End-to-end Deep Learning for VCSEL’s Nonlinear Digital Pre-Distortion

Minelli, Leonardo; Forghieri, F.; Gaudino, Roberto

doi:10.1109/icop56156.2022.9911760

Cited by 2 publications

(8 citation statements)

References 16 publications

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“…For the reader who might not be familiar with the relation between nonlinear digital filters and convolution neural network layers, Appendix A illustrates in detail this strict analogy. We compare the new architecture with an updated version of the DLA we proposed in [16]. Moreover, we illustrate the features of the CNN approach adopted in this work and in [16] compared to the methodology used in our previous works [20], [21], [15].…”

Section: Nonlinear Dpd Optimization Methodologymentioning

confidence: 99%

“…We compare the new architecture with an updated version of the DLA we proposed in [16]. Moreover, we illustrate the features of the CNN approach adopted in this work and in [16] compared to the methodology used in our previous works [20], [21], [15]. In the following, we simplify the representation of coefficients (such as weights, bias terms, etc.)…”

Section: Nonlinear Dpd Optimization Methodologymentioning

confidence: 99%

“…However, the ILA relies on the commutation law, which is mathematically appliable only for linear systems [14]. Recent studies have emphasized the effectiveness of Direct Learning Architectures (DLA) and end-to-end (E2E) learning systems in optimizing nonlinear DPD for optical transmission systems [15], [16], [17], [13]. The DLA is an approach specifically designed for optimizing nonlinear DPD, which has consistently shown to outperform the ILA in both wireless and optical communications [12], [13], [18], [17].…”

Section: Nonlinear Digital Pre-distortion For Improving Vcsel-mmf Opt...mentioning

confidence: 99%

“…In this section, we have presented a novel CNN-based approach for nonlinear DPD optimization, which represents a significant departure from the methodology proposed in our previous works [15], [21], [20] based on finite impulse response neural networks (FIRNN). Despite this paradigm shift, the underlying system emulation remains unchanged from a DSP perspective.…”

Section: Cnn-based Versus Firnn-based Optimizationmentioning

confidence: 99%

“…Signal normalizations also enable the application of differentiable constraints on signal dynamics, such as the P2P normalization. Unlike the previously adopted P2P hard-limiting function [20], [21], [15], [16], this new implementation of the constraint avoids the vanishing gradient issues caused by the hard-limiter clipping of the DPD output. Furthermore, signals statistic estimation allows for better tuning of the optimization loss function during the training, such as the MSE-based selection optimum decimation instant (refer to Sec.…”

Section: Cnn-based Versus Firnn-based Optimizationmentioning

confidence: 99%

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TDECQ-Based Optimization of Nonlinear Digital Pre-Distorters for VCSEL-MMF Optical Links Using End-to-End Learning

Minelli,

Forghieri,

Shao

et al. 2024

J. Lightwave Technol.

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We investigate in this paper the use of nonlinear digital pre-distorters (DPDs) for improving the performance of optical transmitters (TX) employing vertical-cavity surfaceemitting lasers (VCSELs), according to the standard transmitter and dispersion eye closure quaternary (TDECQ) compliance test for short-reach intra data center interconnects (DCI) using PAM4 over multi-mode fibers (MMF). We present a convolutional neural network (CNN) approach for nonlinear DPD optimization, suitable for training the pre-distorters using either a direct learning architecture (DLA) or an end-to-end (E2E) learning system. Then, we focus on a novel E2E architecture based on the reference TDECQ specifications for MMF optical links at net 100 Gbps per wavelength (λ). We experimentally implement the proposed methodology over a VCSEL-MMF setup compliant to the TDECQ test requirements. We evaluate the TDECQ performance of an optical TX employing a commercial 850 nm VCSEL at 107.2 Gbps driven at several nonlinear conditions, comparing nonlinear DPDs optimized using both DLA and TDECQ-based E2E approaches. Experimental results show that nonlinear DPD significantly enhances TDECQ performance, enabling compliance with the IEEE P802.3db TM requirements for net 100 Gbps/λ even in scenarios in which, without nonlinear DPD, the TDECQ test would fail due to VCSEL nonlinear distortions. In particular, nonlinear DPDs trained using the TDECQ-based E2E approach exhibit a consistent 0.8 dB gain in terms of TDECQ with respect to using the DLA.

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Section: Nonlinear Dpd Optimization Methodologymentioning

confidence: 99%

Section: Nonlinear Dpd Optimization Methodologymentioning

confidence: 99%

Section: Nonlinear Digital Pre-distortion For Improving Vcsel-mmf Opt...mentioning

confidence: 99%

Section: Cnn-based Versus Firnn-based Optimizationmentioning

confidence: 99%

Section: Cnn-based Versus Firnn-based Optimizationmentioning

confidence: 99%

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TDECQ-Based Optimization of Nonlinear Digital Pre-Distorters for VCSEL-MMF Optical Links Using End-to-End Learning

Minelli,

Forghieri,

Shao

et al. 2024

J. Lightwave Technol.

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End-to-End Learning for VCSEL-Based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities

Srinivasan

Song

Grabowski

et al. 2023

J. Lightwave Technol.

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End-to-end Deep Learning for VCSEL’s Nonlinear Digital Pre-Distortion

Cited by 2 publications

References 16 publications

TDECQ-Based Optimization of Nonlinear Digital Pre-Distorters for VCSEL-MMF Optical Links Using End-to-End Learning

TDECQ-Based Optimization of Nonlinear Digital Pre-Distorters for VCSEL-MMF Optical Links Using End-to-End Learning

End-to-End Learning for VCSEL-Based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities

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