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

• 作 者: Erich Z?chmann
• 机 构: Christian Doppler Laboratory for Dependable Wireless Connectivity for the Society in Motion, Institu
• 出 处: 中兴通讯技术（英文版）
• 发表日期: 2019年2期
• 关键词: adaptive filters autonomous vehicles directive antennas doppler measure-ment int

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 or-ders of magnitude. This contribution suggests tracking the MPCs geometrically. The de-rived geometric tracker is based on algorithms known as Doppler bearing tracking. A re-cent 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 leing based on their own observed data. By le-ing 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.