Detail publikačního výsledku

Deep Learning based Power Delay Profile Trend Generation: A 60 GHz Intra-Vehicle Case Study

SHUKLA, R.; SARKAR, A.; CHANDRA, A.; MIKULÁŠEK, T.; PROKEŠ, A.; JAN M., K.; ZIÓŁKOWSKI, C.

Originální název

Deep Learning based Power Delay Profile Trend Generation: A 60 GHz Intra-Vehicle Case Study

Anglický název

Deep Learning based Power Delay Profile Trend Generation: A 60 GHz Intra-Vehicle Case Study

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

In this article we have utilized deep learning (DL) for channel sounding application in the millimeter wave (mmWave) band. Using data from a channel sounding campaign for studying intra-vehicle wireless channels operating over the 55-65 GHz mmWave band, we have trained an artificial neural network (ANN) model, which is used to simulate power-delay-profile (PDP) trends. The required simulation inputs form a minimal set, only comprising the frequency points, the transmitter-receiver distance and the presence of passengers inside car. The simulated PDP trend shows good match with the measured PDP and can be used for constructing tapped-delay-line (TDL) based channel models.

Anglický abstrakt

In this article we have utilized deep learning (DL) for channel sounding application in the millimeter wave (mmWave) band. Using data from a channel sounding campaign for studying intra-vehicle wireless channels operating over the 55-65 GHz mmWave band, we have trained an artificial neural network (ANN) model, which is used to simulate power-delay-profile (PDP) trends. The required simulation inputs form a minimal set, only comprising the frequency points, the transmitter-receiver distance and the presence of passengers inside car. The simulated PDP trend shows good match with the measured PDP and can be used for constructing tapped-delay-line (TDL) based channel models.

Klíčová slova

mmWave channel sounding; intra-vehicle communication; deep learning; power-delay-profile

Klíčová slova v angličtině

mmWave channel sounding; intra-vehicle communication; deep learning; power-delay-profile

Autoři

SHUKLA, R.; SARKAR, A.; CHANDRA, A.; MIKULÁŠEK, T.; PROKEŠ, A.; JAN M., K.; ZIÓŁKOWSKI, C.

Rok RIV

2023

Vydáno

15.07.2022

Nakladatel

Institute of Electrical and Electronics Engineers Inc.

Místo

Colorado State University

ISBN

9781665496582

Kniha

2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings

Strany od

209

Strany do

210

Strany počet

2

URL

https://ieeexplore.ieee.org/document/9887316

BibTex

@inproceedings{BUT179856,
  author="Rajeev {Shukla} and Abhishek Narayan {Sarkar} and Aniruddha {Chandra} and Tomáš {Mikulášek} and Aleš {Prokeš} and Jan M. {Kelner} and Cezary {Ziółkowski}",
  title="Deep Learning based Power Delay Profile Trend Generation: A 60 GHz Intra-Vehicle Case Study",
  booktitle="2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings",
  year="2022",
  pages="209--210",
  publisher="Institute of Electrical and Electronics Engineers Inc.",
  address="Colorado State University",
  doi="10.1109/AP-S/USNC-URSI47032.2022.9887316",
  isbn="9781665496582",
  url="https://ieeexplore.ieee.org/document/9887316"
}