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| Chapters: 1-5
| Pages: 59
INTERVAL TYPE-2 FUZZY LOGIC MODEL FOR CONNECTION ADMISSION CONTROL IN 4G NETWORKS
CHAPTER ONE
GENERAL INTRODUCTION
1.0 Introduction
A Connection Admission Control (CAC) is an algorithm of decision making that provides quality of service (QoS) in the network by restricting access to the network resources (Ghaderi and Boutaba, 2006). According to the requested call type, CAC decides to accept or block the new call according to the network resources availability. When there are not sufficient resources to ensure the call’s quality or to keep the active calls’ QoS (services already accepted or established), CAC blocks the new call. Otherwise, the call is accepted. CAC is very important especially in a network whose QoS is of utmost priority. One of such network is the Fourth Generation (4G) Network.
Fourth Generation Network (4G) is the fourth generation of mobile telecommunications technology, succeeding 3G. A 4G system must provide capabilities defined by International Telecommunication Union (ITU) in Internet Mobile Telephony (IMT) Advanced. Potential and current applications include amended mobile web access, Internet Protocol (IP) telephony, gaming services, high definition mobile TV, video conferencing, 3D television, and cloud computing (Vilches, 2010).
Two 4G candidate systems are commercially deployed: the Mobile Worldwide Interoperability for Microwave Access (WiMAX) standard and the Long Term Evolution (LTE) standard. 4G network is believed to be the fastest network technology.
Several methods are used to improve the quality of service across 4G networks. These methods include – markov models, queuing models and expert system etc. but in recent years, fuzzy expert systems are widely used due to its ability to make decision based on the experience of an expert stored in a knowledge base.
A Type-1 Fuzzy Set denoted by , is characterized by a Type-1 membership function (Castillo and Melin, 2008), where , and is the domain of definition of the variable. The type-1 membership function maps each element of to a membership grade (or membership value) between 0 and 1.
Type-1 Fuzzy Logic Systems – also called Type-1 Fuzzy Inference Systems, are both, intuitive and numerical systems that map crisp inputs into a crisp output. Every type-1 fuzzy inference system is associated with a set of rules with meaningful linguistic interpretations, such as: which can be obtained either from numerical data, or from experts familiar with the problem at hand. In particular the rules are in the form of Mamdani fuzzy rules (Mamdani, 1976). Based on this kind of statements, actions are combined with rules in an antecedent/consequent format, and then aggregated according to approximate reasoning theory to produce a nonlinear mapping from input space to output space.
A type-1 fuzzy inference system consists of four basic elements, the Type-1 fuzzifier, the Type-1 fuzzy rule-base, the Type-1 inference engine, and the Type-1 defuzzifier. The Type-1 fuzzy rule-base is a collection of rules in the form of which are combined in the Type-1 inference engine, to produce a fuzzy output. The Type-1 fuzzifier maps the crisp input into a type-1 fuzzy set, which are subsequently used as inputs to the Type-1 inference engine, whereas the Type-1 defuzzifier maps the type-1 fuzzy sets produced by the Type-1 inference engine into crisp numbers. Although type-1 fuzzy controllers have achieved great success in many different real world applications, research has shown that there are limitations in the ability of type-1 fuzzy system to model and minimize the effect of uncertainties. This is because a type-1 fuzzy system is certain in the sense that its membership grades are crisp values. To solve this problem, type-2 fuzzy logic controllers were introduced.
Type-2 fuzzy systems (Zadeh, 1975), are characterized by membership functions that are themselves fuzzy. Type-2 fuzzy system provide additional design degrees of freedom in Mamdani and Takagi-Sugeno-Kang (TSK) fuzzy logic systems (FLSs), which can be very useful when such systems are used in situations where lots of uncertainties are present. Type-2 fuzzy logic systems (T2 FLS) have the potential to provide better performance than a type-1 FLS (Wu and Mendel, 2003). Because of the computational complexity of using a general type-2 fuzzy system, most people only use a special case of type-2 fuzzy system called the interval type-2 fuzzy system in a type-2 fuzzy logic system, the result being an interval T2fuzzy logic system (IT2FLS). The computations associated with interval type-2 fuzzy systems are very manageable, which makes an interval type-2 fuzzy logic system quite practical (Mendel, 2001).
In this project work, an interval type-2 fuzzy logic model for connection admission control in 4G network is proposed. It is a type of fuzzy logic controller that incorporates the experience of human experts in making appropriate decisions to control traffic and congestion. Decision is made based on the information in the traffic contract and the condition of the network.
This system will be implemented in Matlab and the java programming language.
1.1 Statement of Problem
In 1G networks and 2G networks such as GSM and CDMA there was only one aspect of QoS (Quality of Service) and it is voice, yet providing quality speech was the major concern. Things get even more complicated when data and voice service has to be supported (the case of 4G network). Voice services are very delay sensitive and require real – time service. On the other hand data services are less delay sensitive but are very sensitive to loss of data and also they expect error free packets. So both of these factors have to be considered for providing QoS for voice and data services. Deterioration of network quality of service is the current issue in 4G networks in Nigeria. Some problems of connection admission control come from the model used. Such as those that use a type-1 fuzzy logic model. There are limitations in the ability of type-1 fuzzy model to handle uncertainties and this makes it inappropriate when used to solve problems with high degree of uncertainty. Also, the interference among users resulting in serious challenges in the design to 4G network users.
1.2 Motivation
This research work is motivated by the ability of T2FLS to handle uncertainties and the ability of IT2FLS to simplify T2FLS in order to handle the problem of poor network quality of service in Nigeria which is as a result of the scarcity of available resources, such as limited bandwidth and low capacity. Computations using general T2FSs are very costly but computations using 1T2FSs involve only interval arithmetic.
1.3 Aim and Objectives
The aim of this research is to develop an interval type-2 fuzzy logic model for connection admission control in 4G network. The Objectives are to;
1.4 Methodology
The methods employed in this research work are;
1.5 Scope and Limitation
This research work develops an interval type-2 fuzzy logic model for the connection admission control in 4G network. Although other network architectures will be reviewed, this model will not support those network architectures as our focus will be on 4G network. The computational complexity of an interval type-2 fuzzy logic model compared to type-1 fuzzy logic model is a limitation, since it is required that a high-end computing device be used in order to run this system efficiently. Another limitation of this system comes from its standalone nature. In order to use this system, a CD containing the CAC application should be obtained from the developer and installed on a computing device. This means that the system will not be accessed over the internet.
1.6 Significance
The system will guarantee an uninterruptable service for admitted calls as they move from one cell to another and maximize the network resource utilization by reserving resources only when needed and within the expected residence time interval. Also, the system will ensure that Quality of Service is maintained by deciding whether a connection should be accepted or rejected thereby improving customer’s experience.
1.7 Project Organization
Chapter one takes care of the introduction and overview of the entire project work. Chapter two deals with the review of literature about networks, 4G network and fuzzy logic controllers.
Chapter three presents the analysis and design of the new system. Chapter four is concerned with the implementation and evaluation of the designed system. Chapter five contains the conclusion and recommendation of the research project.
1.8 Definition of Terms
Data: This is a fore runner of information that is translated to a particular subject or purpose.
Database: Is a collection of information that is organized so that it can be easily accessed, managed, and updated.
Deffuzification: This is the process of producing a quantifiable result in fuzzy set and corresponding membership degree
Expert System: This is a computer program that emulates the decision making ability of humans and provides expert advice on area of narrow human expert.
Fuzzification: This comprises the process of transforming crisp value into grades of membership for linguistic terms of fuzzy set.
Inference Engine: This is used in reasoning about information in the knowledge base for ultimate purpose of formulating new conclusions.
Knowledge Base: This is an information repository that provides a mean for information to be collected, organized, shared, searched, and utilized from the database through the inference engine.
Latency: refers to the time it takes to transmit packets over a communication channel. Load: refers to the amount of data (traffic) being carried by the network
Network: a group of two or more computer systems linked together.
Packet Loss: packet loss refers to the percentage of transmitted packets that fail to reach the intended receiver.
Signal Strength: refers to the magnitude of the electric field at a reference point that is at a significant distance from the transmitting antenna.
CHAPTER ONE
GENERAL INTRODUCTION
1.0 Introduction
A Connection Admission Control (CAC) is an algorithm of decision making that provides quality of service (QoS) in the network by restricting access to the network resources (Ghaderi and Boutaba, 2006). According to the requested call type, CAC decides to accept or block the new call according to the network resources availability. When there are not sufficient resources to ensure the call’s quality or to keep the active calls’ QoS (services already accepted or established), CAC blocks the new call. Otherwise, the call is accepted. CAC is very important especially in a network whose QoS is of utmost priority. One of such network is the Fourth Generation (4G) Network.
Fourth Generation Network (4G) is the fourth generation of mobile telecommunications technology, succeeding 3G. A 4G system must provide capabilities defined by International Telecommunication Union (ITU) in Internet Mobile Telephony (IMT) Advanced. Potential and current applications include amended mobile web access, Internet Protocol (IP) telephony, gaming services, high definition mobile TV, video conferencing, 3D television, and cloud computing (Vilches, 2010).
Two 4G candidate systems are commercially deployed: the Mobile Worldwide Interoperability for Microwave Access (WiMAX) standard and the Long Term Evolution (LTE) standard. 4G network is believed to be the fastest network technology.
Several methods are used to improve the quality of service across 4G networks. These methods include – markov models, queuing models and expert system etc. but in recent years, fuzzy expert systems are widely used due to its ability to make decision based on the experience of an expert stored in a knowledge base.
A Type-1 Fuzzy Set denoted by , is characterized by a Type-1 membership function (Castillo and Melin, 2008), where , and is the domain of definition of the variable. The type-1 membership function maps each element of to a membership grade (or membership value) between 0 and 1.
Type-1 Fuzzy Logic Systems – also called Type-1 Fuzzy Inference Systems, are both, intuitive and numerical systems that map crisp inputs into a crisp output. Every type-1 fuzzy inference system is associated with a set of rules with meaningful linguistic interpretations, such as: which can be obtained either from numerical data, or from experts familiar with the problem at hand. In particular the rules are in the form of Mamdani fuzzy rules (Mamdani, 1976). Based on this kind of statements, actions are combined with rules in an antecedent/consequent format, and then aggregated according to approximate reasoning theory to produce a nonlinear mapping from input space to output space.
A type-1 fuzzy inference system consists of four basic elements, the Type-1 fuzzifier, the Type-1 fuzzy rule-base, the Type-1 inference engine, and the Type-1 defuzzifier. The Type-1 fuzzy rule-base is a collection of rules in the form of which are combined in the Type-1 inference engine, to produce a fuzzy output. The Type-1 fuzzifier maps the crisp input into a type-1 fuzzy set, which are subsequently used as inputs to the Type-1 inference engine, whereas the Type-1 defuzzifier maps the type-1 fuzzy sets produced by the Type-1 inference engine into crisp numbers. Although type-1 fuzzy controllers have achieved great success in many different real world applications, research has shown that there are limitations in the ability of type-1 fuzzy system to model and minimize the effect of uncertainties. This is because a type-1 fuzzy system is certain in the sense that its membership grades are crisp values. To solve this problem, type-2 fuzzy logic controllers were introduced.
Type-2 fuzzy systems (Zadeh, 1975), are characterized by membership functions that are themselves fuzzy. Type-2 fuzzy system provide additional design degrees of freedom in Mamdani and Takagi-Sugeno-Kang (TSK) fuzzy logic systems (FLSs), which can be very useful when such systems are used in situations where lots of uncertainties are present. Type-2 fuzzy logic systems (T2 FLS) have the potential to provide better performance than a type-1 FLS (Wu and Mendel, 2003). Because of the computational complexity of using a general type-2 fuzzy system, most people only use a special case of type-2 fuzzy system called the interval type-2 fuzzy system in a type-2 fuzzy logic system, the result being an interval T2fuzzy logic system (IT2FLS). The computations associated with interval type-2 fuzzy systems are very manageable, which makes an interval type-2 fuzzy logic system quite practical (Mendel, 2001).
In this project work, an interval type-2 fuzzy logic model for connection admission control in 4G network is proposed. It is a type of fuzzy logic controller that incorporates the experience of human experts in making appropriate decisions to control traffic and congestion. Decision is made based on the information in the traffic contract and the condition of the network.
This system will be implemented in Matlab and the java programming language.
1.1 Statement of Problem
In 1G networks and 2G networks such as GSM and CDMA there was only one aspect of QoS (Quality of Service) and it is voice, yet providing quality speech was the major concern. Things get even more complicated when data and voice service has to be supported (the case of 4G network). Voice services are very delay sensitive and require real – time service. On the other hand data services are less delay sensitive but are very sensitive to loss of data and also they expect error free packets. So both of these factors have to be considered for providing QoS for voice and data services. Deterioration of network quality of service is the current issue in 4G networks in Nigeria. Some problems of connection admission control come from the model used. Such as those that use a type-1 fuzzy logic model. There are limitations in the ability of type-1 fuzzy model to handle uncertainties and this makes it inappropriate when used to solve problems with high degree of uncertainty. Also, the interference among users resulting in serious challenges in the design to 4G network users.
1.2 Motivation
This research work is motivated by the ability of T2FLS to handle uncertainties and the ability of IT2FLS to simplify T2FLS in order to handle the problem of poor network quality of service in Nigeria which is as a result of the scarcity of available resources, such as limited bandwidth and low capacity. Computations using general T2FSs are very costly but computations using 1T2FSs involve only interval arithmetic.
1.3 Aim and Objectives
The aim of this research is to develop an interval type-2 fuzzy logic model for connection admission control in 4G network. The Objectives are to;
1.4 Methodology
The methods employed in this research work are;
1.5 Scope and Limitation
This research work develops an interval type-2 fuzzy logic model for the connection admission control in 4G network. Although other network architectures will be reviewed, this model will not support those network architectures as our focus will be on 4G network. The computational complexity of an interval type-2 fuzzy logic model compared to type-1 fuzzy logic model is a limitation, since it is required that a high-end computing device be used in order to run this system efficiently. Another limitation of this system comes from its standalone nature. In order to use this system, a CD containing the CAC application should be obtained from the developer and installed on a computing device. This means that the system will not be accessed over the internet.
1.6 Significance
The system will guarantee an uninterruptable service for admitted calls as they move from one cell to another and maximize the network resource utilization by reserving resources only when needed and within the expected residence time interval. Also, the system will ensure that Quality of Service is maintained by deciding whether a connection should be accepted or rejected thereby improving customer’s experience.
1.7 Project Organization
Chapter one takes care of the introduction and overview of the entire project work. Chapter two deals with the review of literature about networks, 4G network and fuzzy logic controllers.
Chapter three presents the analysis and design of the new system. Chapter four is concerned with the implementation and evaluation of the designed system. Chapter five contains the conclusion and recommendation of the research project.
1.8 Definition of Terms
Data: This is a fore runner of information that is translated to a particular subject or purpose.
Database: Is a collection of information that is organized so that it can be easily accessed, managed, and updated.
Deffuzification: This is the process of producing a quantifiable result in fuzzy set and corresponding membership degree
Expert System: This is a computer program that emulates the decision making ability of humans and provides expert advice on area of narrow human expert.
Fuzzification: This comprises the process of transforming crisp value into grades of membership for linguistic terms of fuzzy set.
Inference Engine: This is used in reasoning about information in the knowledge base for ultimate purpose of formulating new conclusions.
Knowledge Base: This is an information repository that provides a mean for information to be collected, organized, shared, searched, and utilized from the database through the inference engine.
Latency: refers to the time it takes to transmit packets over a communication channel. Load: refers to the amount of data (traffic) being carried by the network
Network: a group of two or more computer systems linked together.
Packet Loss: packet loss refers to the percentage of transmitted packets that fail to reach the intended receiver.
Signal Strength: refers to the magnitude of the electric field at a reference point that is at a significant distance from the transmitting antenna.
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