Millimeter Wave V2V Beam Tracking using Radar:
Algorithms and Real-World Demonstration

Accepted to the European Signal Processing Conference (EUSIPCO), 2023

Hao Luo, Umut Demirhan, Ahmed Alkhateeb

Wireless Intelligence Lab, ASU

This figure presents the adopted V2V communication scenario, where the system model consists of a transmitter vehicle and a receiver vehicle. The transmitter employs a single omnidirectional antenna, while the receiver is equipped with four pairs of mmWave antenna arrays and FMCW radars in four separate directions. The receiver vehicle leverages the radar measurements to predict the optimal beam that communicates with the transmitter vehicle.

Abstract

Radar sensing applications for assisting communication have attracted interest thanks to their potential in dynamic environments. A particularly interesting problem for this approach appears in the vehicle-to-vehicle (V2V) millimeter wave and terahertz communication scenarios, where the narrow beams change with the movement of both vehicles. To address this problem, in this work, we develop a radar-aided beam-tracking scheme, where a single initial beam and a set of radar measurements over a period of time are utilized to predict the beams after this time duration. Within this framework, we develop two approaches with the combination of various degrees of radar signal processing and machine learning. To evaluate the feasibility of the solutions in a realistic scenario, we test their performance on a real-world V2V dataset. Our results indicated the importance of high angular resolution radar for this task and affirmed the potential of using radar for the V2V beam management problems.

Proposed Solution

This figure presents the proposed end-to-end approach for radar-aided beam tracking. The range-Doppler maps,  which are extracted from the radar measurements, are fed to the shown architecture. Before the beam prediction, the previous optimal beam index is combined with the output of the LSTM layers to provide the historical information of the transmitter.

Video Presentation

Reproducing the Results

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. 

Scenarios

This figure presents the overview of the DeepSense 6G V2V testbed. The image on the left depicts the setup of the testbed, where mmWave antenna arrays and radars are placed on top of a vehicle (Unit 1). Four sets of mmWave receivers and radars are directed toward four sides of the box. The image on the right shows a sample from the data collection, where the transmitter vehicle (Unit 2) is equipped with an omnidirectional antenna.

Citation

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

H.Luo, U. Demirhan and A. Alkhateeb, “Millimeter Wave V2V Beam Tracking using Radar: Algorithms and Real-World Demonstration”, accepted to 2023 31st European Signal Processing Conference (EUSIPCO).

@Article{Luo2023,
author = {Luo, H. and Demirhan, U. and Alkhateeb, A.},
title = {Millimeter Wave V2V Beam Tracking using Radar: Algorithms and Real-World Demonstration},
journal={accepted to 2023 31st European Signal Processing Conference (EUSIPCO)},
year = {2023}}

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 Dataset,” in IEEE Communications Magazine, doi: 10.1109/MCOM.006.2200730.

@Article{DeepSense,
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={IEEE Communications Magazine},
year = {2023},
pages={1-7},
doi={10.1109/MCOM.006.2200730}}