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

The invention discloses a heterogeneous network switching method based on fuzzy logic for service types, which comprises the following steps: the terminal obtains parameter information of each candidate network in the current heterogeneous network scene; 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; 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; and selecting the optimal network to switch according to the fuzzy reasoning result. The invention not only can reasonably select the optimal switching network according to the application type of the terminal, thereby meeting the personalized service requirement of the terminal, but also can effectively reduce the average switching times.

Description

Heterogeneous network switching method based on fuzzy logic and oriented to service type
Technical Field
The invention relates to the technical field of communication networks, in particular to a heterogeneous network switching method based on fuzzy logic and oriented to service types.
Background
With the rapid development of wireless communication technology, the demands for mass terminal devices and emerging services have increased. The explosive increase in the number of users in a network has led to an explosive increase in network traffic, mobile users requiring higher transmission rates, and competition for limited network resources by users becoming increasingly severe. Future network scenes face challenges such as mass Internet of things, high flow, large bandwidth, high reliability, low time delay and the like. Therefore, the development trend of the future network is necessarily the fusion of wireless networks with different characteristics, such as heterogeneous wireless networks formed by fusion of different networks of UMTS, LTE, WIMAX, WLAN, 5G and the like, and through the fusion of the heterogeneous wireless networks, each network can exert the advantages of the network to the greatest extent, thereby meeting the demands of mobile users on the network.
However, since a plurality of types of small base stations are introduced into the heterogeneous wireless network, for example, an important method for dealing with wireless traffic in 5G mobile communication is to deploy a large number of micro base stations in addition to the conventional macro base stations, the handover scenario of the future network is very different from that of the conventional homogeneous cellular network. Mobile users need to switch between various base stations of different types, so that the complexity of the network is obviously improved, and great test is brought to network management. The conventional switching algorithms and switching processes may cause switching failure and increase of switching times, and unnecessary switching or ping-pong effect may occur, and the conventional switching algorithms do not consider personalized service requirements of the terminal, so improvement is provided for the conventional switching algorithms or some new switching decision algorithms are provided for an important direction of future network development.
In recent years, vertical switching algorithms based on fuzzy logic and artificial intelligence are rapidly developed, and the artificial intelligence in the algorithms is a subject for researching a computer to simulate certain thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning and the like) of a person, and mainly comprises a principle of realizing intelligence by the computer, so that the computer similar to the intelligence of a human brain is manufactured, and can realize higher-level application. Fuzzy theory was developed by university of california, berkeley, inc. Professor us when discussing the method of quantification in human subjective processes, while the concept of membership functions was introduced. Many vertical handover decision algorithm studies are based on the condition: constraints, decision factors, etc. are clear and determinable. In a realistic heterogeneous network environment, however, the knowledge of the attributes in the handover decisions by the user is often uncertain, such as packet loss rate, throughput, and signal quality information. Whereas fuzzy logic provides the ability to use a range of data values within a specific range. The switched property may represent a ambiguous term such as "high", "low", "big", "small", which avoids the need to select a specific value. The switching decision algorithm based on the fuzzy logic mainly comprises two parts: firstly, processing each input parameter through a membership function, and secondly, selecting a proper switching network by utilizing a fuzzy reasoning rule and a fuzzy function. The fuzzy logic based switching decisions have higher effectiveness and reliability than other algorithms.
The invention with the patent number of 20201102317. X refers to a network switching method based on a fuzzy logic grading strategy in satellite communication, which can enable control equipment to switch to a beam network with optimal current communication quality, ensure uplink multi-beam satellite communication, and solve the problem that satellite communication is interrupted, and a communication link of an optimal beam coverage network is accurately and reasonably selected under multi-beam cross coverage to build a link. However, the application scenario of the invention is limited to satellite communication, and the invention cannot be applied to more comprehensive heterogeneous networks, and the service type of the user terminal is not considered, so that the personalized requirement of the terminal cannot be met.
The invention of 202010014344.3 refers to a 5G heterogeneous network switching decision method of self-adaptive multi-criterion fuzzy logic, which simply considers session service and non-session service and cannot adapt to the complex and changeable current service types; and five fuzzy inference engines are adopted, so that the system complexity is high and the processing time is long.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the heterogeneous network switching method based on the fuzzy logic for the service type, which not only can reasonably select the optimal switching network according to the application type of the terminal, meets the personalized service requirement of the terminal, but also can effectively reduce the average switching times.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the embodiment of the invention provides a heterogeneous network switching method based on fuzzy logic for service types, which comprises 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;
and 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.
Further, 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 BDA0003362426990000021
wherein B is i Bandwidth, σ, allocated to UE for ith network 2 Is additive Gaussian white 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 BDA0003362426990000031
Figure BDA0003362426990000032
wherein I (k) is the interference signal strength,
Figure BDA0003362426990000033
further, 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 BDA0003362426990000034
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 BDA0003362426990000035
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).
Further, 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 input membership 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; and designing corresponding rules according to expert reasoning fuzzy rules.
Further, 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 BDA0003362426990000041
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.
The beneficial effects of the invention are as follows:
the invention provides a service type-oriented heterogeneous network switching method based on fuzzy logic, which solves the problems that the traditional heterogeneous network switching method possibly causes switching failure and increase of switching times, unnecessary switching or ping-pong effect occurs and the like. The invention designs different membership functions for the selected QoS parameters according to the specific requirement range of different application service types for the QoS parameters by using an improved fuzzy logic reasoning method, thereby not only meeting the personalized requirements of the terminal, but also reducing the average switching times.
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Fig. 1 is a schematic diagram of a heterogeneous wireless network scenario.
Fig. 2 is a flow chart of a heterogeneous network switching system based on fuzzy logic and oriented to service types.
Fig. 3 is a block diagram of a fuzzy inference system designed according to this invention.
Fig. 4 is a partial fuzzy rule of a expert reasoning in the fuzzy reasoning system of the present invention.
Fig. 5 is a graphical representation of membership functions for bandwidths in various applications.
Fig. 6 is a graphical illustration of membership functions of a time delay in various applications.
Fig. 7 is a graphical illustration of membership functions for bit error rates in various applications.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings.
It should be noted that the terms like "upper", "lower", "left", "right", "front", "rear", and the like are also used for descriptive purposes only and are not intended to limit the scope of the invention in which the invention may be practiced, but rather the relative relationship of the terms may be altered or modified without materially altering the teachings of the invention.
Based on the heterogeneous wireless network scene shown in fig. 1, the invention provides a heterogeneous network switching method based on fuzzy logic for service types, which comprises the following specific steps as shown in fig. 2:
the method comprises the steps that firstly, a terminal obtains parameter information of each network in a current heterogeneous network scene, wherein the parameter information comprises received signal strength RSS, network bandwidth B, time delay tau and bit error rate BER, and the implementation process is as follows:
regarding the received signal strength RSS, the terminal receiving RSS from the i-th network can be expressed 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 obeying (0, σ).
Regarding the network bandwidth B, the transmission rate obtained by the terminal from the i-th network can be expressed as:
Figure BDA0003362426990000051
wherein B is i Bandwidth, σ, allocated to UE for ith network 2 Is an additive white gaussian noise power.
With respect to the delay τ, it can be 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.
Regarding bit error rate BER, BER is a function of signal-to-noise ratio (SNR) and can be expressed as:
Figure BDA0003362426990000052
Figure BDA0003362426990000053
wherein I (k) is the interference signal strength,
Figure BDA0003362426990000054
screening candidate networks according to the received signal strength RSS values of the networks, wherein the implementation process is as follows:
for candidate network i, if its received signal strength RSS is less than the absolute threshold RSS th I.e. network i does not satisfy the A2 event at this time, it is deleted from the candidate networks.
Dividing application service types into four types according to a 3GPP protocol standard, namely a session type application, an interaction type application, a stream type application and a background type application, and designing different membership functions for each type of application. The implementation process is as follows:
when the 3GPP standard defines and divides the application types, in order to ensure the QoS from end to end of each application, the value interval of some network attribute parameters are normalized according to the characteristics of the applications, and the parameters include bandwidth, time delay, bit error rate and the like. In general, different types of applications have different requirements on 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, so the invention designs different membership functions for each type of applications by a fuzzy logic method, and the specific representation of the fuzzy is as follows:
in fuzzy decisions, to process parameter information with ambiguity, it is often necessary to represent these parameters in the form of fuzzy sets. The fuzzy set a is defined as follows:
A＝{(x,u A (x)),x∈X}
u A (x) The element X belongs to the membership degree of the fuzzy set A, and the X is the domain of the element X. The membership function is a quantitative description of these fuzzy sets. The parameters to be processed can be mapped into intervals [0,1 ] by membership functions]And a value referred to as the membership of the parameter to the fuzzy set. The greater the degree of membership, the greater the degree to which the parameter belongs to the set. Due to bandwidth, both bit error rate and delay can be obfuscated by membership functions. So that a corresponding membership function can be designed. There are many kinds of input membership functions, triangular and trapezoidal are commonly used.
The membership calculation formula of the triangular membership function is as follows:
Figure BDA0003362426990000061
wherein, alpha and gamma are the upper and lower limits of the fuzzy set, and beta is the value of the input parameter x corresponding to the peak value of the membership function u (x).
The membership calculation formula of the ladder-type membership function is as follows:
Figure BDA0003362426990000062
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).
Step four, 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 system for fuzzy reasoning, wherein the implementation process is as follows:
according to the application service types divided by 3GPP, the invention designs a session class input membership function, an interaction class input membership function, a stream class input membership function and a background class input membership function, and selects the corresponding input membership function according to the current application service type of the terminal to input to a fuzzy reasoning system for fuzzy reasoning, wherein the implementation process of the fuzzy reasoning is as follows:
and obtaining the output membership degree through fuzzy operation according to the input membership degree and the reasoning rule. The fuzzy inference module is used for reasoning based on the fuzzy concept and is the core of the fuzzy inference system, and the fuzzy rule in the module represents a mapping from an input fuzzy set to an output fuzzy set. Fuzzy rules are typically composed of an "IF-THEN" conditional sentence, where the "IF" portion is referred to as the rule front and the "THEN" portion is referred to as the rule back. The method can be expressed as follows:
IF x is A and…and y is B THEN z is C
step five, selecting an optimal network to switch according to a fuzzy reasoning result through a fuzzy reasoning module and a defuzzification module, wherein the implementation process is as follows:
for the fuzzy set of the selected network attribute parameter bandwidth, time delay and error rate is { L, M, H }, the fuzzy set of the output membership function is { VL, L, M, H, VH }, and 27 rules are designed in total according to expert reasoning fuzzy rules. Then, the fuzzy value obtained after fuzzy reasoning is converted into an accurate value through a defuzzification module, the gravity center method is adopted in the conversion method, namely, the accurate value is obtained through calculating the abscissa corresponding to the gravity center of the area surrounded by the membership function curve and the very coordinates, and the calculation method is as follows:
Figure BDA0003362426990000071
and finally, selecting the optimal network for switching according to the defuzzified result.
To illustrate the effectiveness of the method of the present invention, an example is given below. The built heterogeneous network scene is shown in fig. 1, a heterogeneous network is composed of macro base stations and a plurality of micro base stations, user UE moves from left to right, the heterogeneous network is divided into four types of applications of session type, interaction type, stream type and background type according to the service type of the current terminal, and the best network is selected for access by using a fuzzy logic method, and the method comprises the following steps:
step one: the terminal obtains parameter information of each network in the current heterogeneous network scene, wherein the parameter information comprises received signal strength RSS, network bandwidth B, time delay tau and bit error rate BER, and then screens candidate networks according to the received signal strength RSS values of each network, and the implementation process is as follows:
for candidate network i, if its received signal strength RSS is less than the absolute threshold RSS th I.e. network i does not satisfy the A2 event at this time, it is deleted from the candidate networks.
Step two: different membership functions are designed according to the service type of the current terminal, for example, for the bandwidth of the session application, the bandwidth required for maintaining normal communication is 64kbps, when the bandwidth is larger than 64kbps, the communication quality can be improved along with the increase of the bandwidth, and when the bandwidth reaches more than 300kbps, the communication quality is not improved obviously. While bandwidths less than 64kbps reduce the quality of service for the session, and less than 5kbps do not allow communication. Therefore, membership functions are respectively designed for bandwidths, time delays and bit error rates of the four types of applications, namely the session type, the interaction type, the stream type and the background type, the membership functions of the bandwidths in the various types of applications are shown in fig. 5, the membership functions of the time delays in the various types of applications are shown in fig. 6, and the membership functions of the bit error rates in the various types of applications are shown in fig. 7.
Step three: the corresponding input membership function is selected according to the current application service type of the terminal and is input into a fuzzy reasoning system for fuzzy reasoning, and the implementation process is as follows: and obtaining the output membership degree through fuzzy operation according to the input membership degree and the reasoning rule. The fuzzy inference module is used for reasoning based on the fuzzy concept and is the core of the fuzzy inference system, and the fuzzy rule in the module represents a mapping from an input fuzzy set to an output fuzzy set. Fuzzy rules are typically composed of an "IF-THEN" conditional sentence, where the "IF" portion is referred to as the rule front and the "THEN" portion is referred to as the rule back. The method can be expressed as follows:
IF x is A and…and y is B THEN z is C
step four: the method comprises the steps of selecting an optimal network to switch according to a fuzzy reasoning result through a fuzzy reasoning module and a defuzzification module, wherein the implementation process is as follows:
for the fuzzy set of the selected network attribute parameter bandwidth, time delay and error rate is { L, M, H }, the fuzzy set of the output membership function is { VL, L, M, H, VH }, and 27 rules are designed in total according to expert reasoning fuzzy rules. The 27 rules are shown in fig. 3, then the fuzzy value obtained after fuzzy reasoning is converted into an accurate value through a defuzzification module, and finally the optimal network is selected for switching according to the defuzzification result.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.

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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