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In this work, we present a 5G trace dataset collected from a major Irish mobile operator. The dataset is generated from two mobility patterns (static and car), and across two application patterns(video streaming and file download). The dataset is composed of client-side cellular key performance indicators (KPIs) comprised of channel-related metrics, context-related metrics, cell-related metrics and throughput information. These metrics are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 5G networks. To supplement our real-time 5G production network dataset, we also provide a 5G large scale multi-cell ns-3 simulation framework. The availability of the 5G/mmwave module for the ns-3 mmwave network simulator provides an opportunity to improve our understanding of the dynamic reasoning for adaptive clients in 5G multi-cell wireless scenarios. The purpose of our framework is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the basestation (eNodeB or eNB) environment and scheduling principle, to end user. Our framework permits other researchers to investigate this interaction through the generation of their own synthetic datasets.

", "funding": [ { "award": { "id": "0271asj38::13/RC/2077", "number": "13/RC/2077", "program": "SFI Research Centres", "title": { "en": "CONNECT: The Centre for Future Networks & Communications" } }, "funder": { "id": "0271asj38", "name": "Science Foundation Ireland" } }, { "award": { "id": "0271asj38::13/IA/1892", "number": "13/IA/1892", "program": "SFI Investigator Programme", "title": { "en": "An Internet Infrastructure for Video Streaming Optimisation (iVID)" } }, "funder": { "id": "0271asj38", "name": "Science Foundation Ireland" } } ], "publication_date": "2020-04-14", "publisher": "Zenodo", "related_identifiers": [ { "identifier": "https://github.com/uccmisl/5Gdataset/tree/v1.0.0", "relation_type": { "id": "issupplementto", "title": { "de": "Erg\u00e4nzt", "en": "Is supplement to" } }, "resource_type": { "id": "software", "title": { "de": "Software", "en": "Software" } }, "scheme": "url" } ], "resource_type": { "id": "software", "title": { "de": "Software", "en": "Software" } }, "rights": [ { "title": { "en": "" } } ], "subjects": [ { "subject": "Dataset" }, { "subject": "5G" }, { "subject": "NR" }, { "subject": "Mobility" }, { "subject": "throughput" }, { "subject": "context information" }, { "subject": "adaptive video streaming" }, { "subject": "mmwave" } ], "title": "Beyond Throughput, The Next Generation: A 5G Dataset with Channel and Context Metrics", "version": "v1.1" }, "parent": { "access": { "owned_by": { "user": "65350" } }, "communities": {}, "id": "3751186", "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.3751186", "provider": "datacite" } } }, "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.3751194", "provider": "datacite" }, "oai": { "identifier": "oai:zenodo.org:3751194", "provider": "oai" } }, "revision_id": 4, "stats": { "all_versions": { "data_volume": 11328952627.0, "downloads": 260, "unique_downloads": 204, "unique_views": 803, "views": 861 }, "this_version": { "data_volume": 11205597298.0, "downloads": 238, "unique_downloads": 184, "unique_views": 691, "views": 740 } }, "status": "published", "swh": {}, "updated": "2020-04-15T08:20:20.346419+00:00", "versions": { "index": 2, "is_latest": true } }