Orthogonal Polynomial-Based Nonlinearity Modeling and Mitigation for LED Communications

Zhao, Weikang; Guo, Qinghua; Tong, Jun; Xi, Jiangtao; Yu, Yanguang; Niu, Pingjuan; Sun, Xin

doi:10.1109/jphot.2016.2581485

Cited by 23 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its complexity can lead to practical problems. A memory polynomial model that reduces complexity was initially proposed for signal predistortion in the power amplifier (PA) [41,42], and it was recently considered for a pre-distorter (post-distorter) in VLC [36,43]. The Wiener [35] and Hammerstein [36] models can be seen as an overly simplified subset of the Volterra model.…”

Section: Led Models For Communicationsmentioning

confidence: 99%

Characterization of dynamic distortion in LED light output for optical wireless communications

et al. 2021

View full text Add to dashboard Cite

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

show abstract

Section: Led Models For Communicationsmentioning

confidence: 99%

Characterization of dynamic distortion in LED light output for optical wireless communications

et al. 2021

View full text Add to dashboard Cite

show abstract

“…. , x n+M ] T , the output of the ELM is approximately a(x n ) + i n , where a(x n ) and i n are defined in (21). This enables us to estimate a(α i ), i n and V to evaluate the likelihood p(y n |x n ) in (22), which is detailed in the following.…”

Section: Input Outputmentioning

confidence: 99%

“…A possible solution is to first estimate the polynomial coefficients {a k,m } in ( 6) and then calculate a(α i ), i n and V . However, it can be difficult since the estimation of memory polynomial coefficients involves the inversion of a correlation matrix with ill condition, which can easily lead to numerical instabilities especially for high order or even moderate order polynomials [13], [21]. In addition, it can also be difficult to determine the polynomial order and memory length to properly model the LED nonlinearity and memory effects.…”

Section: Elm Based Iterative Receivermentioning

confidence: 99%

Extreme-Learning-Machine-Based Noniterative and Iterative Nonlinearity Mitigation for LED Communication Systems

Gao

Guo

Tong

et al. 2020

IEEE Systems Journal

Self Cite

View full text Add to dashboard Cite

This work concerns receiver design for light emitting diode (LED) communications where the LED nonlinearity can severely degrade the performance of communications. We propose extreme learning machine (ELM) based non-iterative receivers and iterative receivers to effectively handle the LED nonlinearity and memory effects. For the iterative receiver design, we also develop a data-aided receiver, where data is used as virtual training sequence in ELM training. It is shown that the ELM based receivers significantly outperform conventional polynomial based receivers; iterative receivers can achieve huge performance gain compared to non-iterative receivers; and the data-aided receiver can reduce training overhead considerably. This work can also be extended to radio frequency communications, e.g., to deal with the nonlinearity of power amplifiers.

show abstract

“…As the bandwidth of modulated signal increased, more profound memory effect is exhibited in LED [16]. The well-known memory polynomial (MP) and generalized MP are traditional but still popular models for DPD [17]. Nevertheless, only the diagonal kernels are employed in MP which are insufficient to characterize the memory behavior of LED.…”

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

View full text Add to dashboard Cite

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.

show abstract

Orthogonal Polynomial-Based Nonlinearity Modeling and Mitigation for LED Communications

Cited by 23 publications

References 25 publications

Characterization of dynamic distortion in LED light output for optical wireless communications

Characterization of dynamic distortion in LED light output for optical wireless communications

Extreme-Learning-Machine-Based Noniterative and Iterative Nonlinearity Mitigation for LED Communication Systems

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

Contact Info

Product

Resources

About