Imperial College London
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ProfessorDenizGunduz

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor in Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 6218d.gunduz Website

 
 
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Assistant

 

Ms Joan O'Brien +44 (0)20 7594 6316

 
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Location

 

1016Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Mashhadi:2021:10.1109/LWC.2021.3099136,
author = {Mashhadi, MB and Jankowski, M and Tung, T-Y and Kobus, S and Gunduz, D},
doi = {10.1109/LWC.2021.3099136},
journal = {IEEE Wireless Communications Letters},
pages = {2269--2273},
title = {Federated mmWave beam selection utilizing LIDAR data},
url = {http://dx.doi.org/10.1109/LWC.2021.3099136},
volume = {10},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection. For vehicle-to-infrastructure (V2I) networks, side information from LIDAR sensors mounted on the vehicles has been leveraged to reduce the beam search overhead. In this letter, we propose a federated LIDAR aided beam selection method for V2I mmWave communication systems. In the proposed scheme, connected vehicles collaborate to train a shared neural network (NN) on their locally available LIDAR data during normal operation of the system. We also propose a reduced-complexity convolutional NN (CNN) classifier architecture and LIDAR preprocessing, which significantly outperforms previous works in terms of both the performance and the complexity.
AU  - Mashhadi,MB
AU  - Jankowski,M
AU  - Tung,T-Y
AU  - Kobus,S
AU  - Gunduz,D
DO  - 10.1109/LWC.2021.3099136
EP  - 2273
PY  - 2021///
SN  - 2162-2337
SP  - 2269
TI  - Federated mmWave beam selection utilizing LIDAR data
T2  - IEEE Wireless Communications Letters
UR  - http://dx.doi.org/10.1109/LWC.2021.3099136
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000704110300039&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/9493715
UR  - http://hdl.handle.net/10044/1/92898
VL  - 10
ER  -
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