A Big Data Enabled Channel Model for 5G Wireless Communication Systems

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorHuang, Jie
dc.contributor.authorWang, Cheng-Xiang
dc.contributor.authorBai, Lu
dc.contributor.authorSun, Jian
dc.contributor.authorYang, Yang
dc.contributor.authorLi, Jie
dc.contributor.authorTirkkonen, Olav
dc.contributor.authorZhou, Ming-Tuo
dc.contributor.departmentSoutheast University, Nanjing
dc.contributor.departmentShandong University
dc.contributor.departmentChinese Academy of Sciences
dc.contributor.departmentUniversity of Tsukuba
dc.contributor.departmentDepartment of Communications and Networking
dc.contributor.departmentShanghai Research Center for Wireless Communications
dc.date.accessioned2021-03-10T07:25:48Z
dc.date.available2021-03-10T07:25:48Z
dc.date.issued2020-06-01
dc.description.abstractThe standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling.en
dc.description.versionPeer revieweden
dc.format.extent12
dc.format.extent211-222
dc.format.mimetypeapplication/pdf
dc.identifier.citationHuang , J , Wang , C-X , Bai , L , Sun , J , Yang , Y , Li , J , Tirkkonen , O & Zhou , M-T 2020 , ' A Big Data Enabled Channel Model for 5G Wireless Communication Systems ' , IEEE Transactions on Big Data , vol. 6 , no. 2 , pp. 211-222 . https://doi.org/10.1109/TBDATA.2018.2884489en
dc.identifier.doi10.1109/TBDATA.2018.2884489
dc.identifier.issn2332-7790
dc.identifier.otherPURE UUID: 11263a17-46c3-4719-b579-736842a4e59e
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/11263a17-46c3-4719-b579-736842a4e59e
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/56807975/Huang_big_data_enabled.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/102937
dc.identifier.urnURN:NBN:fi:aalto-202103102223
dc.language.isoenen
dc.publisherIEEE
dc.relation.ispartofseriesIEEE Transactions on Big Dataen
dc.relation.ispartofseriesVolume 6, issue 2en
dc.rightsopenAccessen
dc.subject.keywordBig data
dc.subject.keywordwireless communications
dc.subject.keywordmachine learning
dc.subject.keywordchannel modeling
dc.subject.keywordartificial neural network
dc.titleA Big Data Enabled Channel Model for 5G Wireless Communication Systemsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionacceptedVersion
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