A Framework for Active Learning of Beam Alignment in Vehicular Millimeter Wave Communications by Onboard Sensors

Release Date:2019-07-19  Author:ZTE  Click:

A Framework for Active Learning of Beam Alignment in Vehicular Millimeter Wave Communications by Onboard Sensors

Erich Zöchmann
( Christian Doppler Laboratory for Dependable Wireless Connectivity for the Society in Motion, Institute of Telecommunications, TU Wien, 1040 Vienna, Austria)

Abstract:Estimating time-selective millimeter wave wireless channels and then deriving the optimum beam alignment for directional antennas is a challenging task. To solve this problem, one can focus on tracking the strongest multipath components (MPCs). Aligning antenna beams with the tracked MPCs increases the channel coherence time by several orders of magnitude. This contribution suggests tracking the MPCs geometrically. The derived geometric tracker is based on algorithms known as Doppler bearing tracking. A recent work on geometric-polar tracking is reformulated into an efficient recursive version. If the relative position of the MPCs is known, all other sensors on board a vehicle, e.g., lidar, radar, and camera, will perform active learning based on their own observed data. By learning the relationship between sensor data and MPCs, onboard sensors can participate in channel tracking. Joint tracking of many integrated sensors will increase the reliability of MPC tracking.
Keywords: adaptive filters; autonomous vehicles; directive antennas; doppler measurement; intelligent vehicles; machine learning; millimeter wave communication

Share:

 Select Country/Language

Global - English China - 中文