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

Miao, Pu; Zhu, Bingcheng; Qi, Chenhao; Jin, Yi; Lin, Chong

doi:10.1109/access.2019.2919983

Cited by 28 publications

(20 citation statements)

References 32 publications

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“…However, in the experiment, adaptive postdistortion must be applied to compensate the nonlinear impairements and thus linearize the VLC system. Several linearization approaches have been proposed in previous works for that purpose, including the Volterra series model [19], [20], the memory polynomial model [21], or more recently, machine learning algorithms [22]- [24]. In this study, the second-order Volterra series model is implemented, as it provides good performance at high data rate [25].…”

Section: B Led Nonlinearity and Adaptive Volterra-based Postdistortermentioning

confidence: 99%

Experimental Comparison of PAM and CAP Modulation for Visible Light Communication Under Illumination Constraints

et al. 2022

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In this paper, we study different modulation techniques for visible light communication (VLC), taking illumination constraints into account. Two modulation schemes are compared, namely pulse amplitude modulation (PAM) and carrierless amplitude and phase (CAP) modulation, through both simulations and experimental measurements. The data link under study is based on low-cost components comprising a white light-emitting diode (LED) and a silicon PIN photodiode. Moreover, the proposed VLC system complies with illumination standards and limits the brightness level to that of a typical office room. The impact of the roll-off factor parameter, which is directly related to the total occupied bandwidth, and the maximum achievable throughput for PAM and CAP are studied. Moreover, adaptive postdistortion based on a Volterra series expansion is implemented to mitigate the effects of LED nonlinearity in practice. We also demonstrate that 8-PAM can outperform 64-CAP and discrete multitone (DMT) when the LED nonlinearity is adequately compensated.

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Section: B Led Nonlinearity and Adaptive Volterra-based Postdistortermentioning

confidence: 99%

Experimental Comparison of PAM and CAP Modulation for Visible Light Communication Under Illumination Constraints

et al. 2022

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“…A DL framework is proposed in [35] for the design of a binary signaling transceiver in dimmable VLC, where the optical channel layers and binarization techniques are introduced in DL to reflect the physical and discrete nature of the OOK-based VLC systems. In [36], a model-driven DL approach using an autoencoder (AE) network is proposed to mitigate the LED nonlinearity for DCO-OFDM-based VLC systems. The constellation mapping and de-mapping of the transmitted symbols are adaptively acquired and optimized through the DL technique.…”

Section: Introductionmentioning

confidence: 99%

“…The results show that the proposed scheme exhibits better BER performance than some existing methods, and it can also further accelerate the training speed. Similar to [36], an end-to-end learning AE network is proposed in [37] to address the high PAPR and the LED nonlinearity problems in asymmetrically clipped (AC) O-OFDM. The results show that the hybrid AE achieves a distinct PAPR reduction and is more robustness to LED nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

Study of the Performance of Deep Learning-Based Channel Equalization for Indoor Visible Light Communication Systems

et al. 2021

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The inherent impairments of visible light communication (VLC) in terms of nonlinearity of light-emitting diode (LED) and the optical multipath restrict bit error rate (BER) performance. In this paper, a model-driven deep learning (DL) equalization scheme is proposed to deal with the severe channel impairments. By imitating the block-by-block signal processing block in orthogonal frequency division multiplexing (OFDM) communication, the proposed scheme employs two subnets to replace the signal demodulation module in traditional system for learning the channel nonlinearity and the symbol de-mapping relationship from the training data. In addition, the conventional solution and algorithm are also incorporated into the system architecture to accelerate the convergence speed. After an efficient training, the distorted symbols can be implicitly equalized into the binary bits directly. The results demonstrate that the proposed scheme can address the overall channel impairments efficiently and can recover the original symbols with better BER performance. Moreover, it can still work robustly when the system is complicated by serious distortions and interference, which demonstrates the superiority and validity of the proposed scheme in channel equalization.

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“…In [13], a Gaussian kernel-aide deep neural network is applied for 8-pulse amplitude modulation based signal detection to compensate for the nonlinear distortion in an underwater optical system. To mitigate the high peak-toaverage power ratio (PAPR) of the optical orthogonal frequency division multiplexing (O-OFDM) based visible light communication system, an autoencoder network is proposed with efficient learning and end-to-end performance optimization [14]. Similar autoencoder network framework is proposed for binary signal designing in LED-based visible light communications [15].…”

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confidence: 99%

Deep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications

et al. 2020

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In underwater optical wireless communication (UOWC) systems, using single photon avalanche photondiode (SPAD) as the detector can improve the transmission distance. However, the signal detection for SPAD-based systems is greatly challenged by the complex optical channel characteristics and SPAD nonlinear distortion. To address this issue, a novel deep learning aided signal detection scheme is proposed in this paper. By exploiting the physical mechanism and prior expert knowledge of the signal processing, a two-connected multilayer perception (MLP) network is integrated into the receiver. The first subnetwork is regarded as a channel compensation block while the second one works as a demodulator. With sophisticated numerical optical channel model and SPAD non-Poisson model, large amounts of training data are utilized to train the proposed model offline. Afterwards, the online data are recovered with the trained network. Simulation results verify that significant bit error ratio (BER) improvement can be achieved with the proposed scheme. INDEX TERMS Underwater optical wireless communication, nonlinear distortion, deep learning, multilayer perception, signal detection. I. INTRODUCTION With the application of technologies such as massive multi-input multi-output transmission, millimeter-wave (mm-mave) communication and non-orthogonal multiple access scheme, 5G mobile communication has significantly increased the system capacity and supported massive connections [1]. However, the 5G network is still ground based [2]. Vast communication demand at sea is greatly challenged due to the limited 5G network coverage. Thus, the envisaged 6G network is expected to provide global wireless connectivity from space to underwater. As a complementary technology for terrestrial communication, optical wireless communication, like laser communication or visible light The associate editor coordinating the review of this manuscript and approving it for publication was Zinan Wang .

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

Cited by 28 publications

References 32 publications

Experimental Comparison of PAM and CAP Modulation for Visible Light Communication Under Illumination Constraints

Experimental Comparison of PAM and CAP Modulation for Visible Light Communication Under Illumination Constraints

Study of the Performance of Deep Learning-Based Channel Equalization for Indoor Visible Light Communication Systems

Deep Learning Aided Signal Detection for SPAD-Based Underwater Optical Wireless Communications

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