مرکز منطقه ای اطلاع رسانی علوم و فناوری Journal of Information Systems and Telecommunication (JIST) 2322-1437 13 50 2025 7 26 Transmission Parameter-based Demodulation in Visible Light Communications using Deep Learning Transmission Parameter-based Demodulation in Visible Light Communications using Deep Learning 122 129 10.61882/jist.47404.13.50.122 en Sarah Ayashm ارومیه Seyed Sadra Kashef Tarbiat Modares University Morteza Valizadeh دانشگاه ارومیه Hasti Akhavan Urmia University 2024 7 18 <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> http://jist.ir/ar/Article/Download/47404