# Radar-assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing

arXiv: https://arxiv.org/abs/2001.09306 · HTML: https://mlsanity.com/paper/2001.09306/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2020.

## Built on by (most cited first, 12 of 12)

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- [Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics](https://mlsanity.com/paper/2408.13546/) (2024)
- [Deep-Learning-Based Channel Estimation for IRS-Assisted ISAC System](https://mlsanity.com/paper/2402.09439/) (2024)
- [Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System](https://mlsanity.com/paper/2402.09440/) (2024)
- [Energy-Efficient Learning-Based Beamforming for ISAC-Enabled V2X Networks](https://mlsanity.com/paper/2508.19566/) (2025)
- [QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks](https://mlsanity.com/paper/2607.00974/) (2026)
- [Multi-User Beamforming with Deep Reinforcement Learning in Sensing-Aided Communication](https://mlsanity.com/paper/2505.05956/) (2025)
- [Pilot Contamination-Aware Graph Attention Network for Power Control in CFmMIMO](https://mlsanity.com/paper/2506.00967/) (2025)
- [Deep Reinforcement Learning for Adaptive Power Allocation in ISAC Systems with Mobile Target](https://mlsanity.com/paper/2606.12078/) (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)

In vehicular networks of the future, sensing and communication functionalities will be intertwined. In this paper, we investigate a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by exploiting the dual-functional radar-communication (DFRC) technique. Aiming for realizing joint sensing and communication functionalities at road side units (RSUs), we present a novel extended Kalman filtering (EKF) framework to track and predict kinematic parameters of each vehicle. By exploiting the radar functionality of the RSU we show that the communication beam tracking overheads can be drastically reduced. To improve the sensing accuracy while guaranteeing the downlink communication sum-rate, we further propose a power allocation scheme for multiple vehicles. Numerical results have shown that the proposed DFRC based beam tracking approach significantly outperforms the communication-only feedback based technique in the tracking performance. Furthermore, the designed power allocation method is able to achieve a favorable performance trade-off between sensing and communication.