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