Mendeley
TY  _  JOUR
ID  -  2024071847404
TI  -  Transmission Parameter-based Demodulation in Visible Light Communications using Deep Learning
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -  Ayashm Sarah
AU  -  Kashef Seyed Sadra
AU  -  ولی زاده مرتضی 
AU  -   
AD  -  
AD  -  Urmia University
AD  -  دانشگاه ارومیه
AD  -  Urmia University	
Y1  -  2025
PY  -  2025
VL  -  50
IS  -  
SP  -  122
EP  -  129
KW  -  Demodulation
KW  -   VLC
KW  -   Distances
KW  -   Convolutional Neural Network
KW  -   ISI
DO  -  
N2  -  <p>This paper proposes an innovative approach by employing a one-dimensional Convolutional Neural Network (CNN) for demodulation in VLC systems. The used Data-set is real and available online, providing a robust foundation for analysis. It encompasses modulated signals in seven different modulation types, with 29 transmission distances ranging from 0 to 140 centimeters. By accounting for the varying distances between the transmitter and receiver, the model can more accurately interpret the received signals. Additionally, the study suggests that utilizing memory to learn previous symbols, which is essential for mitigating the effects of inter-symbol interference (ISI), can significantly improve demodulation accuracy. Our results of memory-based demodulation show a better performance in contrast to the previous one (AdaBoost).</p>
UR  - rimag.ir/en/Article/47404
L1  - rimag.ir/en/Article/Download/47404
ER  -  
