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Machine Learning-Based Handovers for Sub-6 GHz and mmWave Integrated Vehicular Networks
Southwest Jiaotong Univ, Key Lab Informat Coding & Transmiss, Chengdu 610031, Sichuan, Peoples R China..ORCID iD: 0000-0003-4476-3563
Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA..
Univ Florida, Dept Elect & Comp Engn, Gainesville, FL 32611 USA..
Univ Alabama, Dept Elect & Comp Engn, Huntsville, AL 35899 USA..
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2019 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 18, no 10, p. 4873-4885Article in journal (Refereed) Published
Abstract [en]

The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that the handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles' positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to employ vehicle-to-vehicle (V2V) connections to forward data for V2I links. The theoretical and simulation results are provided to validate the feasibility of the proposed schemes.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 18, no 10, p. 4873-4885
Keywords [en]
Control/user-plane decoupling, vehicular networks, handovers, target discovery, machine learning, V2V communications
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-264167DOI: 10.1109/TWC.2019.2930193ISI: 000492860000020Scopus ID: 2-s2.0-85077323553OAI: oai:DiVA.org:kth-264167DiVA, id: diva2:1374180
Note

QC 20191129

Available from: 2019-11-29 Created: 2019-11-29 Last updated: 2020-03-09Bibliographically approved

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Xiao, Ming

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