Learning-based quality of service prediction in cellular vehicle communication:
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
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Amberg
July 2021
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| Online-Zugang: | Volltext Volltext Inhaltsverzeichnis |
| Beschreibung: | xvi, 147 Seiten Illustrationen, Diagramme, Karten |
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Datensatz im Suchindex
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| adam_text | xi Contents Abstract Zusammenfassung Acknowledgements Relevant Publications iii v vii ix 1 Introduction 1.1 Motivation and research questions......................................... 1.2 Overview of this Thesis .......................................................... 1 1 5 2 Methods and Related Work 2.1 Network Quality Parameter..................................................... 2.1.1 Throughput.................................................................... 2.1.2 Latency............................................................................ 2.2 Network Prediction Features .................................................. 2.3 Models for Network Quality Prediction................................. 2.3.1 Support Vector Regression............................................. 2.3.2 Feedforward Neural Network .................................... 2.3.3 Recurrent Neural Network........................................... 2.3.4 Grid Based Location Smoothing................................. 2.3.5 Comparison of Location and Time Based Approaches . 2.4 Evaluation Metrics................................................................... 2.4.1 Performance Metrics for NQP Prediction................... 2.4.2 Significance Tests........................................................... 2.5 Test Tracks and Datasets.......................................................... 2.5.1 Test Tracks...................................................................... 2.5.2 Overview of Datasets................................................... 7 7 7 10 11 14 17 19 23 26 28 28 29 31 33
33 35 3 Environment Features Based Learning Approach 3.1 Location Dependency of Network Quality............................... 3.2 Environment Features for Network Quality............................ 3.3 Generating Novel Features by Map Extraction....................... 3.4 Preprocessing of the Measurements ........................................ 3.4.1 Environment Based Feature Derivation...................... 3.4.2 Filtering......................................................................... 3.4.3 Downsampling.............................................................. 3.4.4 Encoding......................................................................... 3.4.5 Shifting............................................................................ 39 39 40 42 45 45 46 46 49 50
xii 3.4.6 Feature Selection............................................................. 50 4 Experimental Setups and Results 4.1 Throughput Prediction.............................................................. 4.1.1 Technical Setup................................................................ 4.1.2 Datasets and Preprocessing............................................ 4.1.3 Feature Selection............................................................. 4.1.4 Prediction Methods.......................................................... 4.1.5 Evaluation........................................................................ 4.1.6 Conclusion ..................................................................... 4.2 Latency Prediction ................................................................... 4.2.1 Technical Setup................................................................ 4.2.2 Datasets and Preprocessing............................................ 4.2.3 Feature Selection............................................................. 4.2.4 Prediction Methods.......................................................... 4.2.5 Evaluation........................................................................ 4.2.6 Conclusion ..................................................................... 53 53 53 56 58 62 68 75 76 77 80 81 84 86 94 5 Conclusion 97 5.1 Summary.................................................................................... 97 5.2 Contributions............................................................................ 97 5.3
Limitations................................................................................. 100 5.4 Outlook....................................................................................... 101 A Summary of prediction approaches 113 A.l Summary of prediction approaches for Throughput Prediction (TPP).................................................................................. 113 A.2 Summary of prediction approaches for Latency Prediction (LP) 117 В Selected features for quality of service prediction. 119 B.l Features for Throughput Prediction (TPP).............................. 119 B.2 Features for Latency Prediction (LP)....................................... 121 Bibliography 125
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| adam_txt |
xi Contents Abstract Zusammenfassung Acknowledgements Relevant Publications iii v vii ix 1 Introduction 1.1 Motivation and research questions. 1.2 Overview of this Thesis . 1 1 5 2 Methods and Related Work 2.1 Network Quality Parameter. 2.1.1 Throughput. 2.1.2 Latency. 2.2 Network Prediction Features . 2.3 Models for Network Quality Prediction. 2.3.1 Support Vector Regression. 2.3.2 Feedforward Neural Network . 2.3.3 Recurrent Neural Network. 2.3.4 Grid Based Location Smoothing. 2.3.5 Comparison of Location and Time Based Approaches . 2.4 Evaluation Metrics. 2.4.1 Performance Metrics for NQP Prediction. 2.4.2 Significance Tests. 2.5 Test Tracks and Datasets. 2.5.1 Test Tracks. 2.5.2 Overview of Datasets. 7 7 7 10 11 14 17 19 23 26 28 28 29 31 33
33 35 3 Environment Features Based Learning Approach 3.1 Location Dependency of Network Quality. 3.2 Environment Features for Network Quality. 3.3 Generating Novel Features by Map Extraction. 3.4 Preprocessing of the Measurements . 3.4.1 Environment Based Feature Derivation. 3.4.2 Filtering. 3.4.3 Downsampling. 3.4.4 Encoding. 3.4.5 Shifting. 39 39 40 42 45 45 46 46 49 50
xii 3.4.6 Feature Selection. 50 4 Experimental Setups and Results 4.1 Throughput Prediction. 4.1.1 Technical Setup. 4.1.2 Datasets and Preprocessing. 4.1.3 Feature Selection. 4.1.4 Prediction Methods. 4.1.5 Evaluation. 4.1.6 Conclusion . 4.2 Latency Prediction . 4.2.1 Technical Setup. 4.2.2 Datasets and Preprocessing. 4.2.3 Feature Selection. 4.2.4 Prediction Methods. 4.2.5 Evaluation. 4.2.6 Conclusion . 53 53 53 56 58 62 68 75 76 77 80 81 84 86 94 5 Conclusion 97 5.1 Summary. 97 5.2 Contributions. 97 5.3
Limitations. 100 5.4 Outlook. 101 A Summary of prediction approaches 113 A.l Summary of prediction approaches for Throughput Prediction (TPP). 113 A.2 Summary of prediction approaches for Latency Prediction (LP) 117 В Selected features for quality of service prediction. 119 B.l Features for Throughput Prediction (TPP). 119 B.2 Features for Latency Prediction (LP). 121 Bibliography 125 |
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| spelling | Schmid, Josef Verfasser aut Learning-based quality of service prediction in cellular vehicle communication Josef Schmid Amberg July 2021 xvi, 147 Seiten Illustrationen, Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Dissertation Universität Passau 2022 Autonomes Fahrzeug (DE-588)7714938-5 gnd rswk-swf Zellulares Mobilfunksystem (DE-588)4387563-4 gnd rswk-swf Dienstgüte (DE-588)4496068-2 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Autonomes Fahrzeug (DE-588)7714938-5 s Zellulares Mobilfunksystem (DE-588)4387563-4 s Dienstgüte (DE-588)4496068-2 s DE-604 Erscheint auch als Online-Ausgabe urn:nbn:de:bvb:739-opus4-10772 https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/1077 Verlag kostenfrei Volltext https://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-10772 Resolving-System kostenfrei Volltext Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033739877&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Schmid, Josef Learning-based quality of service prediction in cellular vehicle communication Autonomes Fahrzeug (DE-588)7714938-5 gnd Zellulares Mobilfunksystem (DE-588)4387563-4 gnd Dienstgüte (DE-588)4496068-2 gnd |
| subject_GND | (DE-588)7714938-5 (DE-588)4387563-4 (DE-588)4496068-2 (DE-588)4113937-9 |
| title | Learning-based quality of service prediction in cellular vehicle communication |
| title_auth | Learning-based quality of service prediction in cellular vehicle communication |
| title_exact_search | Learning-based quality of service prediction in cellular vehicle communication |
| title_exact_search_txtP | Learning-based quality of service prediction in cellular vehicle communication |
| title_full | Learning-based quality of service prediction in cellular vehicle communication Josef Schmid |
| title_fullStr | Learning-based quality of service prediction in cellular vehicle communication Josef Schmid |
| title_full_unstemmed | Learning-based quality of service prediction in cellular vehicle communication Josef Schmid |
| title_short | Learning-based quality of service prediction in cellular vehicle communication |
| title_sort | learning based quality of service prediction in cellular vehicle communication |
| topic | Autonomes Fahrzeug (DE-588)7714938-5 gnd Zellulares Mobilfunksystem (DE-588)4387563-4 gnd Dienstgüte (DE-588)4496068-2 gnd |
| topic_facet | Autonomes Fahrzeug Zellulares Mobilfunksystem Dienstgüte Hochschulschrift |
| url | https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/1077 https://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-10772 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033739877&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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