Radar-assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing
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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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 4; 2025: 4; 2026: 2.
11 papers.
- 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
- 2025A Wireless Foundation Model for Multi-Task Predictioncited by 10
- 2024Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication Systemcited by 2
- 2024Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamicscited by 2
- 2024Deep-Learning-Based Channel Estimation for IRS-Assisted ISAC Systemcited by 1
- 2024Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC Systemcited by 1
- 2025Energy-Efficient Learning-Based Beamforming for ISAC-Enabled V2X Networkscited by 1
- 2026QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networkscited by 1
- 2025Multi-User Beamforming with Deep Reinforcement Learning in Sensing-Aided Communicationcited by 0
- 2025Pilot Contamination-Aware Graph Attention Network for Power Control in CFmMIMOcited by 0
- 2026Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networkscited by 0
- 2026Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks4 Aug 2026
- 2026QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks1 Jul 2026
- 2025Energy-Efficient Learning-Based Beamforming for ISAC-Enabled V2X Networks27 Aug 2025
- 2025A Wireless Foundation Model for Multi-Task Prediction8 Jul 2025
- 2025Pilot Contamination-Aware Graph Attention Network for Power Control in CFmMIMO1 Jun 2025
- 2025Multi-User Beamforming with Deep Reinforcement Learning in Sensing-Aided Communication9 May 2025
- 2024Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics24 Aug 2024
- 2024Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication System29 Jan 2024
- 2024Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System29 Jan 2024
- 2024Deep-Learning-Based Channel Estimation for IRS-Assisted ISAC System29 Jan 2024
- 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications24 Nov 2021