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Markov Multi-Beamtracking in SU-MIMO with 60 GHz Mobile Channel Measurements

Published

Author(s)

Steve Blandino, Jelena Senic, Camillo Gentile, Derek Caudill

Abstract

Beamforming training refers to the exhaustive scan over which the receiver and transmitter beams are steered along a predefined set of angles to determine the beam pairs that coincide with the dominant channel paths, for spatial multiplexing. In the presence of mobility, training necessitates a high refresh rate to maintain connectivity and so to reduce overhead, beamtracking algorithms have emerged in the past five years – algorithms that exploit the spatial-temporal consistency of the channel to limit the scan to beam pairs that are local to those determined at the previous time(s). The algorithms' true performance, however, is still unknown since results reported to date have employed oversimplified channel models – either with just a few paths, paths that transition smoothly over time, no blockage, etc. In this paper, we propose a novel beamtracking algorithm formulated as a first-order Markov process that supports multiple digital chains and whose scan limit is dynamically adjusted to the mobile speed. The algorithm's performance is reported based on real channel measurements – not a channel model – collected with our high-precision 3D double-directional 60 GHz channel sounder. The measurement campaign, to our knowledge, is unprecedented – with over 26,000 large-scale measurements, spaced 10 cm apart on average, and up to a receiver-transmitter distance of 20 meters. We demonstrate that up to eight digital chains can be supported and that only the weakest four chains ever lose track, but only when the receiver rotates very quickly.
Citation
IEEE Open Journal of Vehicular Technology
Volume
3

Keywords

Markov Multi-Beamtracking on 60 GHz Mobile Channel Measurements

Citation

Blandino, S. , Senic, J. , Gentile, C. and Caudill, D. (2021), Markov Multi-Beamtracking in SU-MIMO with 60 GHz Mobile Channel Measurements, IEEE Open Journal of Vehicular Technology, [online], https://doi.org/10.1109/OJVT.2021.3138697, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933127 (Accessed October 4, 2022)
Created December 28, 2021, Updated August 30, 2022