# Integrated Sensing and Communications Towards Proactive Beamforming in mmWave V2I via Multi-Modal Feature Fusion (MMFF)

arXiv: https://arxiv.org/abs/2310.02561 · HTML: https://mlsanity.com/paper/2310.02561/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2023.

## Built on by (most cited first, 7 of 7)

- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)
- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics](https://mlsanity.com/paper/2408.13546/) (2024)
- [Doubly-Dynamic ISAC Precoding for Vehicular Networks: A Constrained Deep Reinforcement Learning (CDRL) Approach](https://mlsanity.com/paper/2405.14347/) (2024)
- [Multi-Modal Sensing Aided mmWave Beamforming for V2V Communications with Transformers](https://mlsanity.com/paper/2509.11112/) (2025)
- [Synesthesia of Machines (SoM)-Enhanced Sub-THz ISAC Transmission for Air-Ground Network](https://mlsanity.com/paper/2506.12831/) (2025)
- [Agentic AI for Embodied-enhanced Beam Prediction in Low-Altitude Economy Networks](https://mlsanity.com/paper/2603.11392/) (2026)

## Abstract (arXiv)

The future of vehicular communication networks relies on mmWave massive multi-input-multi-output antenna arrays for intensive data transfer and massive vehicle access. However, reliable vehicle-to-infrastructure links require exact alignment between the narrow beams, which traditionally involves excessive signaling overhead. To address this issue, we propose a novel proactive beamforming scheme that integrates multi-modal sensing and communications via Multi-Modal Feature Fusion Network (MMFF-Net), which is composed of multiple neural network components with distinct functions. Unlike existing methods that rely solely on communication processing, our approach obtains comprehensive environmental features to improve beam alignment accuracy. We verify our scheme on the Vision-Wireless (ViWi) dataset, which we enriched with realistic vehicle drifting behavior. Our proposed MMFF-Net achieves more accurate and stable angle prediction, which in turn increases the achievable rates and reduces the communication system outage probability. Even in complex dynamic scenarios with adverse environment conditions, robust prediction results can be guaranteed, demonstrating the feasibility and practicality of the proposed proactive beamforming approach.