Data Rate Enhancement in Optical Camera Communications Using an Artificial Neural Network Equaliser

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00340234" target="_blank" >RIV/68407700:21230/20:00340234 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ACCESS.2020.2976537" target="_blank" >https://doi.org/10.1109/ACCESS.2020.2976537</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2020.2976537" target="_blank" >10.1109/ACCESS.2020.2976537</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data Rate Enhancement in Optical Camera Communications Using an Artificial Neural Network Equaliser

Popis výsledku v původním jazyce

In optical camera communication (OCC) systems leverage on the use of commercial off-the-shelf image sensors to perceive the spatial and temporal variation of light intensity to enable data transmission. However, the transmission data rate is mainly limited by the exposure time and the frame rate of the camera. In addition, the camera’s sampling will introduce intersymbol interference (ISI), which will degrade the system performance. In this paper, an artificial neural network (ANN)-based equaliser with the adaptive algorithm is employed for the first time in the field of OCC to mitigate ISI and therefore increase the data rate. Unlike other communication systems, training of the ANN network in OCC is done only once in a lifetime for a range of different exposure time and the network can be stored with a look-up table. The proposed system is theoretically investigated and experimentally evaluated. The results record the highest bit rate for OCC using a single LED source and the Manchester line code (MLC) non-return to zero (NRZ) encoded signal. It also demonstrates 2 to 9 times improved bandwidth depending on the exposure times where the system’s bit error rate is below the forward error correction limit.

Název v anglickém jazyce

Data Rate Enhancement in Optical Camera Communications Using an Artificial Neural Network Equaliser

Popis výsledku anglicky

In optical camera communication (OCC) systems leverage on the use of commercial off-the-shelf image sensors to perceive the spatial and temporal variation of light intensity to enable data transmission. However, the transmission data rate is mainly limited by the exposure time and the frame rate of the camera. In addition, the camera’s sampling will introduce intersymbol interference (ISI), which will degrade the system performance. In this paper, an artificial neural network (ANN)-based equaliser with the adaptive algorithm is employed for the first time in the field of OCC to mitigate ISI and therefore increase the data rate. Unlike other communication systems, training of the ANN network in OCC is done only once in a lifetime for a range of different exposure time and the network can be stored with a look-up table. The proposed system is theoretically investigated and experimentally evaluated. The results record the highest bit rate for OCC using a single LED source and the Manchester line code (MLC) non-return to zero (NRZ) encoded signal. It also demonstrates 2 to 9 times improved bandwidth depending on the exposure times where the system’s bit error rate is below the forward error correction limit.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

8

Číslo periodika v rámci svazku

Feb

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

42656-42665

Kód UT WoS článku

000525390500014

EID výsledku v databázi Scopus

2-s2.0-85081561072