# Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration

arXiv: https://arxiv.org/abs/2111.09676 · HTML: https://mlsanity.com/paper/2111.09676/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2021.

## Built on by (most cited first, 17 of 17)

- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction](https://mlsanity.com/paper/2309.11811/) (2023)
- [Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios](https://mlsanity.com/paper/2308.06868/) (2023)
- [BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models](https://mlsanity.com/paper/2503.10432/) (2025)
- [DeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset](https://mlsanity.com/paper/2406.17908/) (2024)
- [M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models](https://mlsanity.com/paper/2506.14532/) (2025)
- [Vehicle Cameras Guide mmWave Beams: Approach and Real-World V2V Demonstration](https://mlsanity.com/paper/2308.10362/) (2023)
- [Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning](https://mlsanity.com/paper/2402.01259/) (2024)
- [Generative AI Empowered LiDAR Point Cloud Generation with Multimodal Transformer](https://mlsanity.com/paper/2406.18542/) (2024)
- [Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks](https://mlsanity.com/paper/2501.17883/) (2025)
- [Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning](https://mlsanity.com/paper/2503.08937/) (2025)
- [Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems](https://mlsanity.com/paper/2507.14180/) (2025)
- [Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction](https://mlsanity.com/paper/2211.07569/) (2022)
- [Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems](https://mlsanity.com/paper/2401.12801/) (2024)
- [CAR-BRAINet: Sub-6GHz Aided Spatial Adaptive Beam Prediction with Multi Head Attention for Heterogeneous Vehicular Networks](https://mlsanity.com/paper/2509.10508/) (2025)
- [Look Once, Beam Twice: Camera-Primed Real-Time Double-Directional mmWave Beam Management for Vehicular Connectivity](https://mlsanity.com/paper/2605.05071/) (2026)

## Abstract (arXiv)

This paper presents the first machine learning based real-world demonstration for radar-aided beam prediction in a practical vehicular communication scenario. Leveraging radar sensory data at the communication terminals provides important awareness about the transmitter/receiver locations and the surrounding environment. This awareness could be utilized to reduce or even eliminate the beam training overhead in millimeter wave (mmWave) and sub-terahertz (THz) MIMO communication systems, which enables a wide range of highly-mobile low-latency applications. In this paper, we develop deep learning based radar-aided beam prediction approaches for mmWave/sub-THz systems. The developed solutions leverage domain knowledge for radar signal processing to extract the relevant features fed to the learning models. This optimizes their performance, complexity, and inference time. The proposed radar-aided beam prediction solutions are evaluated using the large-scale real-world dataset DeepSense 6G, which comprises co-existing mmWave beam training and radar measurements. In addition to completely eliminating the radar/communication calibration overhead, the experimental results showed that the proposed algorithms are able to achieve around 90% top-5 beam prediction accuracy while saving 93% of the beam training overhead. This highlights a promising direction for addressing the beam management overhead challenges in mmWave/THz communication systems.