Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment

Abstract

Efficient beam alignment is a crucial component in millimeter wave systems with analog beamforming, especially in fast-changing vehicular settings. This paper proposes a positionaided approach where the vehicle's position (e.g., available via GPS) is used to query the multipath fingerprint database, which provides prior knowledge of potential pointing directions for reliable beam alignment. The approach is the inverse of fingerprinting localization, where the measured multipath signature is compared to the fingerprint database to retrieve the most likely position. The power loss probability is introduced as a metric to quantify misalignment accuracy and is used for optimizing candidate beam selection. Two candidate beam selection methods are developed, where one is a heuristic while the other minimizes the misalignment probability. The proposed beam alignment is evaluated using realistic channels generated from a commercial ray-tracing simulator. Using the generated channels, an extensive investigation is provided, which includes the required measurement sample size to build an effective fingerprint, the impact of measurement noise, the sensitivity to changes in traffic density, and beam alignment overhead comparison with IEEE 802.11ad as the baseline. Using the concept of beam coherence time, which is the duration between two consecutive beam alignments, and parameters of IEEE 802.11ad, the overhead is compared in the mobility context. The results show that while the proposed approach provides increasing rates with larger antenna arrays, IEEE 802.11ad has decreasing rates due to the larger beam training overhead that eats up a large portion of the beam coherence time, which becomes shorter with increasing mobility.

Channel Fingerprint Based Beam Tracking for Millimeter Wave Communications

“…On one hand, its forward mapping, from channel gains to user locations, is widely used in localization methods [8]. On the other hand, its reverse mapping, from user locations to channel gains, is considered to guide the beam alignment [9], which can greatly improve the beamforming performance, especially on the corner of obstacles. However, the location information used in these references requires external localization equipments such as GPS devices, which are costly and power-consuming .…”

Evaluation of Simplified Methodology for Obtaining mmWave MIMO Channels from Ray-Tracing Simulations

“…The work in [1] gives a brief introduction on virtual representation of MIMO channels for studying signal processing techniques on mmWaves. In [4] and [5] the narrowband virtual channel version is used to generate a database for vehicle to infrastructure (V2I) environment which was applied to a machine learning method for beam selection on the first article and used on the second for beam alignment through multipath fingerprint.…”

LIDAR Data for Deep Learning-Based mmWave Beam-Selection

“…The benefit of a radar located in infrastructure was investigated in [13]. The use of position information in V2I mmWave was studied in [1]- [7]. Some work using position, targeted only line-of-sight (LOS) situations [3], [5], [6].…”

“…Non-LOS (NLOS) was investigated in [4], [7] with measurement fingerprint databases. Prior work has established that position information can reduce mmWave beam-selection overheads, and that machine learning (ML) is a good tool for this problem.…”

“…We also use DL because of its promising results for position-based beam-selection [17] and many other domains [14], [15]. An advantage of our approach versus [1]- [7] is that our distributed architecture does not depend on the penetration rate of connected vehicles, as it only uses the LIDAR of the connecting vehicle.…”