High-speed Optical OFDM transmission by reducing the nonlinearity of LEDs in Visible light Communication Systems

The non-linearity of Light Emitting Diodes (LEDs) has limited the efficiency of visible Light Communication (VLC) in terms of Bit Error Rate (BER). In this report, we propose a model-driven Deep Learning (DL) strategy for Optical Orthogonal Frequency Division Multiplexing (O-OFDM)-based VLC processe...

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Veröffentlicht in:Multimedia tools and applications Jg. 83; H. 16; S. 47353 - 47371
Hauptverfasser: Swaminathan, S., Raajan, N. R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.05.2024
Springer Nature B.V
Schlagworte:
Algorithms
Binary data
Bit error rate
Communication
Communications systems
Computer Communication Networks
Computer Science
Cost function
Data Structures and Information Theory
Demodulation
Efficiency
Emitters
Fast Fourier transformations
Fourier transforms
Light emitting diodes
Machine learning
Multimedia Information Systems
Nonlinearity
Optical communication
Orthogonal Frequency Division Multiplexing
Performance enhancement
Performance measurement
Power consumption
Recurrent neural networks
Signal detection
Special Purpose and Application-Based Systems
Signal Detection
Transmitter Side
Nonlinearity
De-Mapping
I-Q Symbols
Binary Data
ISSN:1573-7721, 1380-7501, 1573-7721
Online-Zugang:Volltext
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Zusammenfassung:The non-linearity of Light Emitting Diodes (LEDs) has limited the efficiency of visible Light Communication (VLC) in terms of Bit Error Rate (BER). In this report, we propose a model-driven Deep Learning (DL) strategy for Optical Orthogonal Frequency Division Multiplexing (O-OFDM)-based VLC processes, that is employed as the Auto Encoder (AE) network system to minimize the LED non-linearity. The suggested system successfully includes communication domain knowledge into the design of the training cost function and network architecture, as opposed to the conventional fully computer-controlled autoencoder. Deep Recurrent Neural Network (Deep RNN) and Inverse Fast Fourier Transform (IFFT) are used at the emitter end to convert the binary data first into challenging I-Q codes for each O- OFDM sub-band. Then, for nonlinearity compensation and signal detection at the receiver, the symbol de-mapping is done and the demodulation is performed through a Deep RNN. DeepRNN's hidden layers are fine-tuned using the MMRFA algorithm for enhanced performance and efficiency. The proposed methodology performance is compared with the existing methods using performance metrics like BER, MSE, ACF, Power consumption, and Energy efficiency. The proposed solution outperforms several current approaches in terms of BER performances and improves operating duration, demonstrating the practicality and promise of DL in the VLC platform.
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-023-17211-x