Deep Learning Predictive Band Switching in Wireless Networks
In cellular systems, the user equipment (UE) can request a change in the frequency band when its rate drops below a threshold on the current band. The UE is then instructed by the base station (BS) to measure the quality of candidate bands, which requires a measurement gap in the data transmission,...
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Veröffentlicht in: | IEEE transactions on wireless communications 2021-01, Vol.20 (1), p.96-109 |
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creator | Mismar, Faris B. Alammouri, Ahmad Alkhateeb, Ahmed Andrews, Jeffrey G. Evans, Brian L. |
description | In cellular systems, the user equipment (UE) can request a change in the frequency band when its rate drops below a threshold on the current band. The UE is then instructed by the base station (BS) to measure the quality of candidate bands, which requires a measurement gap in the data transmission, thus lowering the data rate. We propose an online-learning based band switching approach that does not require any measurement gap. Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location. We focus on switching between a lower (e.g., 3.5 GHz) band and a millimeter wave band (e.g., 28 GHz), and design and evaluate two classification models that are trained on a ray-tracing dataset. A key insight is that measurement gaps are overkill, in that only the relative order of the bands is necessary for band selection, rather than a full channel estimate. Our proposed machine learning-based policies achieve roughly 30% improvement in mean effective rates over those of the industry standard policy, while achieving misclassification errors well below 0.5% and maintaining resilience against blockage uncertainty. |
doi_str_mv | 10.1109/TWC.2020.3023397 |
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The UE is then instructed by the base station (BS) to measure the quality of candidate bands, which requires a measurement gap in the data transmission, thus lowering the data rate. We propose an online-learning based band switching approach that does not require any measurement gap. Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location. We focus on switching between a lower (e.g., 3.5 GHz) band and a millimeter wave band (e.g., 28 GHz), and design and evaluate two classification models that are trained on a ray-tracing dataset. A key insight is that measurement gaps are overkill, in that only the relative order of the bands is necessary for band selection, rather than a full channel estimate. Our proposed machine learning-based policies achieve roughly 30% improvement in mean effective rates over those of the industry standard policy, while achieving misclassification errors well below 0.5% and maintaining resilience against blockage uncertainty.</description><identifier>ISSN: 1536-1276</identifier><identifier>EISSN: 1558-2248</identifier><identifier>DOI: 10.1109/TWC.2020.3023397</identifier><identifier>CODEN: ITWCAX</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial intelligence ; band switching ; Cellular communication ; Channel estimation ; Coherence ; Correlation ; Data transmission ; Deep learning ; Frequencies ; Frequency measurement ; Industry standards ; Machine learning ; millimeter wave (mmWave) ; Millimeter waves ; out-of-band estimation ; Radio signals ; Ray tracing ; Spectral correlation ; Switches ; Switching ; Training ; wireless communications ; Wireless networks</subject><ispartof>IEEE transactions on wireless communications, 2021-01, Vol.20 (1), p.96-109</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-291d998ac4a91fdc21bf36a2ec07089d49c22dea07174e7b88cd21479ad985c53</citedby><cites>FETCH-LOGICAL-c291t-291d998ac4a91fdc21bf36a2ec07089d49c22dea07174e7b88cd21479ad985c53</cites><orcidid>0000-0002-8850-4718 ; 0000-0002-9529-304X ; 0000-0001-5648-1569 ; 0000-0002-9115-5088 ; 0000-0001-8513-1293</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9199558$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9199558$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Mismar, Faris B.</creatorcontrib><creatorcontrib>Alammouri, Ahmad</creatorcontrib><creatorcontrib>Alkhateeb, Ahmed</creatorcontrib><creatorcontrib>Andrews, Jeffrey G.</creatorcontrib><creatorcontrib>Evans, Brian L.</creatorcontrib><title>Deep Learning Predictive Band Switching in Wireless Networks</title><title>IEEE transactions on wireless communications</title><addtitle>TWC</addtitle><description>In cellular systems, the user equipment (UE) can request a change in the frequency band when its rate drops below a threshold on the current band. The UE is then instructed by the base station (BS) to measure the quality of candidate bands, which requires a measurement gap in the data transmission, thus lowering the data rate. We propose an online-learning based band switching approach that does not require any measurement gap. Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location. We focus on switching between a lower (e.g., 3.5 GHz) band and a millimeter wave band (e.g., 28 GHz), and design and evaluate two classification models that are trained on a ray-tracing dataset. A key insight is that measurement gaps are overkill, in that only the relative order of the bands is necessary for band selection, rather than a full channel estimate. Our proposed machine learning-based policies achieve roughly 30% improvement in mean effective rates over those of the industry standard policy, while achieving misclassification errors well below 0.5% and maintaining resilience against blockage uncertainty.</description><subject>Artificial intelligence</subject><subject>band switching</subject><subject>Cellular communication</subject><subject>Channel estimation</subject><subject>Coherence</subject><subject>Correlation</subject><subject>Data transmission</subject><subject>Deep learning</subject><subject>Frequencies</subject><subject>Frequency measurement</subject><subject>Industry standards</subject><subject>Machine learning</subject><subject>millimeter wave (mmWave)</subject><subject>Millimeter waves</subject><subject>out-of-band estimation</subject><subject>Radio signals</subject><subject>Ray tracing</subject><subject>Spectral correlation</subject><subject>Switches</subject><subject>Switching</subject><subject>Training</subject><subject>wireless communications</subject><subject>Wireless networks</subject><issn>1536-1276</issn><issn>1558-2248</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWKt3wcuC562ZJLtJwIvWTygqWOkxpMmsptbdmmwt_nt3afEyMzDPOwMPIadARwBUX0xn4xGjjI44ZZxruUcGUBQqZ0yo_X7mZQ5MlofkKKUFpSDLohiQyxvEVTZBG-tQv2cvEX1wbfjB7NrWPnvdhNZ99JtQZ7MQcYkpZU_Ybpr4mY7JQWWXCU92fUje7m6n44d88nz_OL6a5I5paPOueK2VdcJqqLxjMK94aRk6KqnSXmjHmEdLJUiBcq6U8wyE1NZrVbiCD8n59u4qNt9rTK1ZNOtYdy8NE1KBUFTLjqJbysUmpYiVWcXwZeOvAWp6R6ZzZHpHZueoi5xtIwER_3ENWnfq-B-kxmFW</recordid><startdate>202101</startdate><enddate>202101</enddate><creator>Mismar, Faris B.</creator><creator>Alammouri, Ahmad</creator><creator>Alkhateeb, Ahmed</creator><creator>Andrews, Jeffrey G.</creator><creator>Evans, Brian L.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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subjects | Artificial intelligence band switching Cellular communication Channel estimation Coherence Correlation Data transmission Deep learning Frequencies Frequency measurement Industry standards Machine learning millimeter wave (mmWave) Millimeter waves out-of-band estimation Radio signals Ray tracing Spectral correlation Switches Switching Training wireless communications Wireless networks |
title | Deep Learning Predictive Band Switching in Wireless Networks |
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