Adaptive Beam Sweeping With Supervised Learning

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Fältnamn	Indikatorer	Metadata
000		03055naa a2200385 4500
001		oai:DiVA.org:kth-323209
003		SwePub
008		230130s2022 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3232092 URI
024	7	a https://doi.org/10.1109/LWC.2022.32132332 DOI
040		a (SwePub)kth
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Lei, Wanluu KTH,Teknisk informationsvetenskap,Ericsson Inc, GAIA, Santa Clara, CA 95054 USA.4 aut0 (Swepub:kth)u1dm05jn
245	1 0	a Adaptive Beam Sweeping With Supervised Learning
264	1	b Institute of Electrical and Electronics Engineers (IEEE),c 2022
338		a print2 rdacarrier
500		a QC 20230130
520		a Utilizing millimeter-wave (mmWave) frequencies for wireless communication in mobile systems is challenging since continuous tracking of the beam direction is needed. For the purpose, beam sweeping is performed periodically. Such approach can be sufficient in the initial deployment of the network when the number of users is small. However, a more efficient solution is needed when lots of users are connected to the network due to higher overhead consumption. We explore a supervised learning approach to adaptively perform beam sweeping, which has low implementation complexity and can improve cell capacity by reducing beam sweeping overhead. By formulating the beam tracking problem as a binary classification problem, we applied supervised learning methods to solve the formulated problem. The methods were tested on two scenarios: ray-tracing outdoor scenario and over-the-air (OTA) testing dataset from Ericsson. Both experimental results show that the proposed methods significantly increase cell throughput comparing with existing exhaustive sweeping and periodical sweeping strategies.
650	7	a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Telekommunikation0 (SwePub)202042 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Telecommunications0 (SwePub)202042 hsv//eng
653		a Millimeter-wave
653		a beam tracking
653		a supervised learning
700	1	a Lu, Chenguangu Ericsson Res, S-16483 Stockholm, Sweden.4 aut
700	1	a Huang, Yeziu Ericsson Res, S-16483 Stockholm, Sweden.4 aut
700	1	a Rao, Jingu Ericsson AB, BNEW DNEW, S-16483 Stockholm, Sweden.4 aut
700	1	a Xiao, Ming,d 1975-u KTH,Teknisk informationsvetenskap4 aut0 (Swepub:kth)u1iq6n9a
700	1	a Skoglund, Mikael,d 1969-u KTH,Teknisk informationsvetenskap4 aut0 (Swepub:kth)u1dbnyps
710	2	a KTHb Teknisk informationsvetenskap4 org
773	0	t IEEE Wireless Communications Lettersd : Institute of Electrical and Electronics Engineers (IEEE)g 11:12, s. 2650-2654q 11:12<2650-2654x 2162-2337x 2162-2345
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-323209
856	4 8	u https://doi.org/10.1109/LWC.2022.3213233

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Av författaren/redakt...: Lei, Wanlu; Lu, Chenguang; Huang, Yezi; Rao, Jing; Xiao, Ming, 1975 ...; Skoglund, Mikael ...

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