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dc.contributor.authorWang, Li-Chunen_US
dc.contributor.authorCheng, Shao-Hungen_US
dc.date.accessioned2020-02-02T23:54:38Z-
dc.date.available2020-02-02T23:54:38Z-
dc.date.issued2019-07-01en_US
dc.identifier.issn2327-4697en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TNSE.2018.2842113en_US
dc.identifier.urihttp://hdl.handle.net/11536/153580-
dc.description.abstractDeploying dense small cells is the key to providing high capacity, but raise the serious issue of energy consumption and inter-cell interference. To understand the behaviors of ultra-dense small cells (UDSC) with dynamic interference and traffic patterns, this paper presents a data-driven resource management (DDRM) framework to implement power control and channel rearrangement in UDSC. We find that the inter-cell interference can be used to describe the affinity of cells. Thus, we propose an unsupervised learning algorithm for UDSC, called affinity propagation power control (APPC) mechanism. In principle, APPC first groups small cells into different clusters and identifies cluster centers. Next, the transmission power of a cluster center is decreased to reduce the interference to the neighboring cells' users in this cluster. Since lowering transmission power of a cluster center cell may cause the performance degradation to the users at the cell edge, a victim-aware channel rearrangement (VACR) mechanism is further designed to adjust the channel usage bandwidth of the neighboring cells in order to guarantee the quality of service of these victimized users. Our simulation results show that the DDRM framework can significantly improve energy efficiency and throughput in UDSC compared to the existing approaches.en_US
dc.language.isoen_USen_US
dc.subjectAffinity propagation clusteringen_US
dc.subjectdata-drivenen_US
dc.subjectultra-dense small cellsen_US
dc.subjectenergy efficiencyen_US
dc.subjectquality of serviceen_US
dc.titleData-Driven Resource Management for Ultra-Dense Small Cells: An Affinity Propagation Clustering Approachen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TNSE.2018.2842113en_US
dc.identifier.journalIEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERINGen_US
dc.citation.volume6en_US
dc.citation.issue3en_US
dc.citation.spage267en_US
dc.citation.epage279en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000484296800009en_US
dc.citation.woscount1en_US
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