IMPROVED SCHEDULER FOR LONG TERM EVOLUTION (LTE) DOWNLINK TRANSMISSION


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TABLE OF CONTENT

TITLE PAGE
APPROVAL PAGE
CERTIFICATION
DECLARATION
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
TABLE OF CONTENT
LIST OF FIGURES
LIST OF TABLES
MEANING OF ABREVIATIONS USED IN THE THESIS

CHAPTER ONE: INTRODUCTION
1.1       BACKGROUND OF THE STUDY
1.2       STATEMENT OF THE PROBLEM
1.3       AIMS/OBJECTIVES
1.4       SCOPE OF THE WORK
1.5       SIGNIFICANCE OF STUDY
1.6       METHODOLOGY
1.7       THESIS OUTLINE

CHAPTER TWO: LITERATURE REVIEW
2.1       INTRODUCTION
2.2       GENERAL CELLULAR CONCEPT
2.3       EVOLUTION OF LTE SYSTEM
2.3.1    FIRST-GENERATION MOBILE 1G
2.3.2    SECOND-GENERATION MOBILE 2G
2.3.3    THIRD-GENERATION MOBILE 3G
2.3.4    THE PATH TOWARD 4G
2.4       ADVANTAGES OF LTE TECHNOLOGY
2.5       MOTIVATION FOR 3GPP LTE RELEASE 8[5]
2.6       FEATURES OF LTE
2.7       SCHEDULING
2.7.1    DOWNLINK SCHEDULING
2.7.2    PROCEDURE OF DOWNLINK SCHEDULING
2.8       GENERAL CLASSIFICATION OF SCHEDULING
2.9       WHAT IS FUZZY LOGIC?
2.9.1    WHY USE FUZZY LOGIC?
2.10     FUZZY INFERENCE SYSTEM
2.11     RELATED WORK ON SCHEDULING IN LTE

CHAPTER THREE: SYSTEM MODELING
3.1       INTRODUCTION
3.2       PHYSICAL MODEL
3.3       PROPOSED SCHEDULING SCHEME
3.4       SIMULATION SCENARIO
3.5       SIMULATION PARAMETERS
3.5.1    SIMULATION TRAFFIC MODEL
3.6       MODEL VALIDATION

CHAPTER FOUR: SIMULATION AND RESULT ANALYSIS
4.1       PERFORMANCE METRICS
4.2       RESULT ANALYSIS
4.2.1    THROUGHPUT FOR VIDEO FLOW
4.2.2    THROUGHPUT FOR VOIP FLOWS
4.2.3    THROUGHPUT FOR NON-REAL TIME FLOWS
4.2.4    PACKET LOSS RATE FOR VIDEO FLOWS
4.2.5    PACKET LOSS RATE FOR VOIP FLOWS
4.2.6    PACKET LOSS RATE FOR NON-REAL TIME FLOWS
4.2.7    DELAYS FOR VIDEO FLOWS
4.2.8    DELAYS FOR VOIP FLOWS
4.2.9    DELAYS FOR NON-REAL TIME FLOWS
4.2.10  FAIRNESS INDEX FOR VIDEO FLOWS
4.2.11  FAIRNESS INDEX FOR VOIP FLOWS
4.2.12  FAIRNESS INDEX FOR NON-REAL TIME FLOWS

CHAPTER FIVE: CONCLUSION AND RECOMMENDATION
5.1       CONCLUSION
5.2       RECOMMENDATION
            REFERENCES

APPENDIX
FUZZY INFERENCE PROCESSES TO DETERMINE
FLOW WEIGHT




ABSTRACT


Long term evolution (LTE) Network is often faced with the challenge of meeting up with the quality of service (QoS) requirement of the different services supported in the Network. Maintaining a trade-off between system throughput and fairness among users when making radio resources scheduling decisions is a very sensitive issue. Several algorithms have been proposed to this effect by different researchers in an attempt to manage the limited radio resources. One of well-known packet schedulers known as M-LWDF algorithm is known to support both real time and non-real time services. This algorithm has been found not to support real time services at a sufficient level. This is due to the fact that head of line (HOL) delay and packet delay not sufficient to balance the scheduling decision to real time services thereby degrading its performance. This research work was an attempt to improve the performance of MLWDF by incorporating bandwidth of flow,, which is directly proportional to flow weight and reserved rate .This approach used the uncertainty principle of fuzzy logic to calculate new weight for the different flows, by considering two input parameters from the network which are latency requirement for real time traffic and throughput for non-real time traffic .The performance of this algorithm was compared with PF,M-LWDF and EXP/PF schemes using system throughput, packet loss rate, delay and fairness as a performance indices in an LTE simulator. Results showed that when I-MLWDF was used, the aggregate throughput for video and VOIP increases considerably by 9.8% and 0.1% respectively when the cell was loaded by 60 users and the throughput for non- real time flow showed degraded performance of about 90%as against MLWDF. The improved scheme showed that the packet loss rate for video flow decreases by about 35.71% and 75% when compared with EXP/PF and MLWDF respectively. The acceptable packet loss rate for VOIP flow is under 3% for both algorithm and the improved scheme showing better result by about 20%compared to MLWDF. The packet loss rate for non-real time flow for I-MLWDF showed a poor performance of about 95% as against MLWDF.The delay for video, VOIP for the improved scheme is under 0.05s and 0.007s respectively as against MLWDF with delays under 0.07s and 0.02s for video and VOIP respectively, when the cell was loaded with 60 users. Also the delay for I-MLWDF showed higher delay of 98% as compared to MLWDF. Fairness index for video flow for I-MLWDF is 20% better than MLWDF. Both algorithms showed performance level between 98.5% and 99.5% for fairness index for VOIP flow with I-MLWDF showing improved performance of about 0.2% increase better than M-LWDF. However, the highest fairness index was presented by MLWDF and PF that reach a level above 99% whereas I-MLWDF showed the worst result that reach a level of about 93%.






CHAPTER ONE

INTRODUCTION

1.1BACKGROUND OF THE STUDY

Cellular communication was designed to ameliorate the flaws of traditional wireless and wired communication networks providing communication between moving units and/or stationary units and vice versa[1]. The moving units are referred to as mobile stations while the stationary units are referred to as land fixed units. Some of the flaws encountered in traditional wireless communication networks range from their inability to cope with the travelling speed of fast mobile units; to its low capacity and poor data rate.

Cellular communication divides its coverage region into small regions called cells; hence, the terminology “cellular”. This concept of cellular co mmunication began to appear in Bell System proposals during the late 1970s [1, 2]. Cell size is dynamic and is dependent on the population (traffic) in the region to be covered. However, it is set by the transmitter power and the frequency of operation of the cell. To manage scarce frequency resources and meet the requirement of users, cells apply techniques like frequency hopping, frequency re-use and cell splitting [3].

However, technological advancement and modernization led to an increase in the speed and capacity of communication gadgets and increase in the growth and demands of cellular networks respectively[1]. Furthermore, with the current trend of an integrated network, the limits of cellular communications were expanded and subsequently, cellular communication began to compete favourably with conventional wideband and broadband systems. Cellular network can be viewed as a favorable interface between the user and the integrated network [3]. These....

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