# Beam Alignment for Millimetre Wave Links with Motion Prediction of Autonomous Vehicles

arXiv: https://arxiv.org/abs/1702.04264 · HTML: https://mlsanity.com/paper/1702.04264/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2017.

## Built on by (most cited first, 2 of 2)

- [Federated mmWave Beam Selection Utilizing LIDAR Data](https://mlsanity.com/paper/2102.02802/) (2021)
- [LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training](https://mlsanity.com/paper/2104.14579/) (2021)

## Abstract (arXiv)

Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays and multi-gigabit-per-second data transmission. Millimetre Waves (mmWaves) communications can fulfil these requirements. However, the increased mobility of Connected and Autonomous Vehicles (CAVs), requires frequent beamforming - thus introducing increased overhead. In this paper, a new beamforming algorithm is proposed able to achieve overhead-free beamforming training. Leveraging from the CAVs sensory data, broadcast with Dedicated Short Range Communications (DSRC) beacons, the position and the motion of a CAV can be estimated and beamform accordingly. To minimise the position errors, an analysis of the distinct error components was presented. The network performance is further enhanced by adapting the antenna beamwidth with respect to the position error. Our algorithm outperforms the legacy IEEE 802.11ad approach proving it a viable solution for the future ITS applications and services.