000			07596nam a22002657a 4500
008			201210s2024 a|||f bm|| 00| 0 eng d
024	7		_a0000-0001-9838-5137 _2ORCID
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng _bara
082			_a610
100	0		_aAyman gaber mohamed mohamed _93568
245	1		_aCommunity detection applications in cellular networks _c/Ayman gaber mohamed mohamed
260			_c2024
300			_a49p. _bill. _c21 cm.
500			_3Supervisor: Nashwa Abdelbaki
502			_aThesis (M.A.)—Nile University, Egypt, 2024 .
504			_a"Includes bibliographical references"
505	0		_aContents: DEDICATION................................................................................................................ i ACKNOWLEDGEMENTS........................................................................................... ii TABLE OF CONTENTS.............................................................................................. vi LIST OF FIGURES ...................................................................................................... vi LIST OF TABLES....................................................................................................... vii ACRONYMS.............................................................................................................. viii PUBLICATIONS......................................................................................................... vii ABSTRACT.................................................................................................................. iii Chapter 1 INTRODUCTION.........................................................................................1 1.1 Thesis Outlines.....................................................................................................3 1.2 Thesis Contributions...........................................................................................4 Chapter 2 OPPRTUNITIES AND USE CASES ...........................................................5 Chapter 3 MOBILE NETWORK ARCHETICTURE AND TRACKING AREA DESIGN.........................................................................................................................9 Chapter 4 PREVIOUSE WORK..................................................................................17 Chapter 5 CLUSTERING, CONSTRAINED CLUSTERING AND COMMUNITY DETECTION ...............................................................................................................19 5.1 Clustering Algorithms........................................................................................19 5.2 Edge Betweenness Calculation:.........................................................................23 5.3 Edge Removal:....................................................................................................23 5.4 Final Communities: ............................................................................................24 CHAPTER 6 PHASE1: DATASET AND CLUSTERING ALGORITHMS RESULTS ......................................................................................................................................25 vii 6.1 Dataset Creation................................................................................................25 6.2 Results of Clustering Algorithms and Constrained Clustering..........................27 Chapter 7 Phase 2 Dataset and Community Detection Algorithm..............................32 7.1 Dataset Creation.................................................................................................32 7.2 Community Detection Algorithm ......................................................................33 7.3 Benchmarking Results.......................................................................................42 Chapter 8 CONCLUSION AND FUTURE WORK....................................................46 8.1 Conclusion..........................................................................................................46 8.2 Future Work.......................................................................................................46 REFERENCES ............................................................................................................49
520	3		_aAbstract: The explosive growth of mobile internet services and demand for data connectivity boosts the innovation and development in Radio Access Network (RAN) to define how next generation mobile networks will look like. Continuous improvement in existing RAN is crucial to meet very strict speed and latency requirements by different mobile applications with minimum investments. The existing Radio Access Network (RAN) is facing many challenges to meet Quality of Service (QoS) requirements by different mobile applications in addition to the increasing pressure to reduce operating cost. Innovation and development in RAN have been accelerated to tackle these challenges and to define how next generation mobile networks should look like. The role of Machine Learning (ML) and Artificial Intelligence (AI) driven innovations within the RAN domain is strengthening and attracting lots of attention to tackle many of the challenging problems. In this research we surveyed RAN network base stations clustering and its applications in the literature. The research also demonstrates how to leverage community detection algorithms to understand underlying community structures within RAN. Tracking Areas (TA) planning novel framework was developed by adapting existing community detection algorithm to solve the problem of statically partitioning a set of base stations into TA according to mobility patterns. Finally, live network dataset in dense urban part of Cairo is used to assess how the developed framework is used to partition this part of the network more efficiently compared to other clustering techniques. Results obtained showed that the new methodology saved up to 37.8% of inter TA signalling overhead and surpassing other conventional iv clustering algorithms. The main contributions of the thesis can be summarized as follows. 1. Formulating the Tracking Area optimization problem using live network dataset using real network measurements to estimate density of mobile subscriber’s mobility between different base stations to benchmarking different partitioning approaches. 2. Developing a heuristic approach for solving the tracking area optimization problem using constrained clustering techniques. 3. Developing a heuristic approach for solving the tracking area optimization problem using graph theory by defining each base station as a vertex and define weighted edges between different vertices. 4. A performance comparison for the quality of the constrained partitioning. The assessment is based on the mobility density between different cuts in each of the used algorithms using real network statistics. 5. The thesis propose a new framework to tackle other mobile network problems using graph theory by defining weighted edges between different base stations using the relevant network statistics according to the problem being studied. Keywords: RAN Mobility Optimization, Tracking Area Planning, RAN Intelligence, Community Detection, Machine Learning
546			_aText in English, abstracts in English and Arabic
650		4	_ainformatics
655		7	_2NULIB _aDissertation, Academic _9187
690			_ainformatics
942			_2ddc _cTH
999			_c10896 _d10896