Machine Learning-Assisted Receiver Design for Visible Light Communication Systems

Mustafa Kemal Şahan; Mehmet Sonmez

Makale detayları

Machine Learning-Assisted Receiver Design for Visible Light Communication Systems

Yazarlar:

Mustafa Kemal Şahan | Mehmet Sonmez

Yayıncı:

Fırat University

Ana Başlıklar:

Communications Engineering (Other)

Dergi:

Turkish Journal of Science and Technology Vol.20 Issue 2, pp.573-581

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001357989

pISSN:

1308-9080

Anahtar kelimeler:

Özet:

 Visible Light Communication VLC Systems have been attracted considerable attention from many researchers, papers, and projects due to their energy consumption efficiency. Hence, this paper focuses on VLC-based receiver scheme that is designed by using a machine learning algorithm to provide a sustainable demodulator structure with regardless to received signal format. To provide data transmission, the communication system uses SPAM Superposed Pulse Amplitude Modulation scheme which requires the use of threshold values to detect data bits. Hence, the receiver unit consists of a K-Nearest Neighbors KNN algorithm-based method to determine variable threshold value by using a training data set. An architecture has been enhanced to implement in Field Programmable Gate Arrays FPGA board. To integrate the machine learning based system into SPAM receiver model, a mathematical framework is improved to obtain the digital design techniques. Therefore, both a theoretical analysis and a digital design-based receiver system have been proposed in the paper. Addition to these, an Additive White Gaussian Noise AWGN channel model, which is designed by using Linear Feedback Shift Register LFSR is improved to observe performance of the Machine Learning based System related to the difference between transmitted and received bits.

 Visible Light Communication VLC Systems have been attracted considerable attention from many researchers, papers, and projects due to their energy consumption efficiency. Hence, this paper focuses on VLC-based receiver scheme that is designed by using a machine learning algorithm to provide a sustainable demodulator structure with regardless to received signal format. To provide data transmission, the communication system uses SPAM Superposed Pulse Amplitude Modulation scheme which requires the use of threshold values to detect data bits. Hence, the receiver unit consists of a K-Nearest Neighbors KNN algorithm-based method to determine variable threshold value by using a training data set. An architecture has been enhanced to implement in Field Programmable Gate Arrays FPGA board. To integrate the machine learning based system into SPAM receiver model, a mathematical framework is improved to obtain the digital design techniques. Therefore, both a theoretical analysis and a digital design-based receiver system have been proposed in the paper. Addition to these, an Additive White Gaussian Noise AWGN channel model, which is designed by using Linear Feedback Shift Register LFSR is improved to observe performance of the Machine Learning based System related to the difference between transmitted and received bits.

 Visible Light Communication VLC Systems have been attracted considerable attention from many researchers, papers, and projects due to their energy consumption efficiency. Hence, this paper focuses on VLC-based receiver scheme that is designed by using a machine learning algorithm to provide a sustainable demodulator structure with regardless to received signal format. To provide data transmission, the communication system uses SPAM Superposed Pulse Amplitude Modulation scheme which requires the use of threshold values to detect data bits. Hence, the receiver unit consists of a K-Nearest Neighbors KNN algorithm-based method to determine variable threshold value by using a training data set. An architecture has been enhanced to implement in Field Programmable Gate Arrays FPGA board. To integrate the machine learning based system into SPAM receiver model, a mathematical framework is improved to obtain the digital design techniques. Therefore, both a theoretical analysis and a digital design-based receiver system have been proposed in the paper. Addition to these, an Additive White Gaussian Noise AWGN channel model, which is designed by using Linear Feedback Shift Register LFSR is improved to observe performance of the Machine Learning based System related to the difference between transmitted and received bits.

 Görünür Işık Haberleşme GIH Sistemleri, düşük enerji tüketim verimliliği nedeniyle birçok araştırmacı, çalışma ve projeden büyük ilgi toplamıştır. Bu nedenle; bu makale, alınan sinyal biçimine bakılmaksızın sürdürülebilir bir demodülatör yapısı sağlamak için bir makine öğrenme algoritması kullanılarak tasarlanan VLC tabanlı alıcı şemasına odaklanmaktadır. Veri iletiminin sağlanması için haberleşme sistemi, veri bitlerini algılamak için eşik değeri kullanımını gerektiren SPAM Üst-üste Konumlandırılmış Darbe Genlik Modülasyonu şemasını kullanmaktadır. Bu nedenle, alıcı birim, eğitim veri setini kullanarak değişken eşik değerini belirlemek için K-En Yakın Komşular KNN algoritması tabanlı bir yöntemden oluşmuştur. Önerilen yöntemin Alanda Programlanabilir Kapı Dizileri FPGA kartında uygulanabilmesi için bir mimari geliştirilmiştir. Makine öğrenimi tabanlı sistemi SPAM alıcı modeline entegre etmek için, dijital tasarım tekniklerini elde etmek amacıyla matematiksel bir çerçeve geliştirilmiştir. Bu nedenle, makalede hem teorik bir analiz hem de sayısal tasarım tabanlı bir alıcı sistemi önerilmiştir. Bunlara ek olarak, iletilen ve alınan bitler arasındaki fark ile ilgili makine öğrenmesi tabanlı sistemin performansını gözlemlemek için Doğrusal Geri Beslemeli Kaydırma Kaydı LFSR kullanılarak tasarlanan Eklemeli Beyaz Gauss Gürültüsü AWGN kanal modeli geliştirilmiştir.

Makale detayları

Machine Learning-Assisted Receiver Design for Visible Light Communication Systems

Yazarlar:

Mustafa Kemal Şahan | Mehmet Sonmez

Yayıncı:

Fırat University

Ana Başlıklar:

Communications Engineering (Other)

Dergi:

Turkish Journal of Science and Technology Vol.20 Issue 2, pp.573-581

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001357989

pISSN:

1308-9080

FPGA

machine learning

receiver

VLCFPGA

makine öğrenmesi

alıcı

VLC.

Sayı

Makale detayları

Yazarlar: Mustafa Kemal Şahan | Mehmet Sonmez

Yayıncı: Fırat University

Ana Başlıklar: Communications Engineering (Other)

Dergi: Turkish Journal of Science and Technology Vol.20 Issue 2, pp.573-581

Belge Tipi: Makale

Yayın Tarihi: 9/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001357989

pISSN: 1308-9080

Sayı

Yazarlar:

Mustafa Kemal Şahan | Mehmet Sonmez

Yayıncı:

Fırat University

Ana Başlıklar:

Communications Engineering (Other)

Dergi:

Turkish Journal of Science and Technology Vol.20 Issue 2, pp.573-581

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001357989

pISSN:

1308-9080