Full metadata record
DC FieldValueLanguage
dc.contributor.authorWang, Li-Chunen_US
dc.contributor.authorCheng, Shao-Hungen_US
dc.date.accessioned2019-08-02T02:18:27Z-
dc.date.available2019-08-02T02:18:27Z-
dc.date.issued2019-06-01en_US
dc.identifier.issn1932-8184en_US
dc.identifier.urihttp://dx.doi.org/10.1109/JSYST.2018.2851755en_US
dc.identifier.urihttp://hdl.handle.net/11536/152288-
dc.description.abstractThis paper presents a data-driven biadaptive selforganizing network (Bi-SON) for ultra-dense small cells (UDSC), which can improve energy efficiency and reduce interference in dynamic environments, taking account of cell switching ON/OFF, transmission power adjustment, and traffic loads simultaneously. In the first adaptation of Bi-SON, a joint traffic load and interference aware cell ranking mechanism first determines the necessary active small cells based on traffic loads, and then ranks all the active small cells based on their carried traffic load and resulting interference. Top ranked cells will transmit at the maximum power. The last ranked K cells will adjust the transmission power for interference reduction in the second adaptation function of Bi-SON, while maintaining the required quality of service. According to a polynomial regression learning approach, the total system throughput of UDSC is characterized as a function of K. Compared to the baseline case when all the cells transmit with the maximum power, our proposed Bi-SON framework can improve the throughput and energy efficiency of UDSC by 73% and 169%, respectively. However, the pure switching ON/OFF approach can only improve the throughput and the energy efficiency of UDSC by 52% and 115%, respectively. As demonstrated, even with a simple power adaptation algorithm, a learningbased Bi-SON framework can improve the performance of UDSC by taking advantage of the pervasive availability of voluminous data.en_US
dc.language.isoen_USen_US
dc.subjectData-drivenen_US
dc.subjectenergy efficiencyen_US
dc.subjectpolynomial regression (PR)en_US
dc.subjectself-organizing network (SON)en_US
dc.subjectultra-dense small cells (UDSC)en_US
dc.titleSelf-Organizing Ultra-Dense Small Cells in Dynamic Environments: A Data-Driven Approachen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/JSYST.2018.2851755en_US
dc.identifier.journalIEEE SYSTEMS JOURNALen_US
dc.citation.volume13en_US
dc.citation.issue2en_US
dc.citation.spage1397en_US
dc.citation.epage1408en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000470839000033en_US
dc.citation.woscount0en_US
Appears in Collections:Articles