A simple ANN-MLP model for estimating 60-GHz PDP inside public and private vehicles
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Date
2023-06-10
Authors
Shukla, Rajeev
Sarkar, Abhishek Narayan
Chandra, Aniruddha
Kelner, Vladimír
Ziółkowski, Cezary
Mikulášek, Tomáš
Prokeš, Aleš
0000-0003-3727-3558Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Nature
Altmetrics
Abstract
Radio wave propagation in an intra-vehicular (IV) environment is markedly different from other well-studied indoor scenarios, such as an office or a factory floor. While millimetre wave (mmWave)-based intra-vehicular communications promise large bandwidth and can achieve ultra-high data rates with lower latency, exploiting the advantages of mmWave communications largely relies on adequately characterising the propagation channel. Channel characterisation is most accurately done through an extensive channel sounding, but due to hardware and environmental constraints, it is impractical to test channel conditions for all possible transmitter and receiver locations. Artificial neural network (ANN)-based channel sounding can overcome this impediment by learning and estimating the channel parameters from the channel environment. We estimate the power delay profile in intra-vehicular public and private vehicle scenarios with a high accuracy using a simple feedforward multi-layer perception-based ANN model. Such artificially generated models can help extrapolate other relevant scenarios for which measurement data are unavailable. The proposed model efficiently matches the taped delay line samples obtained from real-world data, as shown by goodness-of-fit parameters and confusion matrices.
Description
Citation
EURASIP Journal on Wireless Communications and Networking. 2023, vol. 2023, issue 1, p. 1-16.
https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-023-02257-0
https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-023-02257-0
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en