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Basic Optical Fiber Nonlinear Limits.

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Abstract

In the past years, optical communication systems witnessed different developments in the fields of optical amplification, coherent receivers, digital signal processing (DSP), and high spectral efficiency modulation formats. However, these technologies have reached a point …‏
Andrew Ellis
Mariia Sorokina
edited by
Optical
Communication Systems
Limits and Possibilities
Published by
Jenny Stanford Publishing Pte. Ltd.
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Optical Communication Systems: Limits and Possibilities
Copyright © 2020 Jenny Stanford Publishing Pte. Ltd.
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ISBN 978-981-4800-28-0 (Hardcover)
ISBN 978-0-429-02780-2 (eBook)
Contents
Preface xiii
1. Modelling High-Capacity Nonlinear Transmission
Systems 1
Hadrien Louchet, Nikolay Karelin, and André Richter
1.1 Introduction 1
1.2 Nonlinear Fibre Propagation: From Single to
Multimode 2
1.2.1 Wave Equation 2
1.2.2 Linear Propagat 4
1.2.2.1 Loss 4
1.2.2.2 Chromatic dispersion 5
1.2.2.3 Birefringence 7
1.2.3 Nonlinear Propagation Effects 9
1.2.4 The Scalar Nonlinear Schrödinger
Equation 11
1.2.5 The Manakov-PMD Equation 12
1.2.6 Extension to SDM Systems Using
Multimode Fibre 14
1.3 Solving the Manakov-PMD Equation 17
1.3.1 Signal Representations 17
1.3.2 Numerical Methods 19
1.3.2.1 The split-step (Fourier) method 19
1.3.2.2 Step-size control 22
1.3.2.3 The coarse-step model 24
1.3.3 Simulation Framework for SDM Systems 25
1.4 Accurate Modelling of System-Level Nonlinear
Impairments 29
vi Contents
1.4.1 Self-Phase Modulation 29
1.4.2 Intra-Channel Cross-Phase Modulation
and Four-Wave Mixing 30
1.4.3 Cross-Phase Modulation 31
1.4.4 Four-Wave Mixing 32
1.4.5 Signal-Noise Interaction 33
1.4.6 Cross-Polarization Modulation 34
1.4.7 Stimulated Raman Scattering 36
1.4.8 The Nature of the Nonlinear Interference
Noise 37
1.5 Guidelines for Modelling High-Capacity
Nonlinear Systems 38
1.5.1 Overview of System Performance
Criteria 38
1.5.1.1 Bit-error-rate 39
1.5.1.2 Signal-to-noise ratio 39
1.5.1.3 System penalty and system
margin 40
1.5.1.4 Error-vector magnitude 42
1.5.2 Estimating the Bit-Error-Rate 43
1.5.2.1 Error-counting 43
1.5.2.2 BER estimation techniques 44
1.5.3 Estimating System Average Performance
and Outage Probability 45
1.5.3.1 System-level components
modelling 45
1.5.3.2 Transmission link modelling 47
1.5.3.3 Deterministic propagation 49
1.5.3.4 Modelling stochastic propagation
 0
1.6 Summary and Outlook 52
2. Basic Optical Fiber Nonlinear Limits 63
Mohammad Ahmad Zaki Al-Khateeb, Abdallah Ali,
and Andrew Ellis
2.1 Nonlinear Behavior of Optical Fibers 65
vii
Contents
2.1.1 Kerr Nonlinear Effects in a Single-Span
Transmission System 67
2.1.2 Kerr Nonlinear Effects in a Multi-Span
Transmission System 69
2.2 Noise Accumulation Optical Transmission
Systems 74
2.2.1 Total Nonlinear Kerr Noise 75
2.2.2 Total Linear ASE Noise 80
2.2.3 Total Signal-ASE Nonlinear Noise 81
2.3 Performance of Coherently Detected Optical
Transmission Systems 85
3. Fiber Nonlinearity Compensation: Performance
Limits and Commercial Outlook 95
 DanishRafique
3.1 Fiber Nonlinearity Compensation 96
3.2 Digital Back Propagation 98
3.2.1 DBP Performance Scaling 103
3.2.2 DBP Performance Limits 107
3.3 Phase Conjugation 110
3.3.1 Pre-Dispersed PC 111
3.3.2 Comparison of Single-Channel DBP and
PC 112
3.4 Commercial Applications and Perspective 114
4. Phase-Conjugated Twin Waves and Phase-Conjugated
Coding 123
Son Thai Le
4.1 Introduction 123
4.2 General Principle 125
4.2.1 Phase-Conjugated Twin Waves 126
4.2.2 Nonlinear Noise Squeezing 128
4.2.3 Connection between NLNS and PCTW 129
4.2.4 Generalized Phase-Conjugated Twin
Waves 131
4.3 Beneit and Limitation of PCTW 132
viii Contents
4.3.1 SNR and Capacity Gain in PCTW-Based
Transmissions 132
4.3.2 Beneit and Application Range of PCTW 135
4.4 Phase-Conjugated Pilot 140
4.4.1 Principle 140
  2
4.5 Phase-Conjugated Subcarrier Coding 148
4.5.1 Principle of PCSC 148
4.5.2 Performance of PCSC 151
4.6 Other Variants of PCTW 156
4.6.1 Temporally Multiplexed PCTW 156
4.6.2 Modiied PCTW 158
4.6.3 PCTW for Multimode and Multi-Core
Fibers 159
4.7 Conclusion 159
5. Information-Theoretic Concepts for Fiber Optic
Communications 165
Mariia Sorokina and Metodi P. Yankov
5.1 Communication Channel 165
5.2 Fiber Optic Communications 167
5.3 Shannon Capacity and Mutual Information 170
5.4 Information-Theoretic Channel Modeling 172
5.5 Numerical Calculations of Lower Bounds on
Shannon Capacity 174
5.6 Probabilistic Shaping 176
5.6.1 Optimization for the Optical Fiber
Channel 178
5.6.2 Probabilistic Shaping of Binary Data 181
5.7 Concluding Remarks 185
6. Advanced Coding for Fiber-Optics Communications
Systems 191
Ivan B. Djordjevic
6.1 Introduction 192
6.2 Turbo-Product Codes 193
ix
Contents
6.3 LDPC Codes 197
6.3.1 LDPC Codes Fundamentals and Large-Girth
Code Design 197
6.3.2 Decoding of Binary LDPC Codes 202
6.3.3 Nonbinary LDPC Codes: Quasi-Cyclic Code
Design and Decoding Algorithms 207
6.3.4 Rate-Adaptive LDPC Coding
Implementations in FPGA 210
6.4 Coded Modulation for Optical Communications 215
6.4.1 Coded Modulation Fundamentals 216
6.4.2 Multilevel Coded Modulation and
Unequal Error Protection 219
6.4.3 Bit-Interleaved Coded Modulation 225
6.4.4 Hybrid Multidimensional Coded
Modulation Scheme for High-Speed
Optical Transport 226
6.5 LDPC Coded Modulation for Optical
Communications Enabling Quasi-Single-Mode
Transmission over Transoceanic Distances
Using Few-Mode Fibers 230
6.6 Concluding Remarks 236
7. Nonlinear Fourier Transform-Based Optical
Transmission: Methods for Capacity Estimation 243
Jaroslaw E. Prilepsky, Stanislav A. Derevyanko,
and Sergei K. Turitsyn
7.1 Introduction 243
7.2 Main Model and Basics of NFT 246
7.2.1 Nonlinear Schrödinger Equation 246
7.2.2 NFT Operations 248
7.3 General Expressions for Noise Autocorrelation
Functions inside NF Domain 250
7.3.1 Perturbed Evolution of NF Spectrum 250
7.3.2 Noise Autocorrelation Functions for the
Continuous Part of NF Spectrum 251
7.4 Capacity Estimates for the Nonlinear Inverse
Synthesis NFT-Based Method 253
xContents
7.4.1 NIS Basics and Continuous Input-Output
Channel Model 253
7.4.2 Discrete Input-Output Model 257
7.4.3 Capacity Estimates for WDM/OFDM NIS
Transmission 258
7.4.4 Applicability of Results 261
7.5 Conclusion 262
8. Spatial Multiplexing: Technology 273
Yongmin Jung, Qiongyue Kang, Shaif-ul Alam,
and David J. Richardson
8.1 Introduction 274
8.2 SDM Transmission Fibres 275
8.2.1 Few-Mode Fibres 277
8.2.2 Multi-Core Fibres 279
8.3 SDM Multiplexers and Demultiplexers 281
8.3.1 Mode MUXs/DEMUXs for Few-Mode
Fibres 281
8.3.2 Fan-In/Fan-Out Devices for Multi-Core
Fibres 283
8.4 SDM Optical Ampliiers 284
8.4.1 Strategies to Minimize Differential Modal
Gain in Few-Mode EDFA 286
8.4.2 Core Pumped 6-Mode EDFA 287
8.4.3 Cladding Pumped 6-Mode EDFA 289
8.4.4 Future Prospects to Increase the Number
of Spatial Modes in FM-EDFAs 290
8.5 Conclusion 292
9. Spatial Multiplexing: Modelling 297
Filipe Ferreira, Christian Costa, Sygletos Stylianos,
and Andrew Ellis
9.1 Introduction 298
9.2 Coupled-Mode Theory for Few-Mode Fibers 300
9.2.1 Coupled-Mode Equations 301
9.2.2 Coupled-Mode Equations Solution for
Two-Mode Fibers 303
xi
Contents
9.3 Semi-Analytical Solutions for Higher-Order
Modes 304
9.3.1 Analytical Expressions for the
Three-Modes Case 306
9.3.2 Analytical Expressions for More Than
Three-Modes 306
9.3.3 Algorithm Complexity 308
9.4 Single-Section Modelling 309
9.5 Multi-Section Modelling 312
9.5.1 Setting Mode Coupling Strength and
Correlation Length 312
9.5.2 Mode Coupling Accumulation over
Transmission Length 314
9.5.3 Polarization Mode Coupling 315
9.6 GD Statistics in Non-Delay-Managed Links 316
9.6.1 GD Standard Deviation and Intensity
Impulse Response 317
9.6.2 GD Probability Density Function and
Maximum GD Spread 320
9.7 GD Statistics in Delay-Managed Links 323
9.8 Nonlinear Propagation Modelling 326
9.8.1 Modiied Split-Step Fourier Method 327
9.8.2 Extreme Coupling Strength Regimes 328
9.8.3 Intermediate Coupling Strength Regime 328
9.8.4 Total Nonlinear Noise: Analytical
Integration 329
9.9 Linear Coupling Impact Nonlinear Noise for
Delay Uncompensated Spans 331
9.10 Linear Coupling Impact on Nonlinear Noise for
Delay Compensated Spans 333
9.11 Manakov Approximation vs. Fully Stochastic
Propagation 336
9.12 Conclusions 342
Index 347
Preface
         
     
transmission from dense ultrashort cables in data-centers to
transoceanic distances around the globe, connecting billions of
users and linking cities, countries and continents. It is hard to
overstate the impact       
economy, healthcare, public and government services, society, and
almost every aspects of our lives.
         
     
online services brings about the escalating pressure on the speed
(capacity) and quality (bit error rate) characteristics of information
     
for these bandwidth-hungry online services include cloud
computing, on-demand HD video streams, online business analytics
   
arising from data-center applications, the Internet of Things, and
various other broadband services. It is well recognized nowadays
  
systems are quickly approaching the limits of current transmission
technologies, many of which were originally developed for
communication over linear channels (e.g., radio). However, optical

Nonlinear effects        

Unlike wireless communications, where signal quality can be
enhanced by increasing the optical power at the transmitter, in
        
signal impairments. Consequently, there is a clear need for the
development 
This book gives an overview of the current research by
         
Nikolay Karelin, and André Richter from VPIphotonics, covers
key requirements and challenges for accurate modeling of
xiv
      
Chapter 2, Mohammad Ahmad Zaki Al-Khateeb, Abdallah Ali, and
Andrew Ellis from Aston University discuss theoretical models
that can predict the maximum performance and discuss optical

          
       
nonlinearity compensation. Chapter 4 is focused on the method
of phase-conjugated twin waves and phase-conjugated coding,
presented by Son Thai Le from Nokia Bell-Labs.
The information-theoretic treatment, focused on channel
models and their limits for estimating transmission throughput
          
and Metodi P. Yankov from Aston University and Technical
University of Denmark, respectively. In Chapter 6, Ivan B. Djordjevic
from the University of Arizona reviews coding algorithms for
        
       
distance. The nonlinear Fourier transform method is described in
Chapter 7 by Jaroslaw E. Prilepsky, Stanislav A. Derevyanko, and
Sergei K. Turitsyn from Aston University and Ben-Gurion University
of the Negev, who discuss challenges and advantages of its
application on communication systems.
Finally, spatial multiplexing is considered here (i) from the
technology perspective in Chapter 8 by Yongmin Jung, Qiongyue
Kang, Shaif-ul Alam, and David J. Richardson from the University
of Southampton, who discuss the prospects of scaling as well
as potential energy and cost savings of this technology and
(ii) from the modeling perspective by Filipe Ferreira, Christian
Costa, Sygletos Stylianos, and Andrew Ellis from Aston University,

To conclude, the book presents a broad overview of the
      
      
and prospects.
We wish to thank the authors and the Jenny Stanford

Preface
Conference Paper
Full-text available
Video streaming services such as Amazon Prime Video, Netflix and YouTube, continue to be of enormous demands in everyday peoples’ lives. This enticed research in new mechanisms to provide a clear image of network usage and ensure better Quality of Service (QoS) for these applications. This paper proposes an accurate video streaming traffic classification model based on deep learning (DL). We first collected a set of video traffic data from a real network. Then, data was pre-processed to select the desired features for video traffic classification. Based on the performance evaluation, the model produces an overall accuracy of 99.3% when classifying video streaming traffic using a multi-layer feedforward neural network. This paper also evaluates the DL approach’s effectiveness compared to the Gaussian Naive Bayes algorithm (GNB), one of the most well-known machine learning techniques used in Internet traffic classification. The model is promising to be applied in a real-time scenario as it showed its ability to predict new unseen data with 98.4% overall accuracy.
Chapter
This chapter introduces the space‐division multiplexing (SDM) technique, and discusses the modes of multimode and multicore fibers (MCFs). A numerical approach is needed in general to find the supermodes of a MCF. An analytical solution can be found when all cores in a MCF are identical and arranged in a circular fashion with equal spacing. The chapter describes the devices and the components that needed to be developed for the SDM technology. SDM technology required the development of new kinds of fibers and new components such as spatial multiplexers and optical amplifiers capable of amplifying all spatial channels simultaneously. The chapter focuses on the coupled multimode equations that are used for understanding the impact of the dispersive and nonlinear effects in SDM systems. The most important issue for the viability of the SDM technique concerns the crosstalk among the signals propagating in different modes of a fiber link.
Article
Full-text available
Practical implementation of digital signal processing for mitigation of transmission impairments in optical communication systems requires reduction of the complexity of the underlying algorithms. Here, we investigate the application of convolutional neural networks for compensating nonlinear signal distortions in a 3200~km fiber-optic 11x400-Gb/s WDM PDM-16QAM transmission link with a focus on the optimization of the corresponding algorithmic complexity. We propose a design that includes original initialisation of the weights of the layers by a filter predefined through the training a single-layer convolutional neural network. Furthermore, we use an enhanced activation function that takes into account nonlinear interactions between neighbouring symbols. To increase learning efficiency, we apply a layer-wise training scheme followed by joint optimization of all weights applying additional training to all of them together in the large multi-layer network. We examine application of the proposed convolutional neural network for the nonlinearity compensation using only one sample per symbol and evaluate complexity and performance of the proposed technique.
Article
In this paper, we propose a model-based machine-learning approach for dual-polarization systems by parameterizing the split-step Fourier method for the Manakov-PMD equation. The resulting method combines hardware-friendly time-domain nonlinearity mitigation via the recently proposed learned digital backpropagation (LDBP) with distributed compensation of polarization-mode dispersion (PMD). We refer to the resulting approach as LDBP-PMD. We train LDBP-PMD on multiple PMD realizations and show that it converges within 1% of its peak performance after 428 training iterations on average, yielding a peak effective signal-to-noise ratio of only 0.30 dB below the PMD-free case. Similar to state-of-the-art lumped PMD compensation algorithms in practical systems, our approach does not assume any knowledge about the particular PMD realization along the link, nor any knowledge about the total accumulated PMD. This is a significant improvement compared to prior work on distributed PMD compensation, where knowledge about the accumulated PMD is typically assumed. We also compare different parameterization choices in terms of performance, complexity, and convergence behavior. Lastly, we demonstrate that the learned models can be successfully retrained after an abrupt change of the PMD realization along the fiber.
Article
The nonlinear Schrodinger equation (NLSE) is often used as a master path-average model for fiber-optic links to analyse fundamental properties of such nonlinear communication channels. Transmission of signal in nonlinear channels is conceptually different from linear communications. We use here the NLSE channel model to explain and illustrate some new unusual features introduced by nonlinearity. In general, NLSE describes the co-existence of dispersive (continuous) waves and localised (here in time) waves - soliton pulses. The nonlinear Fourier transform method allows one to compute for any given temporal signal the so-called nonlinear spectrum, that defines both continuous spectrum (analogue to conventional Fourier spectral presentation) and solitonic components. Nonlinear spectrum remains invariant during signal evolution in the NLSE channel. We examine conventional orthogonal frequency-division multiplexing (OFDM) and wavelength-division multiplexing (WDM) return-to-zero signals and demonstrate that both signals at certain power levels have soliton component. We would like to stress that this effect is completely different from the soliton communications studied in the past. Applying Zakharov-Shabat spectral problem to a single WDM or OFDM symbol with multiple sub-carriers we quantify the effect of statistical occurrence of discrete eigenvalues in such an information-bearing optical signal. Moreover, we observe that at signal powers optimal for transmission an OFDM symbol with high probability has a soliton component.
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