Integrated Sensing and Communication-Enabled Predictive Beamforming With Deep Learning in Vehicular Networks

Accurate beam tracking in high-mobility vehicular networks has become a key issue for reliable communication link establishment. Recent work on integrated sensing and communication has shown the superiority of the sensing-assisted beam tracking. By estimating the angular parameters of vehicles based...

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Bibliographic Details
Published in:IEEE communications letters 2021-10, Vol.25 (10), p.3301-3304
Main Authors: Mu, Junsheng, Gong, Yi, Zhang, Fangpei, Cui, Yuanhao, Zheng, Feng, Jing, Xiaojun
Format: Article
Language:English
Subjects:
Array signal processing
beam tracking
Beamforming
Communication
Deep learning
Error analysis
Integrated sensing and communication
Networks
Neural networks
Performance prediction
Radar
Radar echoes
Radar tracking
Roadsides
Sensors
Signal to noise ratio
Tracking
Vehicles
vehicular networks
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Summary:Accurate beam tracking in high-mobility vehicular networks has become a key issue for reliable communication link establishment. Recent work on integrated sensing and communication has shown the superiority of the sensing-assisted beam tracking. By estimating the angular parameters of vehicles based on the radar echoes, the roadside unit (RSU) can perform a so-called predictive beamforming to pair the beams with high accuracy as well as low latency. To further reduce the estimation error due to the nonlinear measurements, in this letter, we develop a deep learning-based approach to exploit the dependency between the angles and the measurements, and to obtain the angle estimates. Simulation results show that our proposed approach can improve the estimation performance and maintain reliable communications in high-mobility vehicular networks.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2021.3098748