Imperial College London
Portrait Picture

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{Zecchin:2022:10.1109/TVT.2022.3142513,
author = {Zecchin, M and Mashhadi, MB and Jankowski, M and Gunduz, D and Kountouris, M and Gesbert, D},
doi = {10.1109/TVT.2022.3142513},
journal = {IEEE Transactions on Vehicular Technology},
pages = {2979--2990},
title = {LIDAR and position-aided mmWave beam selection with non-local CNNs and curriculum training},
url = {http://dx.doi.org/10.1109/TVT.2022.3142513},
volume = {71},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility. To reduce the search overhead of iterative beam discovery procedures, contextual information from light detection and ranging (LIDAR) sensors mounted on vehicles has been leveraged by data-driven methods to produce useful side information. In this paper, we propose a lightweight neural network (NN) architecture along with the corresponding LIDAR preprocessing, which significantly outperforms previous works. Our solution comprises multiple novelties that improve both the convergence speed and the final accuracy of the model. In particular, we define a novel loss function inspired by the knowledge distillation idea, introduce a curriculum training approach exploiting line-of-sight (LOS)/non-line-of-sight (NLOS) information, and we propose a non-local attention module to improve the performance for the more challenging NLOS cases. Simulation results on benchmark datasets show that, utilizing solely LIDAR data and the receiver position, our NN-based beam selection scheme can achieve 79.9% throughput of an exhaustive beam sweeping approach without any beam search overhead and 95% by searching among as few as 6 beams. In a typical mmWave V2I scenario, our proposed method considerably reduces the beam search time required to achieve a desired throughput, in comparison with the inverse fingerprinting and hierarchical beam selection schemes.
AU  - Zecchin,M
AU  - Mashhadi,MB
AU  - Jankowski,M
AU  - Gunduz,D
AU  - Kountouris,M
AU  - Gesbert,D
DO  - 10.1109/TVT.2022.3142513
EP  - 2990
PY  - 2022///
SN  - 0018-9545
SP  - 2979
TI  - LIDAR and position-aided mmWave beam selection with non-local CNNs and curriculum training
T2  - IEEE Transactions on Vehicular Technology
UR  - http://dx.doi.org/10.1109/TVT.2022.3142513
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000769985100062&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://ieeexplore.ieee.org/document/9681382
UR  - http://hdl.handle.net/10044/1/101296
VL  - 71
ER  -
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