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

IEEE Wireless Communications and Networking Conference (WCNC) 2022

Umut Demirhan and Ahmed Alkhateeb

Wireless Intelligence Lab, ASU

Given a radar observation of the wireless environment, can the mmWave/sub-THz basestation predict the optimal beam index?
Our machine learning-based solution can leverage such sensing data for efficient mmWave/THz beam prediction.


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.

Proposed Solution

This figure illustrates the proposed machine learning-based beam prediction model that leverages radar observations with the classical radar signal processing steps as the preprocessing to the machine learning model

Video Presentation

DeepSense 6G Dataset

DeepSense 6G is a real-world multi-modal dataset that comprises coexisting multi-modal sensing and communication data, such as mmWave wireless communication, Camera, GPS data, LiDAR, and Radar, collected in realistic wireless environments.  Link to the DeepSense 6G website is provided below. 


In this beam prediction task, we build development/challenge datasets based on the DeepSense data from scenario 9

For further details regarding the scenarios, follow the links provided below. 

Reproducing the Results

Please use the GitHub page for the code and the radar-aided beam prediction task page for the dataset.


If you want to use the dataset or scripts in this page, please cite the following two papers:

U. Demirhan and A. Alkhateeb, “Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration,” 2022 IEEE Wireless Communications and Networking Conference (WCNC)2022, pp. 2655-2660, doi: 10.1109/WCNC51071.2022.9771564.

author={Demirhan, Umut and Alkhateeb, Ahmed},
booktitle={2022 IEEE Wireless Communications and Networking Conference (WCNC)},
title={Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration},

A. Alkhateeb, G. Charan, T. Osman, A. Hredzak, J. Morais, U. Demirhan, and N. Srinivas, “DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Datasets,” to be available on arXiv, 2022. [Online]. Available: https://www.DeepSense6G.net

author = {Alkhateeb, A. and Charan, G. and Osman, T. and Hredzak, A. and Morais, J. and Demirhan, U. and Srinivas, N.},
title = {{DeepSense 6G}: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset},
journal={to be available on arXiv},
year = {2022},
url = {https://www.DeepSense6G.net},}