On the study of the relation between linear/nonlinear PAPR reduction and transmission performance for OFDM-based VLC systems

Lu, Huimin; Hong, Yang; Chen, Lian-Kuan; Wang, Jianping

doi:10.1364/oe.26.013891

Cited by 17 publications

(8 citation statements)

References 30 publications

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“…However, a high peak to average power ratio (PAPR) is an obvious disadvantage, particularly in ACO-OFDM systems. To lower high PAPR, a Selective Mapping (SLM) [9], a Partial Transmit Sequence (PTS) [10], a genetic algorithm [11], an upper clipping scheme [12], a semi-definite relaxation approach [13] are used. Deep learning can give a new solution to handle this problem because of the vast quantity of data generated during the real functioning of the equipment.…”

Section: Introductionmentioning

confidence: 99%

LSTM-Autoencoder Deep Learning Technique for PAPR Reduction in Visible Light Communication

et al. 2022

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Visible light communication (VLC) is a relatively new wireless communication technology that allows for high data rate transfer. Because of its capability to enable high-speed transmission and eliminate inter-symbol interference, orthogonal frequency division multiplexing (OFDM) is widely employed in VLC. Peak to average power ratio (PAPR) is an issue that impacts the effectiveness of OFDM systems, particularly in VLC systems, because the signal is distorted by the nonlinearity of light-emitting diodes (LEDs). The proposed method Long Short Term Memory-Autoencoder (LSTM-AE) uses an autoencoder as well as an LSTM to learn a compact representation of an input, allowing the model to handle variable length input sequences as well as predict or produce variable length output sequences. This study compares the suggested model with various PAPR reduction strategies to demonstrate that it offers a superior improvement in PAPR reduction of the transmitted signal while maintaining BER. Also, this model provides a flexible compromisation between PAPR and BER.

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Section: Introductionmentioning

confidence: 99%

LSTM-Autoencoder Deep Learning Technique for PAPR Reduction in Visible Light Communication

et al. 2022

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“…However, the peak-to-average power ratio (PAPR) is high in such an OCT precoding MIMO-OFDM system. As the nonlinearity exists when electrical signals are converted into optical signals by the LED, the high PAPR becomes the main factor restricting the system performance [20].…”

Section: Introductionmentioning

confidence: 99%

Experimental Demonstration of Zadoff–Chu Matrix Transform Precoding for MIMO-OFDM Visible Light Communications

Guo

Wang

Wang³

2020

Advances in Condensed Matter Physics

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Visible light communication (VLC) based on a light-emitting diode (LED) is considered to be a potential candidate for the next-generation communication. In this paper, a novel Zadoff–Chu matrix transform (ZCT) precoding is proposed to improve the performance of the traditional space-time block coding- (STBC-) based multiple-input multiple-output and orthogonal frequency division multiplexing (MIMO-OFDM) system. Compared with the existing orthogonal circulant matrix transform (OCT) precoding scheme, the proposed ZCT precoding achieves a much lower peak-to-average power ratio (PAPR) while maintaining the advantage of the uniform signal-to-noise ratio (SNR), which reduces the performance loss caused by LED nonlinearity. To study the system performance further, we set up an experimental demonstration to verify performance improvement under the condition of different driving peak-to-peak voltages (Vpps) and direct current (DC) offsets. Experimental results show that ZCT precoding gains the best bit error rate (BER) performance compared with the traditional and the OCT precoding MIMO-OFDM systems, whose BER is always below the 7% pre-forward error correction (pre-FEC) threshold of 3.8 × 10−3.

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“…employed in VLC system due to its high spectral efficiency and resistance to inter symbol interference (ISI) resulting from the optical diffuse channel [3]. However, the OFDM is characterized by high peak-to-average power ratio (PAPR) thus its performance is more susceptible to the LED nonlinearity, i.e., the characteristics between output optical power and input current [4]. As the LED is driven by the OFDM signal with high PAPR, a large number of nonlinear distortions will be consequently produced [5].…”

Section: Introductionmentioning

confidence: 99%

A Model-Driven Deep Learning Method for LED Nonlinearity Mitigation in OFDM-Based Optical Communications

Miao

Zhu

et al. 2019

IEEE Access

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The nonlinearity of light emitting diodes (LED) has restricted the bit error rate (BER) performance of visible light communications (VLC). In this paper, we propose model-driven deep learning (DL) approach using an autoencoder (AE) network to mitigate the LED nonlinearity for orthogonal frequency division multiplexing (OFDM)-based VLC systems. Different from the conventional fully data-driven AE, the communication domain knowledge is well incorporated in the proposed scheme for the design of network architecture and training cost function. First, a deep neural network (DNN) combined with discrete Fourier transform spreading (DFT-S) is adopted at the transmitter to map the binary data into complex I-Q symbols for each OFDM subcarrier. Then, at the receiver, we divide the symbol demapping module into two subnets in terms of nonlinearity compensation and signal detection, where each subnet is comprised of a DNN. Finally, both the autocorrelation of the learned mapping symbols and the mean square error of demapping symbols are taken into account simultaneously by the cost function for network training. With this approach, the LED nonlinearity and the interference introduced by the multipath channel can be effectively mitigated. The simulation results show that the proposed scheme exhibits better BER performance than some existing methods and further accelerates the training speed, which demonstrates the prospective and validity of DL in the VLC system. INDEX TERMS Deep learning, visible light communication, LED nonlinearity, orthogonal frequency division multiplexing.

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On the study of the relation between linear/nonlinear PAPR reduction and transmission performance for OFDM-based VLC systems

Cited by 17 publications

References 30 publications

LSTM-Autoencoder Deep Learning Technique for PAPR Reduction in Visible Light Communication

LSTM-Autoencoder Deep Learning Technique for PAPR Reduction in Visible Light Communication

Experimental Demonstration of Zadoff–Chu Matrix Transform Precoding for MIMO-OFDM Visible Light Communications

A Model-Driven Deep Learning Method for LED Nonlinearity Mitigation in OFDM-Based Optical Communications

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