# Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach

arXiv: https://arxiv.org/abs/2005.07698 · HTML: https://mlsanity.com/paper/2005.07698/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2020.

## Built on by (most cited first, 11 of 11)

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- [Hybrid Mamba-Attention Neural Architecture for Channel Estimation](https://mlsanity.com/paper/2601.17108/) (2026)
- [Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks](https://mlsanity.com/paper/2608.03319/) (2026)

## Abstract (arXiv)

The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side units estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields near optimal solution to the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links.}With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle-based methods.