Community detection applications in cellular networks (Record no. 10896)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 07596nam a22002657a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 201210s2024 a|||f bm|| 00| 0 eng d |
| 024 7# - Author Identifier | |
| Standard number or code | 0000-0001-9838-5137 |
| Source of number or code | ORCID |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | EG-CaNU |
| Transcribing agency | EG-CaNU |
| 041 0# - Language Code | |
| Language code of text | eng |
| Language code of abstract | eng |
| -- | ara |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Ayman gaber mohamed mohamed |
| 245 1# - TITLE STATEMENT | |
| Title | Community detection applications in cellular networks |
| Statement of responsibility, etc. | /Ayman gaber mohamed mohamed |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Date of publication, distribution, etc. | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 49p. |
| Other physical details | ill. |
| Dimensions | 21 cm. |
| 500 ## - GENERAL NOTE | |
| Materials specified | Supervisor: <br/>Nashwa Abdelbaki |
| 502 ## - Dissertation Note | |
| Dissertation type | Thesis (M.A.)—Nile University, Egypt, 2024 . |
| 504 ## - Bibliography | |
| Bibliography | "Includes bibliographical references" |
| 505 0# - Contents | |
| Formatted contents note | Contents:<br/>DEDICATION................................................................................................................ i<br/>ACKNOWLEDGEMENTS........................................................................................... ii<br/>TABLE OF CONTENTS.............................................................................................. vi<br/>LIST OF FIGURES ...................................................................................................... vi<br/>LIST OF TABLES....................................................................................................... vii<br/>ACRONYMS.............................................................................................................. viii<br/>PUBLICATIONS......................................................................................................... vii<br/>ABSTRACT.................................................................................................................. iii<br/>Chapter 1 INTRODUCTION.........................................................................................1<br/>1.1 Thesis Outlines.....................................................................................................3<br/>1.2 Thesis Contributions...........................................................................................4<br/>Chapter 2 OPPRTUNITIES AND USE CASES ...........................................................5<br/>Chapter 3 MOBILE NETWORK ARCHETICTURE AND TRACKING AREA <br/>DESIGN.........................................................................................................................9<br/>Chapter 4 PREVIOUSE WORK..................................................................................17<br/>Chapter 5 CLUSTERING, CONSTRAINED CLUSTERING AND COMMUNITY <br/>DETECTION ...............................................................................................................19<br/>5.1 Clustering Algorithms........................................................................................19<br/>5.2 Edge Betweenness Calculation:.........................................................................23<br/>5.3 Edge Removal:....................................................................................................23<br/>5.4 Final Communities: ............................................................................................24<br/>CHAPTER 6 PHASE1: DATASET AND CLUSTERING ALGORITHMS RESULTS<br/>......................................................................................................................................25<br/>vii<br/>6.1 Dataset Creation................................................................................................25<br/>6.2 Results of Clustering Algorithms and Constrained Clustering..........................27<br/>Chapter 7 Phase 2 Dataset and Community Detection Algorithm..............................32<br/>7.1 Dataset Creation.................................................................................................32<br/>7.2 Community Detection Algorithm ......................................................................33<br/>7.3 Benchmarking Results.......................................................................................42<br/>Chapter 8 CONCLUSION AND FUTURE WORK....................................................46<br/>8.1 Conclusion..........................................................................................................46<br/>8.2 Future Work.......................................................................................................46<br/>REFERENCES ............................................................................................................49 |
| 520 3# - Abstract | |
| Abstract | Abstract:<br/>The explosive growth of mobile internet services and demand for data connectivity <br/>boosts the innovation and development in Radio Access Network (RAN) to define how<br/>next generation mobile networks will look like. Continuous improvement in existing <br/>RAN is crucial to meet very strict speed and latency requirements by different mobile <br/>applications with minimum investments. The existing Radio Access Network (RAN)<br/>is facing many challenges to meet Quality of Service (QoS) requirements by different <br/>mobile applications in addition to the increasing pressure to reduce operating cost. <br/>Innovation and development in RAN have been accelerated to tackle these challenges <br/>and to define how next generation mobile networks should look like. The role of<br/>Machine Learning (ML) and Artificial Intelligence (AI) driven innovations within the <br/>RAN domain is strengthening and attracting lots of attention to tackle many of the <br/>challenging problems. In this research we surveyed RAN network base stations <br/>clustering and its applications in the literature. The research also demonstrates how to<br/>leverage community detection algorithms to understand underlying community <br/>structures within RAN. Tracking Areas (TA) planning novel framework was developed <br/>by adapting existing community detection algorithm to solve the problem of statically <br/>partitioning a set of base stations into TA according to mobility patterns. Finally, live <br/>network dataset in dense urban part of Cairo is used to assess how the developed <br/>framework is used to partition this part of the network more efficiently compared to <br/>other clustering techniques. Results obtained showed that the new methodology saved <br/>up to 37.8% of inter TA signalling overhead and surpassing other conventional <br/>iv<br/>clustering algorithms. The main contributions of the thesis can be summarized as <br/>follows. <br/>1. Formulating the Tracking Area optimization problem using live network dataset <br/>using real network measurements to estimate density of mobile subscriber’s mobility <br/>between different base stations to benchmarking different partitioning approaches. <br/>2. Developing a heuristic approach for solving the tracking area optimization problem <br/>using constrained clustering techniques. <br/>3. Developing a heuristic approach for solving the tracking area optimization problem <br/>using graph theory by defining each base station as a vertex and define weighted edges <br/>between different vertices.<br/>4. A performance comparison for the quality of the constrained partitioning. The <br/>assessment is based on the mobility density between different cuts in each of the used <br/>algorithms using real network statistics.<br/>5. The thesis propose a new framework to tackle other mobile network problems using <br/>graph theory by defining weighted edges between different base stations using the <br/>relevant network statistics according to the problem being studied.<br/>Keywords:<br/> RAN Mobility Optimization, Tracking Area Planning, RAN Intelligence, <br/>Community Detection, Machine Learning |
| 546 ## - Language Note | |
| Language Note | Text in English, abstracts in English and Arabic |
| 650 #4 - Subject | |
| Subject | informatics |
| 655 #7 - Index Term-Genre/Form | |
| Source of term | NULIB |
| focus term | Dissertation, Academic |
| 690 ## - Subject | |
| School | informatics |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| 655 #7 - Index Term-Genre/Form | |
| -- | 187 |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Main library | Main library | 08/27/2024 | 610/A.G.C/2024 | 08/27/2024 | 08/27/2024 | Thesis |