# Deep Learning-based Link Configuration for Radar-aided Multiuser mmWave Vehicle-to-Infrastructure Communication

arXiv: https://arxiv.org/abs/2201.04657 · HTML: https://mlsanity.com/paper/2201.04657/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2022.

## Built on by (most cited first, 6 of 6)

- [Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning](https://mlsanity.com/paper/2402.01259/) (2024)
- [Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning](https://mlsanity.com/paper/2504.06173/) (2025)
- [Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection](https://mlsanity.com/paper/2404.10936/) (2024)
- [Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images](https://mlsanity.com/paper/2501.18618/) (2025)
- [Heterogeneity-agnostic AI/ML-assisted beam selection for multi-panel arrays](https://mlsanity.com/paper/2602.18678/) (2026)
- [Deep Reinforcement Learning for Adaptive Power Allocation in ISAC Systems with Mobile Target](https://mlsanity.com/paper/2606.12078/) (2026)

## Abstract (arXiv)

Configuring millimeter wave links following a conventional beam training protocol, as the one proposed in the current cellular standard, introduces a large communication overhead, specially relevant in vehicular systems, where the channels are highly dynamic. In this paper, we propose the use of a passive radar array to sense automotive radar transmissions coming from multiple vehicles on the road, and a radar processing chain that provides information about a reduced set of candidate beams for the links between the road-infrastructure and each one of the vehicles. This prior information can be later leveraged by the beam training protocol to significantly reduce overhead. The radar processing chain estimates both the timing and chirp rates of the radar signals, isolates the individual signals by filtering out interfering radar chirps, and estimates the spatial covariance of each individual radar transmission. Then, a deep network is used to translate features of these radar spatial covariances into features of the communication spatial covariances, by learning the intricate mapping between radar and communication channels, in both line-of-sight and non-line-of-sight settings. The communication rates and outage probabilities of this approach are compared against exhaustive search and pure radar-aided beam training methods (without deep learning-based mapping), and evaluated on multi-user channels simulated by ray tracing. Results show that: (i) the proposed processing chain can reliably isolate the spatial covariances for individual radars, and (ii) the radar-to-communications translation strategy based on deep learning provides a significant improvement over pure radar-aided methods in both LOS and NLOS channels.