: The terminal obtains the parameter information of each network in the current heterogeneous network scenario, including received signal strength RSS, network bandwidth B, delay τ and bit error rate BER, and then evaluates the candidate network according to the received signal strength RSS value of each network Screening, the implementation process is:

对于候选网络i,如果其接收信号强度RSS小于绝对门限RSSth,即网络i此时不满足A2事件,则将其从候选网络中进行删除。For the candidate network i, if its received signal strength RSS is smaller than the absolute threshold RSS th , that is, the network i does not meet the A2 event at this time, it will be deleted from the candidate network.

步骤二:根据当前终端的业务类型设计不同的隶属度函数,比如对于会话类应用的带宽而言,保持正常通信所需要的带宽为64kbps,当带宽大于64kbps时,通信质量会随着带宽的增大而提高,而达到300kbps以上时,通信质量将不在发生明显提升。而带宽小于64kbps时,会降低会话的服务质量,而小于5kbps时,无法进行通信。所以对于会话类、交互类、流类和后台类这四类应用的带宽、时延和误码率分别设计了隶属度函数,带宽在各类应用中的隶属度函数如图5所示,时延在各类应用中的隶属度函数如图6所示,误码率在各类应用中的隶属度函数如图7所示。Step 2: Design different membership functions according to the service type of the current terminal. For example, for the bandwidth of conversational applications, the bandwidth required to maintain normal communication is 64kbps. When the bandwidth is greater than 64kbps, the communication quality will increase with the bandwidth. When it reaches 300kbps or more, the communication quality will no longer be significantly improved. When the bandwidth is less than 64kbps, the service quality of the session will be reduced, and when the bandwidth is less than 5kbps, communication cannot be performed. Therefore, membership functions are designed for the bandwidth, delay and bit error rate of the four types of applications: conversational, interactive, streaming, and background. The membership functions of bandwidth in various applications are shown in Figure 5. The membership function of delay in various applications is shown in Figure 6, and the membership function of bit error rate in various applications is shown in Figure 7.

步骤三:根据终端当前的应用业务类型选择相应的输入隶属度函数输入到模糊推理系统进行模糊推理,其实现过程为:根据输入隶属度和推理规则,经过模糊运算,得到输出隶属度。模糊推理模块基于模糊概念进行推理,是模糊推理系统的核心,模块中的模糊规则表示了从输入模糊集合到输出模糊集合的一种映射。模糊规则通常由“IF-THEN”条件句组成,其中“IF”部分称为规则前件,“THEN”部分称为规则后件。可表示如下:Step 3: Select the corresponding input membership degree function according to the current application business type of the terminal and input it to the fuzzy reasoning system for fuzzy reasoning. The realization process is: according to the input membership degree and reasoning rules, the output membership degree is obtained through fuzzy operation. The fuzzy reasoning module performs reasoning based on fuzzy concepts, and is the core of the fuzzy reasoning system. The fuzzy rules in the module represent a mapping from input fuzzy sets to output fuzzy sets. Fuzzy rules are usually composed of "IF-THEN" conditional sentences, where the "IF" part is called the antecedent of the rule, and the "THEN" part is called the postcondition of the rule. Can be expressed as follows:

IF x is A and…and y is B THEN z is CIF x is A and...and y is B THEN z is C

步骤四:经过模糊推理模块和去模糊化模块,根据模糊推理结果选择最佳网络进行切换,其实现过程为:Step 4: After the fuzzy reasoning module and the defuzzification module, select the best network for switching according to the fuzzy reasoning result, and the implementation process is as follows:

对于选定的网络属性参数带宽、时延和误码率的模糊集合为{L,M,H},输出隶属度函数的模糊集合为{VL,L,M,H,VH},根据专家推理模糊规则,一共设计了27条规则。这27条规则如图3所示,然后通过去模糊化模块将模糊推理后得到的模糊值转换为精确值,最后根据去模糊化的结果选择最佳的网络进行切换。For the selected network attribute parameters bandwidth, delay and bit error rate, the fuzzy set is {L, M, H}, and the fuzzy set of the output membership function is {VL, L, M, H, VH}, according to expert reasoning Fuzzy rules, a total of 27 rules are designed. These 27 rules are shown in Figure 3, and then the fuzzy value obtained after fuzzy reasoning is converted into an accurate value through the defuzzification module, and finally the best network is selected for switching according to the defuzzification result.

以上仅是本发明的优选实施方式,本发明的保护范围并不仅局限于上述实施例,凡属于本发明思路下的技术方案均属于本发明的保护范围。应当指出,对于本技术领域的普通技术人员来说,在不脱离本发明原理前提下的若干改进和润饰,应视为本发明的保护范围。The above are only preferred implementations of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions under the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those skilled in the art, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims (3)

1. A heterogeneous network switching method based on fuzzy logic for service types is characterized by comprising the following steps:
s1, a terminal obtains parameter information of each candidate network in a current heterogeneous network scene, wherein the parameter information comprises received signal strength RSS, network bandwidth, time delay and error rate;
s2, screening candidate networks according to the received signal strength RSS values of the networks, and eliminating candidate networks with strength values lower than a preset strength threshold;
s3, dividing application service types into four types, namely a session type application, an interaction type application, a stream type application and a background type application, and respectively designing a session type input membership function, an interaction type input membership function, a stream type input membership function and a background type input membership function by taking network bandwidth, time delay and error rate as elements for each type of application service type;
s4, selecting a corresponding input membership function according to the current application service type of the terminal, and inputting the corresponding input membership function into a fuzzy reasoning module for fuzzy reasoning;
s5, importing the result output by the fuzzy reasoning module into a defuzzification module for defuzzification processing to obtain a fuzzy reasoning result, and selecting an optimal network for switching according to the fuzzy reasoning result;
in step S4, the process of selecting a corresponding input membership function according to the current application service type of the terminal and inputting the function into the fuzzy inference module for fuzzy inference includes the following steps:
selecting a plurality of network parameters according to application characteristics, wherein the selected network parameters comprise bandwidth, time delay and error rate, and normalizing the value interval of the selected network attribute parameters;
aiming at the selected network parameters, membership functions of different application service types are designed in a mode of combining ladder type membership functions and triangle type membership functions, the selected network parameters are subjected to fuzzification processing, and the output membership functions adopt triangle type membership functions;
wherein, the selected network parameter defines 3 fuzzy logic grades { L, M, H }, and the output obtained after fuzzy reasoning is defined with 5 fuzzy grades { VL, L, M, H, VH }, wherein, VL refers to Very Low, L refers to Low, M refers to Middle, H refers to High, and VH refers to Very High; designing corresponding rules according to expert reasoning fuzzy rules;
in step S5, the process of importing the result output by the fuzzy inference module into the defuzzification module to perform defuzzification processing to obtain the fuzzy inference result includes the following steps:
the accurate value is obtained by calculating the abscissa corresponding to the gravity center of the area surrounded by the membership function curve and the root coordinate by adopting a gravity center method:
Figure FDA0004228396560000011
wherein u is i Represents [0,1 ]]The abscissa of the center of gravity point of the interval, u (u) i ) The membership degree corresponding to the gravity center point is represented, and n represents the number of condition parameters;
and obtaining the network with the highest score value according to the defuzzified result of each candidate network, and switching the terminal and the network.
2. The heterogeneous network handover method based on fuzzy logic for a service type according to claim 1, wherein in step S1, the process of obtaining parameter information of each candidate network in the current heterogeneous network scenario by the terminal includes the following steps:
the terminal receives RSS from the i-th network as:
RSS(i)=P i -L i lg(d(x i ,y i ))+u(x)
wherein d (x i ,y i ) Representing the line of sight between the UE and the ith network, P i Representing the transmission power of the ith network, L i Representing the path loss of the ith network, u (x) is a gaussian random distribution function subject to (0, σ);
the transmission rate obtained by the terminal from the i-th network is expressed as:
Figure FDA0004228396560000021
wherein B is i Bandwidth, σ, allocated to UE for ith network 2 Is additive white Gaussian noise power;
the delay τ is expressed as:
τ=d tran +d proc +d prop
wherein d tran Is the transmission delay, d proc Is the processing delay, d prop Is propagation delay;
the bit error rate BER is expressed as:
Figure FDA0004228396560000022
Figure FDA0004228396560000023
wherein I (k) is the interference signal strength,
Figure FDA0004228396560000024
3. the heterogeneous network handover method based on fuzzy logic for a service type according to claim 1, wherein in step S3, the membership function includes a triangular membership function and a trapezoidal membership function;
the triangular membership function formula is expressed as:
Figure FDA0004228396560000025
wherein alpha and gamma are the upper limit and the lower limit of the fuzzy set respectively, and beta is the value of an input parameter x corresponding to the peak value of a membership function u (x);
the ladder membership function formula is expressed as:
Figure FDA0004228396560000031
wherein a and h are the upper and lower limits of the fuzzy set, and b and g are the upper and lower limits of the x value corresponding to the peak value of the membership function u (x).
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