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 millimeter-wave 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 measurementsnot a channel modelrecorded 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.

Beam Tracking Based on a New State Model for mmWave V2I Communication on 3D Roads

“…The reference power gain factor was normalized to β ῀ = 1. If there is no special comparison, the default antenna number for both transmitter and receiver is 16, the transmission power is −20dBm, the deflection angle of vehicle motion is π/2 7 , and the initial velocity of vehicle motion is 10m/s. Table 1 lists the preceding public simulation parameters.…”

“…For a user who is moving at high speed, a training beam is sent during the communication to allow accurate tracking of its changing channel. Such data transmission scheme used to deal with such users with high mobility is usually called beam tracking [6][7][8]. Since the high directionality of the beam in mmWave MIMO system, the communication parties are easy to suffer from beam misalignment and communication interruption due to small device movement or environmental changes.…”

A deep neural network based MU-MIMO beamforming training protocol for IEEE 802.11ay

“…The simulation results demonstrate that our scheme transmits fewer action frames and achieves a lower BFT time than existing schemes. characteristics [2]; recently, the 802.11ay has been standardized to address this challenge and enables wireless local area network (WLAN) devices to achieve transmission rates of up to 100 Gbps in the 60 GHz band [3].…”

“…For IEEE 802.11ay dense environments, [15] and [16] designed advanced protocols for adjustable BFT with the aim of shortening the BFT time. Furthermore, a recent study [2] described efficient BFT algorithms formulated as a first-order Markov process that supports multiple beam pairs, exploiting the spatial-temporal consistency of the mmWave channel.…”

“…The computational complexity of our DTAC scheme depends on the number of times the SNRs corresponding to candidate sets of TSs have to be estimated. (2) In our heuristic algorithm, a lookup table is used to reduce the number of times the SNRs are estimated from a pretrained DNN; our scheme can easily initialize the lookup table using the SISO feedback. (3) Using the lookup table, our scheme selects sets of TSs to be used for the transmission of action frames in the MIMO phase.…”