Distributed Radar-aided Vehicle-to-Vehicle Communication

Abstract

Establishing high-rate vehicle-to-vehicle (V2V) links with narrow beamwidth is challenging due to the varying network topology. A too narrow beam may miss the intended receiver, while a too broad beam leads to SNR loss. We propose to harness the high accuracy of radar detections to establish V2V links. In particular, we develop a distributed method where each vehicle associates local radar detections with GPS information communicated by nearby vehicles. The method relies on the transformation of relative to global coordinates, the definition of a suitable metric, and solving an optimal assignment problem. We demonstrate that the proposed approach avoids time-consuming channel estimation and provides high SNR, under the condition that reliable relative and absolute location information is present.

Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond

“…To guarantee the communication QoS for latency-critical applications such as V2X networks, the beam training overhead needs to be reduced to the minimum, which motivates research on sensor-aided beam alignment. Indeed, by leveraging the prior information provided by the sensors, such as GNSS, radar, lidar, and cameras, the search space of the beams can be narrowed down [180]. It has been shown in [181] that, for a V2I communication system with 64×16 = 1024 beam pairs, the search space can be reduced to 475 beam pairs through the use of the positioning information generated by the GPS, and to 32 beam pairs with the help of radar-based positioning, both of which attain the same accuracy compared to the exhaustive search method.…”

Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond

“…To guarantee the communication QoS for latency-critical applications such as V2X networks, the beam training overhead needs to be reduced to the minimum, which motivates research on sensor-aided beam alignment. Indeed, by leveraging the prior information provided by the sensors, such as GNSS, radar, lidar, and camera, the search space of the beams can be narrowed down [164]. It has been shown in [165] that, for a V2I communication system with 64×16 = 1024 beam pairs, the search space can be reduced to 475 beam pairs through the use of the positioning information generated by the GPS, and to 32 beam pairs with the help of the radarbased positioning, both of which attain the same accuracy compared to the exhaustive search method.…”

Channel Estimation for 6G V2X Hybrid Systems Using Multi-Vehicular Learning

“…To the best of the authors' knowledge, the most relevant works in this direction are [6]- [8]. The authors of [6], [7] focus on sensing-aided vehicular communications and tackle the problem of finding the correct association among vehicle ID and detected targets using radar and GPS data. The association is carried out by adopting the Kullback-Leibler divergence as a similarity metric to solve a constrained data association problem.…”