INTERVAL TYPE-2 FUZZY LOGIC MODEL FOR CONNECTION ADMISSION CONTROL IN 4G NETWORKS

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. Develop a prototype of a computer aided system for CAC in 4G network.
2. Design a database model for CAC in 4G network using MySQL.
3. Design a T2FL model for CAC in 4G network.
4. Design an IT2FL model for CAC in 4G network.
5. Develop IT2FLS for CAC in 4G network.

1.4 Methodology

The methods employed in this research work are;

1. Review of relevant literatures on 4G network and Type-2 fuzzy logic controller
2. Data collection from a network operator switching office.
3. The UML Object oriented software design methodology is employed in the design of the database
4. Matlab 7.0 is used for CAC membership functions generation
5. MySQL database management system is used for CAC knowledge base design
6. Netbeans IDE 8.0  is employed in the design of the graphical user interface
7. Implementation of the CAC model using the Java programming language

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