Markov Multi-Beamtracking on 60 GHz Mobile Channel Measurements

Abstract

Beamforming training refers to the exhaustive scan over which the transmitter and receiver jointly steer their beams along a predefined set of double-directional angles to determine the beam pairs that coincide with the dominant propagation paths of the channel, for spatial multiplexing at millimeter-wave. When mobile, training necessitates a high refresh rate to maintain connectivity and so, to reduce overhead, beamtracking algorithms exploit the spatial-temporal consistency of the channel to localize the scan around the beam pairs determined at a previous time. The algorithms’ true performance, however, is still unknown since results reported to date are based on oversimplified channel models. In this paper, we propose a novel beamtracking algorithm formulated as a first-order Markov process that supports multiple beam pairs. The algorithm is evaluated through actual channel measurements – not a channel model – recorded with our high-precision 3D double-directional 60 GHz channel sounder. The measurement campaign, to our knowledge, is unprecedented: with 10, 895 large-scale measurements, spaced 8.8 cm apart on average to emulate continuous motion, over which the mobile receiver traversed a total of 900.2 m. We demonstrate that four beam pairs can be sustained always and that eight pairs can be sustained 57% of the time.

Keywords

• 5G
• beamforming training
• hybrid beamforming
• IEEE 802.11ay
• millimeter-wave
• mmWave