Enhancing distributed feedback‐standard single mode fiber‐radio over fiber links performance by neural network digital predistortion

This paper demonstrates an unprecedented novel neural network (NN)-based digital predistortion (DPD) solution to overcome the signal impairments and nonlinearities in Analog Optical fronthauls using radio over fiber (RoF) systems. DPD is realized with Volterra-based procedures that utilize indirect learning architecture (ILA) and direct learning architecture (DLA) that becomes quite complex. The proposed method using NNs evades issues associated with ILA and utilizes an NN to first model the RoF link and then trains an NN-based predistorter by backpropagating through the RoF NN model. Furthermore, the experimental evaluation is carried out for Long Term Evolution 20 MHz 256 quadraturre amplitude modulation (QAM) modulation signal using an 850 nm Single Mode VCSEL and Standard Single Mode Fiber to establish a comparison between the NN-based RoF link and Volterra-based Memory Polynomial and Generalized Memory Polynomial using ILA. The efficacy of the DPD is examined by reporting the Adjacent Channel Power Ratio and Error Vector Magnitude. The experimental findings imply that NN-DPD convincingly learns the RoF nonlinearities which may not suit a Volterra-based model, and hence may offer a favorable trade-off in terms of computational overhead and DPD performance.

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RL-SARSA Machine Learning Based Analog Radio over Fiber System

10.20944/preprints201909.0159.v1 ◽

2019 ◽

Author(s):

Muhammad Usman Hadi

Keyword(s):

Machine Learning ◽

Bit Error Rate ◽

Fiber Length ◽

Single Mode ◽

Single Mode Fiber ◽

Radio Over Fiber ◽

Fiber System ◽

Decision Method ◽

Effective Decision ◽

Radio Over Fiber System

We propose a 10-Gb/s 64-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes Reinforcement Learning (RL) SARSA based decision method to indicate an effective decision which mitigates nonlinearity. By utilizing RL-SARSA algorithm, the results demonstrate that significant reduction can be obtained in terms of bit error rate.

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Nonlinearities Diminution in 40 Gb/s 256 QAM Radio over Fiber Link via Machine Learning Method

10.20944/preprints201909.0031.v1 ◽

2019 ◽

Author(s):

Muhammad Usman Hadi

Keyword(s):

Machine Learning ◽

Single Mode ◽

Single Mode Fiber ◽

Radio Over Fiber ◽

Support Vector ◽

Potential Candidate ◽

Input Signal Power ◽

Quadrature Phase ◽

Fiber Link ◽

Mach Zehnder Modulator

Machine learning (ML) methodologies have been looked upon recently as a potential candidate for mitigating nonlinearity issues in optical communications. In this paper, we experimentally demonstrate a 40-Gb/s 256-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator. By utilizing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.

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Characterization of semiconductor laser frequency chirp based on signal distortion in dispersive optical fiber

Opto-Electronics Review ◽

10.2478/s11772-006-0015-z ◽

2006 ◽

Vol 14 (2) ◽

Cited By ~ 17

Author(s):

P. Krehlik

Keyword(s):

Single Mode ◽

Laser Frequency ◽

Single Mode Fiber ◽

Distributed Feedback ◽

Parameters Estimation ◽

Signal Distortion ◽

Measuring Time ◽

Simple Method ◽

Laser Chirp

AbstractIn the paper, the simple method of laser chirp parameters estimation is presented. It is based on measuring time-domain distortions of chirped signal transmitted through dispersive fiber and finding laser chirp parameters by matching measured distortions to calculated ones. Experiments undertaken with 1.55 μm telecommunication grade distributed feedback (DFB) lasers and standard single-mode fiber are described, together with some practical remarks on measurement setup and main conclusions.

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