A simple ANN-MLP model for estimating 60-GHz PDP inside public and private vehicles

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148534" target="_blank" >RIV/00216305:26220/23:PU148534 - isvavai.cz</a>

Výsledek na webu

<a href="https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-023-02257-0" target="_blank" >https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-023-02257-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13638-023-02257-0" target="_blank" >10.1186/s13638-023-02257-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A simple ANN-MLP model for estimating 60-GHz PDP inside public and private vehicles

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A simple ANN-MLP model for estimating 60-GHz PDP inside public and private vehicles

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/GF23-04304L" target="_blank" >GF23-04304L: Vícepásmová predikce šíření milimetrových vln pro dynamické a statické scénáře v členitých časově proměnných prostředích</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

EURASIP Journal on Wireless Communications and Networking

ISSN

1687-1472

e-ISSN

1687-1499

Svazek periodika

2023

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

001002976900001

EID výsledku v databázi Scopus

2-s2.0-85163081070