University of Oulu

D. Marasinghe et al., "LiDAR aided Wireless Networks - Beam Prediction for 5G," 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), London, United Kingdom, 2022, pp. 1-7, doi: 10.1109/VTC2022-Fall57202.2022.10012751.

LiDAR aided wireless networks : beam prediction for 5G

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Author: Marasinghe, Dileepa1; Jayaweera, Nalin1; Rajatheva, Nandana1;
Organizations: 1Centre for Wireless Communications, University of Oulu, Oulu, Finland
2Nokia Standards, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-03-23


5G New Radio (NR) mmWave operates with narrow beams. Beam-based connections require careful management of beams to ensure a reliable connection, specially when the user has mobility. 5G NR defines beam management procedures to achieve this, at the expense of periodic reporting with increased overheads and resource usage. Concurrently, recent interest in sensing for assisting wireless systems provides an opportunity to extract situational awareness information which can aid in proactive decisions for the network. In this work, we utilize an infrastructure-mounted light detection and ranging (LiDAR) sensor system simultaneously operating with the wireless system to predict future beam decisions. A recurrent neural network (RNN) based learning model is proposed for the beam prediction, employing tracking information of users facilitated by the LiDARs and beam sequence information from the wireless system. Furthermore, a method for predictive beam management with increased periodicity of the reporting mechanism and aperiodic reporting is analyzed. The results for the considered scenario reveal 86.8% of the resources can be saved compared to the conventional beam reporting procedure, while achieving an 88.7% accuracy for optimal beam decisions.

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Series: IEEE Vehicular Technology Conference
ISSN: 1090-3038
ISSN-L: 1090-3038
ISBN: 978-1-6654-5468-1
ISBN Print: 978-1-6654-5469-8
Pages: 1 - 7
Article number: 10012751
DOI: 10.1109/vtc2022-fall57202.2022.10012751
Host publication: 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)
Conference: IEEE Vehicular Technology Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
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