CNN-LSTM-AM approach for outdoor wireless optical communication systems

This paper introduces the enhancement of Visible Light Communications (VLC) for V2V using artificial intelligence models. Different V2V scenarios are simulated. The first scenario considers a specific longitudinal separation and a variable lateral shift between vehicles. The second scenario assumes...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 32178 - 26
Hauptverfasser:	Abdelsattar, Montaser, Amer, Eman S., Ziedan, Hamdy A., Salama, Wessam M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 01.09.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166 639/166/987 Artificial intelligence Bit error rate (BER) Communication channels Communications systems Convolutional neural network (CNNs) Datasets Deep denoising autoencoder (DDAE) Deep learning Gated recurrent unit (GRU) Generative adversarial network (GAN) Humanities and Social Sciences Intelligent systems multidisciplinary Neural networks Power consumption Roads & highways Science Science (multidisciplinary) Transmitters Vehicles Deep learning Convolutional neural network (CNNs) Deep denoising autoencoder (DDAE) Generative adversarial network (GAN) Gated recurrent unit (GRU) Bit error rate (BER)
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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