VLCnet: Deep Learning Based End-to-End Visible Light Communication System

Ulkar, Mehmet Görkem; Baykaş, Tunçer; Pusane, Alí Emre

doi:10.1109/jlt.2020.3006827

Cited by 23 publications

(9 citation statements)

References 20 publications

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“…The available research focuses on developing DL-based methods for LED positioning [41], [42], resource allocation [43], [44], LSTM-based techniques for reducing peak-to-averagepower ratio [45], etc. Although, few DL-based receiver architectures are proposed in [46]- [48], to the best of our knowledge, a dedicated DL-based receiver, which bypasses the SIC operations for an indoor MIMO RSMA VLC system is not yet investigated in literature. To this end, we propose ViRSMALNet -a deep learning LSTM based receiver architecture for a VLC system -which aids in mitigating the drawbacks due to SIC.…”

Section: Motivationmentioning

confidence: 99%

ViRSMALNet: A Twin-Tier LSTM-Based Deep Learning Network for Indoor MIMO RSMA VLC Systems

Kowshik,

Gurugopinath,

Muhaidat

2024

IEEE Open J. Commun. Soc.

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In this article, we present a twin-tiered deep learning (DL) symbol detector architecture for a multi-user multiple-input multiple-output (MIMO) visible light communication (VLC) system, based on rate-splitting multiple access (RSMA). The DL architecture exploits long short-term memory (LSTM), by utilizing two identical LSTM-based deep neural networks (DNNs) to concurrently decode the common and private streams, within a multicarrier framework. Termed as VLC with RSMA and LSTM-based DNN (ViRSMALNet), this detector overcomes the limitations in successive interference cancellation (SIC) receivers, and decodes the RSMA messages from the combined signal in a single step. Extensive simulations based on Monte Carlo methods are executed to assess the symbol error rate (SER) performance of ViRSMALNet across diverse modulation schemes. The results establish that ViRSMALNet outperforms SIC-based least squares (LS) and minimum mean square error (MMSE) detectors, and closely attains the performance of the optimal maximum likelihood (ML) detector. Further, this study also investigates the influence of factors such as the number of light-emitting diodes per fixture, the number of photodetectors per user, and the DNNs' hyperparameters on the SER performance. Moreover, the impact of the distance between the PDs on the system's sum rate performance is studied.INDEX TERMS Deep learning (DL), long short-term memory (LSTM), maximum likelihood (ML) detection, multiple-input multiple-output (MIMO), rate-splitting multiple access (RSMA), visible light communications (VLC).

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Section: Motivationmentioning

confidence: 99%

ViRSMALNet: A Twin-Tier LSTM-Based Deep Learning Network for Indoor MIMO RSMA VLC Systems

Kowshik,

Gurugopinath,

Muhaidat

2024

IEEE Open J. Commun. Soc.

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“…Similarly, an AE based on recurrent neural networks was employed for optical IM/DD fiber-based systems in [29]. Additionally, an end-to-end AE-based VLC system was proposed in [30] both for flicker reduction and dimming. An ACO-OFDM-based VLC system using a hybrid AE scheme was advocated in [31] for improving the BER and peak-toaverage power ratio (PAPR) of classical ACO-OFDM, while DL was adopted in multi-colored VLC systems in [32] for reducing the symbol error probability.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Aided Optical IM/DD OFDM Approaches the Throughput of RF-OFDM

Luong

Zhang

Xiang

et al. 2022

IEEE J. Select. Areas Commun.

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Deep learning-aided optical orthogonal frequency division multiplexing (O-OFDM) is proposed for intensity modulated direct detection transmissions, which is termed as O-OFDMNet. In particular, O-OFDMNet employs deep neural networks (DNNs) for converting a complex-valued signal into a non-negative signal in the time-domain at the transmitter and vice versa at the receiver. The associated frequency-domain signal processing remains the same as in conventional radio frequency (RF) OFDM. As a result, our scheme achieves the same spectral efficiency as the RF scheme, which has never been attained by the existing O-OFDM schemes, because they have relied on the Hermitian symmetry of the spectral-domain signal to guarantee that the time-domain signal becomes real-valued. We show that O-OFDMNet can be viewed as an autoencoder architecture, which can be trained in an end-to-end manner in order to simultaneously improve both the bit error ratio (BER) and the peak-to-average power ratio (PAPR) for transmission over both additive white Gaussian noise and frequency-selective channels. Furthermore, we intrinsically integrate a soft-decision aided channel decoder with our O-OFDMNet and investigate its coded performance relying on both convolutional and polar codes. The simulation results show that our scheme improves both the uncoded and coded BER as well as a reducing the PAPR compared to the benchmarks at the cost of a moderate additional DNN complexity. Furthermore, our scheme is capable of approaching the throughput of RF-OFDM, which is notably higher than that of conventional O-OFDM. Finally, our complexity analysis shows that O-OFDMNet is suitable for real-time operation.

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“…As a further advance, Karanov et al [25], [29] studied the AE in the context of optical fiber communications and provided experimental results to prove its performance benefits. Further optical physical layer performance investigations have been carried out by Hao et al [30] and Ulkar et al [31] in the context of ACO-OFDM and other scenarios, respectively.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection

Zhang

Luong

Petropoulos

et al. 2022

J. Lightwave Technol.

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End-to-end learning systems are conceived for Orthogonal Frequency Division Multiplexing (OFDM)-aided optical Intensity Modulation paired with Direct Detection (IM/DD) communications relying on the Autoencoder (AE) architecture in deep learning. We first propose an AE-aided Layered ACO-OFDM (LACO-OFDM) scheme, termed as LACONet, for exploiting the increased bandwidth efficiency of LACO-OFDM. LACONet employs a Neural Network (NN) at the transmitter for bit-tosymbol mapping, and another NN at the receiver for recovering the data bits, which together form an AE and can be trained in an end-to-end manner for simultaneously minimizing both the BER and PAPR. Moreover, the detection architecture of LACONet is drastically simplified compared to classical LACO-OFDM, since the Fast Fourier Transform (FFT) operation is applied only once at the receiver. We further propose a generalized AE-aided optical OFDM scheme for IM/DD communications, termed as IMDD-OFDMNet, where the unipolarity of the Time Domain (TD) signal is no longer guaranteed by the Hermitian Symmetry, but rather by taking the absolute square value of the complex TD signal. As such, all available subcarriers of IMDD-OFDMNet are used for carrying useful information, hence it has a higher throughput than the LACO-based schemes. As a further benefit, its transceiver requires only a single Inverse FFT or FFT. Finally, simulation results are provided to show that our learning schemes achieve better BER and PAPR performance than their conventional counterparts.

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VLCnet: Deep Learning Based End-to-End Visible Light Communication System

Cited by 23 publications

References 20 publications

ViRSMALNet: A Twin-Tier LSTM-Based Deep Learning Network for Indoor MIMO RSMA VLC Systems

ViRSMALNet: A Twin-Tier LSTM-Based Deep Learning Network for Indoor MIMO RSMA VLC Systems

Deep Learning-Aided Optical IM/DD OFDM Approaches the Throughput of RF-OFDM

Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection

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