ABSTRACT
This work presents a dynamic time threshold based scheme for voice calls in cellular networks In order to carry out effective response of the dynamic time threshold scheme on cellular network for improving the QoS, Globacom Mobile network in Abuja, Nigeria was used for the experimentation. Abuja is located at longitude 7.491302°E, latitude 9.072264°N in Nigeria. The base station used for this study has a cell site id: ABJ 002NC. The frequency of the network is 805.24MHz and the base station has a transmitting power of 45.1dBm and the base station is located at longitude 7.4037°E, latitude 9.0678°N in Abuja. The measurements carried out from the investigative base station were the performance indicators which include Traffic Channel Congestion Ratio, Call Drop Ratio and Call Setup Success Ratio. The sample measurements were generated from the BTSs which also provided the File Transfer Protocol (FTP) services to the Network Management System (NMS). The NMS which is known as the iManager M2000 was used to pull CSSR, CDR and TCH Congestion Ratio measurements for the period of the study. The NMS is hosted on a central server which is connected to other network elements such as BSC, BTS, MSC, HLR etc. The server with its NMS software was configured to retrieve BTS measurements of remote MSCs and its BSCs. The collated data from the Manager server were sorted and analyzed by the codes written in Microsoft Visual Basic 2010 Express Edition programming language for this analysis. The results obtained showed that busy hour TCH Congestion Ratio was 1.5993% which is within the expected threshold value of ≤ 2%. Also the busy hour CDR was 1.5425% and again is within the expected threshold value of ≤ 2%. Finally the Busy hour CSSR was 98.8507% which is within the expected threshold set by NCC. There was 2% improvement of traffic channel congestion, call drop ratio and call setup success ratio when dynamic time threshold scheme was applied. Efforts should be made to prohibit all forms of promos in order to improve the quality of service delivery.
TABLE OF CONTENTS
Title Page i
Declaration ii
Certification iii
Dedication iv
Acknowledgements v
Table of Contents vi
List of Tables ix
List of Figures xi
Abstract x
CHAPTER 1: INTRODUCTION
1.1 Background of Study 1
1.2 Problem Statement 4
1.3 Aim and Objectives of the Study 5
1.4 Scope of the Study 5
1.5 Significance of the Study 5
1.6 Organisation of Chapters 6
CHAPTER 2: LITERATURE REVIEW
2.1 Historical Background 7
2.1.1 First generation networks (1G) 7
2.1.2 Second generation networks (2G) 9
2.1.3 Third generation networks (3G) 10
2.1.4 Fourth generation networks (4G) 11
2.2 Quality of Service 12
2.2.1 Quality of service challenges 12
2.2.2 Individual QoS for voice services 13
2.2.3 Purpose of measuring iQoS 14
2.2.4 Concept of iQoS rating 14
2.3 Parameters of Quality of Service 15
2.4 Dynamic Time Threshold Scheme 20
2.5 Other Threshold Schemes 20
2.6 Reasons for Using Dynamic Time Threshold Scheme 28
2.7 Authors Work and Their Limitation 29
2.8 Research Gap 38
CHAPTER 3: MATERIALS AND METHODS
3.1 Materials 39
3.2 Methods 42
3.2.1 The block diagram of dynamic time threshold scheme 42
3.2.2 Flow chart of dynamic time threshold scheme 43
3.3 Research Design 44
3.4 Method of Data Collection 45
3.5 Busy Hour Traffic 67
3.6 Data Flow Diagram and Entire Software Architecture 68
3.7Software Parts 71
CHAPTER 4: RESULT AND DISCUSSION
4.1 Results 72
4.2Chi-Square Testing Of Data Samples 87
4.3 Discussion of Results 88
CHAPTER 5: CONCLUSION AND RECOMMENDATIONS
5.1 Conclusion 95
5.2 Recommendations 95
References 97
Appendices 102
LIST OF TABLES
3.1 Traffic channel congestion ratio data from August 1 - August 7, 2017 46
3.2 Traffic channel congestion ratio data fromSeptember 8 – September 14, 2017 47
3.3 Traffic channel congestion ratio data from October 15 – October 21, 2017 48
3.4 Traffic Channel Congestion Ratio data from November 22 – November 28, 2017 49
3.5 Traffic channel congestion ratio data from December 9–December 15, 2017 50
3.6 Traffic channel congestion ratio data from January 25 – January 31, 2018 51
3.7 Call drop ratio data from August 1 – August 7, 2017 52
3.8 Call drop ratio data from September 8 – September 14, 2017 53
3.9 Call drop ratio data from October 15 – October 21, 2017 54
3.10 Call drop ratio data from November 22 – November 28, 2017 55
3.11 Call drop ratio data from December 9 – December 15, 2017 56
3.12 Call drop ratio data from January 25 – January 31, 2018 57
3.13 Call setup success ratio data from August 1 – August 7, 2017 58
3.14 Call setup success ratio data from September 8 – September 14, 2017 59
3.15 Call setup success ratio data from October 15 – October 21, 2017 60
3.16 Call setup success ratio data from November 22 – November 28, 2017 61
3.17 Call setup success ratio data from December 9 – December 15, 2017 62
3.18 Call setup success ratio data from January 25 – January 31 63
3.19 Average traffic channel congestion ratio for six months 64
3.20 Average call drop ratio for six months 65
3.21 Average call setup success ratio for six months 66
3.22 Summary of KPI averages 67
3.23 Possible scenarios given three input parameters 71
4.1 Summary of improved KPI data 72
4.2 Chi-square test statistics 87
LIST OF FIGURES
2.1 Wireless communication networks 21
2.2 Cell cluster model to illustrate distributed channel allocation 22
2.3 Flow chart for CAC algorithm 24
2.4 Road topology information for mobility prediction 25
2.5 Behaviour of two flows 27
3.1 NMS server 39
3.2 Base station controller (BSC) 40
3.3 Mobile switching centre (MSC) 40
3.4 Base transceiver station (BTS) 41
3.5 Block diagram showing interconnectivity of equipments 41
3.6 Block diagram of dynamic time threshold scheme 42
3.7 Flow chart of dynamic time threshold scheme 43
3.8 Data flow diagram and entire software architecture 69
4.1 The graph of average traffic channel congestion ratio
for the month of August 2017 73
4.2 The graph of average traffic channel congestion ratio
for the month of September 2017 73
4.3 The graph of average traffic channel congestion ratio
for the month of October 2017 74
4.4 The graph of average traffic channel congestion ratio
for the month of November 2017 75
4.5 The graph of average traffic channel congestion ratio
for the month of December 2017 75
4.6 The graph of average traffic channel congestion ratio
for the month of January 2018 76
4.7 The graph of average call drop ratio for the month of August 2017 77
4.8 The graph of average call drop ratio for the month of September 2017 77
4.9 The graph of average call drop ratio for the month of October 2017 78
4.10 The graph of average call drop ratio for the month of November 2017 78
4.11 The graph of average call drop ratio for the month of December 2017 79
4.12 The graph of average call drop ratio for the month of January 2018 79
4.13 The graph of average call setup success ratio for the month of August 2017 80
4.14 The graph of average call setup success ratio for the month of September 2017 80
4.15 The graph of average call setup success ratio for the month of October 2017 81
4.16 The graph of average call setup success ratio for the month of November 2017 81
4.17 The graph of average call setup success ratio for the month of December 2017 82
4.18 The graph of average call setup success ratio for the month of January 2018 82
4.19 The graph of overall average for traffic channel congestion ratio 83
4.20 The graph of overall average for call drop ratio 83
4.21 The graph of overall average for call setup success ratio 84
4.22 Simulation of traffic channel congestion ratio 84
4.23 Simulation of call drop ratio 85
4.24 Simulation of call setup success ratio 85
4.25 Comparison of improved and un-improved traffic channel congestion ratio 86
4.26 Comparison of improved and un-improved call drop ratio 86
4.27 Comparison of improved and un-improved call setup success ratio 87
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND OF STUDY
Over the past years, wireless communication has moved from what is mobile phone service to an all-in-one audio/video/data service. Such evolution is driven by not only new hardware/software development but also the increasing dependence of human's daily life on wireless communication. For example, people expect to use smart phones for all personal communication needs, to maintain ubiquitous connections to corporate/enterprise networks at work, or to establish a wireless entertainment network at home. To satisfy these diverse demands for wireless communication, the next-generation wireless network has to provide users/applications certain Quality-of-Service (QoS) (Singh et al.,2012).
Among these, supporting QoS in acellular network is more difficult than in its wired counterparts. First, the radio is a very limited and precious resource. Although new modulation, coding or medium access techniques permit efficient usage of the radio resource, these improvements cannot be sustained with the explosive growth of bandwidth demanding applications. Second, users of wireless/mobile networks may not keep connected via a fixed attachment point (e.g., an access point) due to user mobility. Therefore, users may experience unpredictable disconnection from the core network while they are moving, hence resulting in service disruption. Because of these two unique properties, providing “absolute” QoS guarantees in mobile networks is very difficult, if not impossible.
Imagine a situation where you are hardly able to hear what your friend is talking over the phone or the phone gets cut when you are talking something important. These things are not needed and people would not want to pay enormous amount while getting poor services. Telecommunication is a key player in providing quality of service, and to support quality of service (QoS), communication has to be given due priority. It is important to know the traffic based on priority level. Certain types of traffic should be given more priority over other types. For example, voice should be given a more priority than data traffic because voice is regarded as the most important service. More preference should be given to customers who pay more to get better service, without ignoring the other customers who pay minimal amount. To realize all of these, effective QoS techniques are required (Chenet al., 2007).
The high growth of mobile networks requires corresponding multimedia (such as voice, audio, video, data, etc) applications available over the network. This application requirement and assignment could cause the network to reach congestion point if the network has to maintain such high resources for the quality of service (QoS)demands of the applications. In the past years, the mobile network has experienced so much growth, and this growth may continue in the future. Apart from the sudden rise in the number of users more tasking applications will come up, with consequent increase in resource requirements. (Oyebisiet al.,2008)
The design of such a network, which is based on a wireless architecture, will give room for proper usage of the available frequency spectrum (Goodman et al.,1990). It has been discovered that a strong network link is required to support connections since high quality of service (QoS) without fully coordinated medium and network access can be achieved (Lee,1989).
The major challenge of providing QoS guarantees is to ensure that users' requirements are satisfied throughout the entire service period. The most common quality of service (QoS) demands include: minimum/maximum throughput, delay out-bound or delay in-bound, and packet losses.
The wireless medium must be kept free from congestion, since it will cause an overall channel quality to degrade and loss rates to rise, leads to buffer drops and increased delays, and tends to be totally unfair toward calls which have to traverse a larger number of radio circuits. Call admission control (CAC) and network resource allocation are the major parameters of concern. CAC determines the condition for accepting or rejecting a new call based on the available network resources to ensure that the QoS parameters are met without affecting the current calls.
The main call-level qualities of service determinants based on mobile phone concept are: new call blocking and handoff call blocking probabilities. When a cellular subscriber tries to talk with another subscriber or a base station, the subscriber has to first obtain a channel from one of the base stations that listens. If a channel is available, it is granted to the user otherwise the new call will be terminated. The user relinquishes the medium either when the subscriber is through with the call or migrates to another cell before the call is completed (Tekinay et al., 1991).
The process of migrating from one cell to another cell while a call is ongoing is called handoff. While carrying out handoff, the cellular requires the base station in the cell that it enters to assign it a channel. If no channel is free in the new cell, the handoff call is truncated. Instead of terminating such call, a memory is incorporated which saves the call until a channel is free for transmission of the call. However, for practicality, the medium allocation is usually allocated in a static manner. The use of dynamic threshold to manage the memory at various points in the network will lessen the problems associated with static channel allocation, since the buffer threshold is determined by the rate at which traffics enter the network. This is a pertinent step towards evolution from the other threshold techniques which could be employed by network operators. Data traffic is regarded to be delay-tolerant.
1.2 PROBLEM STATEMENT
Subsequent to the rollout of GSM in Nigeria in 2001, the number of GSM users, internet connectivity and various telecommunication networks grew rapidly, with the introduction of various services such as real-time applications. The communication industry was saddled with Quality of Service (QoS) challenges as a result of this sudden growth without corresponding increase in network infrastructures. This ordeal is being experienced by over 120 million GSM subscribers in Nigeria especially during festive seasons. These challenges are due to inability to set up calls, call drops, occasional service outages, network congestions and crosstalk. The GSM users find it difficult in reaching family members and acquaintances, business associates, well-wishers and friends through services provided by the operators as a result of quality of service degradation ravaging the Nigerian telecommunications industry (Oyebisiet al.,2008).
The above scenario has been as a result of network expansion without corresponding investment in infrastructural development. The “Nigerian Communication Act (NCA)2003”, provides the GSM subscribers the statutory right to derive desired satisfaction for services they pay for. Hence, service providers are required by law to do their best in order to improve quality of service given to the subscribers(Frederic et al.,2010).Therefore, in this research, effort is made to establish key parameters that determine the quality of service of a cellular network and proffer optimization solution to the unending quality of service challenges.
1.3 AIM AND OBJECTIVES OF THE STUDY
The aim of this thesis is to improve the quality of service of cellular networks using dynamic time threshold scheme analysis.
The objectives of this thesis include:
i) To develop appropriate documentation for effective deployment of time threshold scheme.
ii) To improve system performance parameters such as: Traffic Channel Congestion Ratio (TCH Cong. Ratio), Call Drop Ratio (CDR) and Call Setup Success Ratio (CSSR).
iii) To validate the obtained result with standard values set by the regulating bodies of cellular network.
1.4 SCOPE OF THE STUDY
This work is based on the use of dynamic time threshold scheme analysis to improve the Quality of Service (QoS) of cellular networks taking Globacom mobile network as a case study with generalization of findings to other network service providers and operators.
1.5 SIGNIFICANCE OF THE STUDY
It is worthwhile and beneficial to explore how time threshold scheme manage to improve the network utilization. This will permit direct comparison and allow for the determination of the best scheme for improving quality of service. This is one of the goals of this research. With the current trend towards an ever growing use of cellular networks for purposes other than simple voice call, there is a need to explore how incoming traffic properties could impact on network performance, and to seek the best values for key parameters.
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