LiDAR aided wireless networks : beam prediction for 5G

Marasinghe, Dileepa; Jayaweera, Nalin; Rajatheva, Nandana; Hakola, Sami; Koskela, Timo; Tervo, Oskari; Karjalainen, Juha; Tiirola, Esa; Hulkkonen, Jari (2023-01-18)
 
Avaa tiedosto
Lataukset: 

URL:
https://doi.org/10.1109/VTC2022-Fall57202.2022.10012751

Marasinghe, Dileepa
Jayaweera, Nalin
Rajatheva, Nandana
Hakola, Sami
Koskela, Timo
Tervo, Oskari
Karjalainen, Juha
Tiirola, Esa
Hulkkonen, Jari
Institute of Electrical and Electronics Engineers
18.01.2023

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.

https://rightsstatements.org/vocab/InC/1.0/
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1109/vtc2022-fall57202.2022.10012751
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2023032332978
Tiivistelmä

Abstract

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.

Kokoelmat