Channel Charting: Locating Users within the Radio Environment using Channel State Information

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorStuder, Christophen_US
dc.contributor.authorMedjkouh, Saiden_US
dc.contributor.authorGonultas, Emreen_US
dc.contributor.authorGoldstein, Tomen_US
dc.contributor.authorTirkkonen, Olaven_US
dc.contributor.departmentDepartment of Communications and Networkingen
dc.contributor.groupauthorCommunications Theoryen
dc.contributor.organizationCornell Universityen_US
dc.contributor.organizationUniversity of Maryland, College Parken_US
dc.date.accessioned2018-12-21T10:31:51Z
dc.date.available2018-12-21T10:31:51Z
dc.date.issued2018-08-23en_US
dc.description.abstractWe propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area. The channel chart captures the local spatial geometry of the area so that points that are close in space will also be close in the channel chart and vice versa. CC works in a fully unsupervised manner, i.e., learning is only based on channel state information (CSI) that is passively collected at a single point in space, but from multiple transmit locations in the area over time. The method then extracts channel features that characterize large-scale fading properties of the wireless channel. Finally, the channel charts are generated with tools from dimensionality reduction, manifold learning, and deep neural networks. The network element performing CC may be, for example, a multi-antenna base-station in a cellular system and the charted area in the served cell. Logical relationships related to the position and movement of a transmitter, e.g., a user equipment (UE), in the cell can then be directly deduced from comparing measured radio channel characteristics to the channel chart. The unsupervised nature of CC enables a range of new applications in UE localization, network planning, user scheduling, multipoint connectivity, hand-over, cell search, user grouping, and other cognitive tasks that rely on CSI and UE movement relative to the base-station, without the need of information from global navigation satellite systems.en
dc.description.versionPeer revieweden
dc.format.extent47682-47698
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationStuder, C, Medjkouh, S, Gonultas, E, Goldstein, T & Tirkkonen, O 2018, ' Channel Charting : Locating Users within the Radio Environment using Channel State Information ', IEEE Access, vol. 6, pp. 47682-47698 . https://doi.org/10.1109/ACCESS.2018.2866979en
dc.identifier.doi10.1109/ACCESS.2018.2866979en_US
dc.identifier.issn2169-3536
dc.identifier.otherPURE UUID: 8be76996-b6d0-4ac8-9d63-ece9ee4659a4en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/8be76996-b6d0-4ac8-9d63-ece9ee4659a4en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85052676374&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/30325447/08444621.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/35683
dc.identifier.urnURN:NBN:fi:aalto-201812216692
dc.language.isoenen
dc.relation.ispartofseriesIEEE Accessen
dc.relation.ispartofseriesVolume 6en
dc.rightsopenAccessen
dc.subject.keywordAutoencodersen_US
dc.subject.keyworddeep learningen_US
dc.subject.keyworddimensionality reductionen_US
dc.subject.keywordlocalizationen_US
dc.subject.keywordmachine learningen_US
dc.subject.keywordmanifold learningen_US
dc.subject.keywordmassive multiple-input multiple-output (MIMO)en_US
dc.subject.keywordSammon’s mappingen_US
dc.titleChannel Charting: Locating Users within the Radio Environment using Channel State Informationen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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