Abstracting Optical Fibre Communication

Networks

Seb Savory

University of Cambridge

sjs1001@cam.ac.uk

Aim of the INSIGHT project • Software defined networking enables an intelligent

information infrastructure

• SDN needs the physical infrastructure to be abstracted

• The aim of INSIGHT is to create a framework for abstracting the physical infrastructure by embedding knowledge of the underpinning physics into the abstraction process

1

Project timeline • Project started 31st December 2014 with project

partners UCL, University of Bristol, BT and Polatis (UCL lead)

• First part formally ended 31st January 2016, then restarted 1st May 2016 when lead was transferred to Cambridge University, with UCL, Bristol, BT and Polatis remaining as project partners. Ciena has also assisted testbed development at Cambridge

• Formal end date is 31st March 2018 albeit an extension is being sought due to disruption of moving to Cambridge and staffing issues at other project partners (movement of postdocs to industry and lectureships)

2

Physical Layer Optical

Network Abstraction and Optical Resource

Description Experimental

use case Scenarios

Project Overview

3

Network Information

Service Requirements

Physical Network 1 Physical Network 2

Customer 1 Network Customer 2 Network Customer 3 Network

Multi-domain abstraction and virtualisation

Understand the available physical resources

Control routing and transceivers to optimise usage of resources

Provide a virtual resource

Allow limited control of routing transceivers within customer domain

Network Virtualisation

4

The challenge of optical networks • A single fibre is shared by multiple users as signals are routed in the optical domain

– fibre nonlinearity causes wavelengths to interact • E.g consider US NSFnet core network topology which goes from California (C1 &

C2) on the West to New Jersey (NJ) on the East

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Numbers represent the distances in km between nodes

Nonlinearities in fibres • The core which guides the light in an optical fibre has an effective diameter of ~

10 µm so the effective area is about 80 µm2

• 80 channels x 1 mW the power density is ~ 1 mW/ µm2 = 1 GW/m2

• High power density changes the speed of light in the fibre

• The change is small, e.g. 1 mW/ µm2 causes a Δn=3x10-11 i.e. 20ppt but currently limits optical fibre communication

0.063 GW/m2

1525 1530 1535 1540 1545 1550 1555 1560 1565 1570

Wavelength (nm)

-50

-40

-30

-20

-10

0

10

Pow

er

(dB

m)

1 GW/m2

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Dynamic Link Abstraction • Impairments occurs during transmission affect signal quality.

• All the impairments in one link can be abstracted as SNR degradation of signal

• The Kerr nonlinearity is the dominant factor which limits the signal transmission power in the optical fibre.

• How do we calculate nonlinearity?

• Worst-case or margin method

• Real-time case analysis

Signal SNR degradation

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University of Cambridge

ROADM A ROADM B

Impairments: ASE, CD, PMD,

PDL, Loss, Kerr effect...

Nonlinear term:

SPM

partially

coherent

Nonlinear term:

XPM

incoherent

• SNR of 𝑖𝑡ℎ channel calculated from a Gaussian noise model

1

𝑆𝑁𝑅𝑖=1

𝑆𝑁𝑅0𝑖+𝑁𝑠𝐴𝑆𝐸𝑖𝑃𝑖+ 𝑁𝑠1+𝜀𝑋𝑖,𝑖𝑃𝑖

2 + 𝑁𝑆 𝑋𝑖,𝑗𝑃𝑗2

𝑗≠𝑖

Abstracting using a basic SNR model

Transceiver

SNR

• 𝑃𝑖 is the power in 𝑖𝑡ℎ WDM channel, 𝑁𝑠is number of spans, 𝐴𝑆𝐸𝑖 is the ASE noise power for the 𝑖𝑡ℎ channel,

• 𝑋𝑖,𝑗 represents nonlinear interaction between 𝑖𝑡ℎ and 𝑗𝑡ℎ channel and for SPM 𝜀 ≈ 0.22 (for XPM 𝜀 ≈ 0)

Linear SNR

from optical

amplifiers

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National Dark Fibre Infrastructure Service

9

• NDFIS is an EPSRC Mid-Range Facility linking four universities (Bristol,

Southampton, UCL and Cambridge), three of which are partners in INSIGHT

• Bristol, UCL and Cambridge are able to independently abstract the same

physical network, giving significant INSIGHT as to the impact as to the

location of the observer on the abstracted network and also the abstraction

methodology

Bristol Abstraction Testbed

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• Two channel 28 GSa/s real-time transmitter with 3 bit resolution

• I/Q modulator for 16QAM/QPSK generation

• Pol. Mux emulator • 15 channel WDM source (external

cavity lasers) • Phase and polarisation diverse

coherent receiver with 33 GHz real-time oscilloscope

• 4 node optically routed network, including installed fibre

• SDN controlled WSS for spectral routing/grooming

A

Link 3=50 km

B

C

15 carriers

27G Baud PM-QPSK

ECL bank

Coherent receiver

15 CH

• EDFA noise figure • WSS attenuation • Filter detuning

• Fiber attenuation • Non-linearity

A

B

C

D • QoT monitoring

QoT estimator

SDN controller

DB DB Parameter learning

abstraction

SMF

WSS

Modelling process: OSNR𝑙𝑖𝑛𝑘 = 𝑃𝑖𝑛 𝑃𝐴𝑆𝐸 + 𝛼𝑁𝐿 ∗ 𝑃𝑖𝑛

3 𝑃𝐴𝑆𝐸 = ℎ ∗ 𝑣 ∗ 𝑁𝐹 ∗ 𝐺 ∗ 𝐵

𝑂𝑆𝑁𝑅𝑙𝑖𝑛𝑘#𝑘 = (𝑂𝑆𝑁𝑅𝑝𝑎𝑡ℎ#𝑘−1 − 𝑂𝑆𝑁𝑅𝑝𝑎𝑡ℎ#(𝑘−1)

−1 )−1

𝑂𝑆𝑁𝑅𝑝𝑎𝑡ℎ_𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 = ( 𝑂𝑆𝑁𝑅𝐿𝑖𝑛𝑘#𝑘−1

𝑛

𝑘=1

)−1

MCMC: a new sample guess only depends on the current state(Markov Chain) P(𝜃𝑛|𝜃𝑛−1, … , 𝜃2, 𝜃1)=P(𝜃𝑛|𝜃𝑛−1)

Optical Performance Monitoring (OPM) testbed

Abstraction @ Bristol

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F. Meng et al., "Robust Self-Learning Physical Layer Abstraction Utilizing Optical Performance Monitoring and Markov Chain Monte Carlo," ECOC 2017; 43nd European Conference on Optical Communication, Gothenburg, Sweden, 2017,.

Abstraction @ Bristol

Verifying prediction capability

with learning

without learning

Accuracy improves with learning

UCL Abstraction Testbed

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• 12-channel arbitrary waveform generator (AWG) with 92GSa/s and 32GHz electrical bandwidth

• 3 dual-polarisation IQ-modulators with 45GHz bandwidth

• 16 channel WDM source (external cavity lasers) • Spectrally-shaped (SS-ASE) source: 4.5THz

bandwidth is used to emulate neighbouring channels

• Recirculating loop for broadband (4.5~THz) long-haul transmission systems

• 70GHz balanced photodetectors and 63GHz electrical bandwidth allows reception in one single receiver 126GHz optical bandwidth (>252 GHz with frequency-stitching)

Abstraction @ UCL

• Gaussian noise model extended to included nonlinear receiver noise beating

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SNR =P

𝑁Pn + 𝜅P + 𝜂P3 + 3𝜂P2(Pn 𝑖1+𝜀𝑁

𝑖=1 + 𝜅𝑅𝑁1+𝜀P)

linear noise

nonlinear ASE beating

nonlinear Rx noise beating

nonlinear noise

• Digital backpropagation enables mitigation of Kerr nonlinearity, but studies demonstrates need to consider nonlinear receiver noise beating

100 200 400 800 1600 3200 6400

15

20

25

30

35

40

45

Simulation without transceiver noise

EDC only DBP without PMD DBP with PMD

Analytical model without trasnceiver noise

EDC only DBP without PMD

SN

R [

dB

]

Distance [km]

(a)

Validation of improved abstraction

• Improved model accurately predicts experimental data

15

100 200 400 800 1600 3200 6400

11

12

13

14

15

16

17

SN

R [

dB

]

(b)

Distance [km]

Simulation with tranceiver noise

EDC DBP without PMD DBP with PMD

Analytical model

EDC DBP without PMD

Experimental data

EDC DBP

150 %

Cambridge Abstraction Testbed

• 1200 km of single mode fibre • 32 x 32 Polatis switch • 14 EDFAs • Arranged as 10x100 km amplified spans

+ 4x50 km unamplified spans • Ciena WaveLogic 2, Coherent PM-QPSK,

40Gb/s (single channel) • Ciena WaveLogic 3, Coherent PM-

QPSK/16 QAM, 100/200 Gbps (single channel)

• Ciena WaveLogic 3, Coherent PM-QPSK/16 QAM, 100/200 Gbps (WDM – currently 12 channels)

• Upgrading with six wavelength selective switches (1x9) for networking studies

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-15 -10 -5 0 5

Launch Power [dBm]

8

10

12

14

Rece

ived

Sym

bol S

NR

[d

B]

9x100 km

-15 -10 -5 0 5

Launch Power [dBm]

8

10

12

14

Rece

ived

Sym

bol S

NR

[d

B]

8x100 km

-15 -10 -5 0 5

Launch Power [dBm]

8

10

12

14

Rece

ived

Sym

bol S

NR

[d

B]

7x100 km

-15 -10 -5 0 5

Launch Power [dBm]

8

10

12

14

Rece

ived

Sym

bol S

NR

[d

B]

6x100 km

-15 -10 -5 0 5

Launch Power [dBm]

8

10

12

14

Rece

ived

Sym

bol S

NR

[d

B]

5x100 km

-15 -10 -5 0 5

Launch Power [dBm]

8

10

12

14

Rece

ived

Sym

bol S

NR

[d

B]

4x100 km

-15 -10 -5 0 5

Launch Power [dBm]

8

10

12

14

Rece

ived

Sym

bol S

NR

[d

B]

3x100 km

-15 -10 -5 0 5

Launch Power [dBm]

8

10

12

14

Rece

ived

Sym

bol S

NR

[d

B]

2x100 km

-15 -10 -5 0 5

Launch Power [dBm]

8

10

12

14

Rece

ived

Sym

bol S

NR

[d

B]

1x100 km

Abstraction @ Cambridge

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-15 -10 -5 0 5

Launch Power [dBm]

8

10

12

14

Rece

ived

Sym

bol S

NR

[d

B]

10x100 km

Link is abstracted using an enhanced Gaussian noise (EGN) model, with 3 global parameters extracted from the measurements

Aim to abstract 10x100 km links using a single 11.5 GBd DP-QPSK probe

NDFIS loop back Cambridge to Reading

Duxford Telehouse PowerGate Reading Cambridge Cambridge

7dB 22dB 9dB 17dB

Amplifiers have a nominal 20 dB gain Some gain variation is possible but impacts flatness

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Cambridge – Reading - Cambridge Three term fit applied to overall link, indicating feasibility of abstraction using installed fibre (again using a single 11.5 GBd probe)

-10 -5 0 5

Launch Power [dBm]

10-8

10-6

10-4

10-2

pre

FE

C B

ER

at

WL

2

-5 0 5

Launch Power [dBm]

10

11

12

13

14

15

SN

R a

t W

L2

[d

B]

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Next steps (1/3)

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All three partners have established both extensive experimental testbeds and developed a framework for abstraction Challenge is now to collectively abstract a common infrastructure

Initially each partner will independently abstract infrastructure

Next steps (2/3)

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• Once each partner has abstracted NDFIS will perform a round robin comparison

• One partner’s actual transmission performance compared with performance predicted by other two partners independent abstractions

Next steps (3/3)

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• Following the calibration of abstractions a common framework for abstraction (including links and nodes) will be formed

• Finally this will be tested on use case scenarios for the intelligent information infrastructure

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Thank you

More information at posters

• Coded-modulation for long‐haul optical fibre transmission: optimising information rates for pragmatic transceivers

• Data-driven optical networking with performance monitoring and machine learning

• On the Limits of Digital Back-Propagation in the Presence of Transceiver Noise

• Single Channel Probe Utilizing the EGN Model to Estimate Link Parameters for Network Abstraction

• State-of-the-art experimental test-bed

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