# Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning

arXiv: https://arxiv.org/abs/2402.01259 · HTML: https://mlsanity.com/paper/2402.01259/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration](https://mlsanity.com/paper/2111.09676/) (2021)
- [Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network](https://mlsanity.com/paper/2201.04712/) (2022)
- [Computer Vision Aided mmWave Beam Alignment in V2X Communications](https://mlsanity.com/paper/2207.11409/) (2022)
- [An Overview of 5G Advanced Evolution in 3GPP Release 18](https://mlsanity.com/paper/2201.01358/) (2022)
- [Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?](https://mlsanity.com/paper/2205.09054/) (2022)
- [Deep Learning-based Link Configuration for Radar-aided Multiuser mmWave Vehicle-to-Infrastructure Communication](https://mlsanity.com/paper/2201.04657/) (2022)
- [A Deep Learning Approach to Location- and Orientation-aided 3D Beam Selection for mmWave Communications](https://mlsanity.com/paper/2110.06859/) (2021)
- [mmWave Enabled Connected Autonomous Vehicles: A Use Case with V2V Cooperative Perception](https://mlsanity.com/paper/2310.09700/) (2023)
- [Location-Aided Beamforming in Mobile Millimeter-Wave Networks](https://mlsanity.com/paper/2205.09887/) (2022)
- [Millimeter Wave V2V Beam Tracking using Radar: Algorithms and Real-World Demonstration](https://mlsanity.com/paper/2308.01558/) (2023)
- [On the Feasibility of Out-of-Band Spatial Channel Information for Millimeter-Wave Beam Search](https://mlsanity.com/paper/2208.02480/) (2022)

## Built on by (most cited first, 3 of 3)

- [Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning](https://mlsanity.com/paper/2504.06173/) (2025)
- [Multi-Modal Sensing Aided mmWave Beamforming for V2V Communications with Transformers](https://mlsanity.com/paper/2509.11112/) (2025)
- [Multi-Modal Sensing and Fusion in mmWave Beamforming for Connected Vehicles: A Transformer Based Framework](https://mlsanity.com/paper/2602.13606/) (2026)

## Abstract (arXiv)

Beamforming techniques are considered as essential parts to compensate the severe path loss in millimeter-wave (mmWave) communications by adopting large antenna arrays and formulating narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over such narrow beams for efficient link configuration by traditional beam selection approaches, mainly relied on channel state information, typically impose significant latency and computing overheads, which is often infeasible in vehicle-to-vehicle (V2V) communications like highly dynamic scenarios. In contrast, utilizing out-of-band contextual information, such as vehicular position information, is a potential alternative to reduce such overheads. In this context, this paper presents a deep learning-based solution on utilizing the vehicular position information for predicting the optimal beams having sufficient mmWave received powers so that the best V2V line-of-sight links can be ensured proactively. After experimental evaluation of the proposed solution on real-world measured mmWave sensing and communications datasets, the results show that the solution can achieve up to 84.58% of received power of link status on average, which confirm a promising solution for beamforming in mmWave at 60 GHz enabled V2V communications.