Non-linear Compensation of Multi-CAP VLC System Employing Pre-Distortion Base on Clustering of Machine Learning

Lu, Xingyu; Zhao, Mingming; Qiao, Liang; Chi, Nan

doi:10.1364/ofc.2018.m2k.1

Cited by 24 publications

(15 citation statements)

References 2 publications

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“…In the context of nonlinearity mitigation or in general, impairment mitigation, there are a group of references that implement equalization of the optical signal using a variety of ML algorithms like Gaussian mixture models [75], clustering [76], and artificial neural networks [77]- [82]. In [75], the authors propose a GMM to replace the soft/hard decoder module in a PAM-4 decoding process whereas in [76], the authors propose a scheme for pre-distortion using the ML clustering algorithm to decode the constellation points from a received constellation affected with nonlinear impairments.…”

Section: Nonlinearity Mitigationmentioning

confidence: 99%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

486

226

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Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management.The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation.In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions.

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Section: Nonlinearity Mitigationmentioning

confidence: 99%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

486

226

View full text Add to dashboard Cite

show abstract

“…These aim to mitigate linear distortions due to the limited link bandwidth, multipath effects and time sampling offsets and nonlinear impairments due to the inherent nonlinear behaviour of the electrical and optical components used in the link (mainly LEDs and electrical amplifiers). These include the use of pre-compensation analogue circuits at the transmitter [66,90,91], the use of FFE and DFE equalizers [32,67,[91][92][93], nonlinear equalizers [94][95][96][97][98], frequency domain equalization (FDE) [25,60,99], machine learning-based algorithms [100,101] In general, the use of longer FFEs and DFEs (i.e. larger number of taps) improves the system performance but these additional taps produce diminishing returns (i.e.…”

Section: (B) Improved Equalizationmentioning

confidence: 99%

“…A detailed performance analysis for CAP-DFE is presented in [25]. Recently, pre-and postequalization algorithms based on machine learning have been proposed in order to mitigate the nonlinear impairments of CAP-based VLC links [100,101,106,110]. These involve finding the centroids of the constellation diagrams of the received data using machine learning algorithms and appropriately either pre-distort the transmitted symbols [100] or adjust the symbol decision areas in the QAM constellation diagrams [101].…”

Section: (B) Improved Equalizationmentioning

confidence: 99%

“…Recently, pre-and postequalization algorithms based on machine learning have been proposed in order to mitigate the nonlinear impairments of CAP-based VLC links [100,101,106,110]. These involve finding the centroids of the constellation diagrams of the received data using machine learning algorithms and appropriately either pre-distort the transmitted symbols [100] or adjust the symbol decision areas in the QAM constellation diagrams [101]. In general, the use of more complex equalization schemes can provide important performance improvements but increase the system complexity and require additional digital signal processing which cannot be readily achieved with simple hardware.…”

Section: (B) Improved Equalizationmentioning

confidence: 99%

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Carrierless amplitude and phase modulation in wireless visible light communication systems

Bamiedakis

Penty

White

2020

Phil. Trans. R. Soc. A.

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Visible light communications (VLCs) have attracted considerable interest in recent years owing to the potential to simultaneously achieve data transmission and illumination using low-cost light-emitting diodes (LEDs). However, the high-speed capability of such links is typically limited by the low bandwidth of LEDs. As a result, spectrally efficient advanced modulation formats have been considered for use in VLC links in order to mitigate this issue and enable higher data rates. Carrierless amplitude and phase (CAP) modulation is one such spectrally efficient scheme that has attracted significant interest in recent years owing to its good potential and practical implementation. In this paper, we introduce the basic features of CAP modulation and review its use in the context of indoor VLC systems. We describe some of its attributes and inherent limitations, present related advances aiming to improve its performance and potential and report on recent experimental demonstrations of LED-based VLC links employing CAP modulation. This article is part of the theme issue ‘Optical wireless communication’.

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“…Typically, the equalization applied is based on conventional FFEs and DFEs as these are relatively simple to implement in hardware. More complex equalizers, such as non-linear Volterra equalizers [19][20][21][22] and those based on machine-learning algorithms [23][24][25], have been also proposed for use in CAP-based transmission systems and have been demonstrated in optical links. However, these are more complex to implement or require significant digital signal processing.…”

Section: Figure 1 Typical Transmission System Based On Cap Modulation and Conventional Ffe And Dfe Equalizersmentioning

confidence: 99%

A New Equalizer Structure for High-Speed Optical Links Based on Carrierless Amplitude and Phase Modulation

Bamiedakis

Dong

Cunningham

et al. 2020

2020 22nd International Conference on Transparent Optical Networks (ICTON)

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Spectral efficient modulation formats can enable the transmission of higher data rates than conventional on-off keying (OOK). Carrierless amplitude and phase modulation (CAP) is such an attractive modulation scheme that has been widely considered for use in different types of optical links. The scheme however can suffer from intersymbol interference (ISI) and channel crosstalk (CCI) when the frequency response of the channel is not ideal. Conventional equalizers based on feedforward (FFE) and decision feedback (DFE) equalizers are easy to implement in practice and can mitigate some of the induced ISI. However, they fail to suppress the induced CCI in the link as each channel is equalized independently. As a result, we have recently proposed the use of a new equalizer structure for use in CAP-based optical links to mitigate these transmission impairments. This new equalizer, named CAP equalizer, can be formed with conventional FFEs and DFEs with minimal additional complexity whilst providing significant performance advantages. In this paper therefore, we review the equalizer structure and report recent demonstrations of its use in short-reach optical links. We present experimental studies on a 112 Gb/s CAP-16 VCSEL-based OM4 MMF link and a 4 Gb/s CAP-16 LED-based POF link and compare the performance of the links when both a conventional FFE and DFE equalizer and the newly proposed CAP equalizer are used. The results clearly demonstrate that the CAP equalizer offers improved receiver sensitivity and enables successful data transmission over longer fibre reaches.

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Non-linear Compensation of Multi-CAP VLC System Employing Pre-Distortion Base on Clustering of Machine Learning

Cited by 24 publications

References 2 publications

An Overview on Application of Machine Learning Techniques in Optical Networks

An Overview on Application of Machine Learning Techniques in Optical Networks

Carrierless amplitude and phase modulation in wireless visible light communication systems

A New Equalizer Structure for High-Speed Optical Links Based on Carrierless Amplitude and Phase Modulation

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