IEC 61280-2-8:2003

Fibre optic communication subsystem test procedures - Digital systems - Part 2-8: Determination of low BER using Q-factor measurements

IEC 61280-2-8:2003 specifies two main methods for the determination of low BER values by making accelerated measurements. These include the variable decision threshold method (Clause 4) and the variable optical threshold method (Clause 5). In addition, a third method, the sinusoidal interference method, is described in Annex B. Key Words: BER values, variable decision threshold method, variable optical threshold method, sinusoidal interference method

Procédures d'essai des sous-systèmes de télécommunication à fibres optiques - Systèmes numériques - Partie 2-8: Détermination de faible Taux d'Erreur Binaire (TEB) en utilisant des mesures du facteur Q

La CEI 61280-2-8:2003 spécifie deux méthodes principales de détermination de faibles valeurs de TEB en réalisant des mesures accélérées. Celles-ci incluent la méthode du seuil de décision variable (Article 4) et la méthode du seuil optique variable (Article 5). En outre, une troisième méthode, la méthode de brouillage sinusoïdal, est décrite à l'Annexe B. Mots clés: faibles valeurs de TEB, la méthode du seuil de décision variable, la méthode du seuil optique variable, la méthode de brouillage sinusoïdal

General Information

• Status: Published
• Publication Date: 10-Feb-2003
• ICS: 33.180.10 - Fibres and cables
• Technical Committee: SC 86C - Fibre optic systems, sensing and active devices
• Drafting Committee: WG 1 - TC 86/SC 86C/WG 1
• Current Stage: DELPUB - Deleted Publication
• Start Date: 02-Mar-2021
• Completion Date: 13-Dec-2019

Relations

• Revised: IEC 61280-2-8:2021 - Fibre optic communication subsystem test procedures - Part 2-8: Digital systems - Determination of low BER using Q-factor measurements
• Effective Date: 05-Sep-2023

Standards Content (Sample)

INTERNATIONAL IEC
STANDARD
61280-2-8
First edition
2003-02
Fibre optic communication subsystem test
procedures – Digital systems
Part 2-8:
Determination of low BER
using Q-factor measurements
Reference number
Publication numbering
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60000 series. For example, IEC 34-1 is now referred to as IEC 60034-1.

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edition numbers 1.0, 1.1 and 1.2 refer, respectively, to the base publication, the

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INTERNATIONAL IEC
STANDARD
61280-2-8
First edition
2003-02
Fibre optic communication subsystem test
procedures – Digital systems
Part 2-8:
Determination of low BER
using Q-factor measurements
 IEC 2003  Copyright - all rights reserved
No part of this publication may be reproduced or utilized in any form or by any means, electronic or
mechanical, including photocopying and microfilm, without permission in writing from the publisher.
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U
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For price, see current catalogue

– 2 – 61280-2-8  IEC:2003(E)
CONTENTS
FOREWORD . 4

1 Scope . 5

2 Definitions and abbreviated terms . 5

2.1 Definitions . 5

2.2 Abbreviations. 5

3 Measurement of low bit-error ratios . 6

3.1 General considerations . 6
3.2 Background to Q-factor . 7
4 Variable decision threshold method . 9
4.1 Overview . 9
4.2 Apparatus .12
4.3 Sampling and specimens .12
4.4 Procedure.12
4.5 Calculations and interpretation of results .13
4.6 Test documentation .17
4.7 Specification information .17
5 Variable optical threshold method.17
5.1 Overview .17
5.2 Apparatus .18
5.3 Items under test.18
5.4 Procedure for basic optical link .18
5.5 Procedure for self-contained system .19
5.6 Evaluation of results.20
Annex A (normative) Calculation of error bound in the value of Q .22
Annex B (informative) Sinusoidal interference method .24
Bibliography .30
Figure 1 – A sample eye diagram showing patterning effects . 8
Figure 2 – A more accurate measurement technique using a DSO that samples the
noise statistics between the eye centres . 8
Figure 3 – Bit error ratio as a function of decision threshold level .10
Figure 4 – Plot of Q-factor as a function of threshold voltage .10
Figure 5 – Set-up for the variable decision threshold method .12
Figure 6 – Set-up of initial threshold level (approximately at the centre of the eye) .12
Figure 7 – Effect of optical bias .17
Figure 8 – Set-up for optical link or device test.19
Figure 9 – Set-up for system test .19
Figure 10 – Extrapolation of log BER as function of bias .21
Figure B.1 – Set-up for the sinusoidal interference method by optical injection .25
Figure B.2 – Set-up for the sinusoidal interference method by electrical injection .27
Figure B.3 – BER Result from the sinusoidal interference method
(data points and extrapolated line) .28
Figure B.4 – BER versus optical power for three methods .29

61280-2-8  IEC:2003(E) – 3 –
Table 1 – Mean time for the accumulation of 15 errors as a function of BER and bit rate . 6

Table 2 – BER as function of threshold voltage .14

Table 3 – f as a function of D .14
i i
Table 4 – Values of linear regression constants .15

Table 5 – Mean and standard deviation.16

Table 6 – Example of optical bias test.20

Table B.1 – Results for sinusoidal injection.26

– 4 – 61280-2-8  IEC:2003(E)
INTERNATIONAL ELECTROTECHNICAL COMMISSION

____________
FIBRE OPTIC COMMUNICATION SUBSYSTEM TEST PROCEDURES –

DIGITAL SYSTEMS –
Part 2-8: Determination of low BER

using Q-factor measurements
FOREWORD
1) The IEC (International Electrotechnical Commission) is a worldwide organisation for standardisation comprising
all national electrotechnical committees (IEC National Committees). The object of the IEC is to promote
international co-operation on all questions concerning standardisation in the electrical and electronic fields. To
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Standards transparently to the maximum extent possible in their national and regional standards. Any
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6) Attention is drawn to the possibility that some of the elements of this International Standard may be the subject
of patent rights. The IEC shall not be held responsible for identifying any or all such patent rights.
International Standard IEC 61280-2-8 has been prepared by subcommittee 86C: Fibre optic
systems and active devices, of IEC technical committee 86: Fibre optics.
The text of this standard is based on the following documents:
FDIS Report on voting
86C/485/FDIS 86C/505/RVD
Full information on the voting for the approval of this standard can be found in the report on
voting indicated in the above table.
This publication has been drafted in accordance with the ISO/IEC Directives, Part 2.
The committee has decided that the contents of this publication will remain unchanged
until 2010. At this date, the publication will be
• reconfirmed;
• withdrawn;
• replaced by a revised edition, or
• amended.
61280-2-8  IEC:2003(E) – 5 –
FIBRE OPTIC COMMUNICATION SUBSYSTEM TEST PROCEDURES –

DIGITAL SYSTEMS –
Part 2-8: Determination of low BER

using Q-factor measurements
1 Scope
This part of IEC 61280 specifies two main methods for the determination of low BER values by
making accelerated measurements. These include the variable decision threshold method
(Clause 4) and the variable optical threshold method (Clause 5). In addition, a third method,
the sinusoidal interference method, is described in Annex B.
2 Definitions and abbreviated terms
2.1 Definitions
For the purposes of this document, the following terms and definitions apply.
2.1.1
amplified spontaneous emission
ASE
impairment generated in optical amplifiers
2.1.2
bit error ratio
BER
the number bits in error as a ratio of the total number of bits
2.1.3
intersymbol interference
ISI
mutual interference between symbols in a data stream, usually caused by non-linear effects
and bandwidth limitations of the transmission path
2.1.4
Q-factor
Q
ratio of the difference between the mean voltage of the 1 and 0 rails, and the sum of their
standard deviation values
2.2 Abbreviations
cw Continuous wave (normally referring to a sinusoidal wave form)
DC Direct current
DSO Digital sampling oscilloscope
DUT Device under test
PRBS Pseudo-random binary sequence

– 6 – 61280-2-8  IEC:2003(E)
3 Measurement of low bit-error ratios

3.1 General considerations
Fibre optic communication systems and subsystems are inherently capable of providing

exceptionally good error performance, even at very high bit rates. The mean bit error ratio
–12 –20
(BER) may typically lie in the region 10 to 10 , depending on the nature of the system.

While this type of performance is well in excess of practical performance requirements for
digital signals, it gives the advantage of concatenating many links over long distances without
the need to employ error correction techniques.

The measurement of such low error ratios presents special problems in terms of the time taken
to measure a sufficiently large number of errors to obtain a statistically significant result.
Table 1 presents the mean time required to accumulate 15 errors. This number of errors
can be regarded as statistically significant, offering a confidence level of 75 % with a variability
of 50 %.
Table 1 – Mean time for the accumulation of 15 errors
as a function of BER and bit rate
BER
–6 –7 –8 –9 –10 –11 –12 –13 –14 –15
10 10 10 10 10 10 10 10 10 10
Bits/s
1,0M 1,5 s 15 s 2,5 min 25 min 4,2 h 1,7d 17 d 170 d 4,7 years 47 years
2,0M 750 ms 7,5 s 75 s 750 s 2,1 h 21 h 8,8 d 88 d 2,4 years 24 years
10M 150 ms 1,5 s 15 s 2,5 min 25 min 4,2 h 1,7 d 17 d 170 d 4,7 years
50M 30 ms 300 ms 3,0 s 30 s 5,0 min 50 min 8,3 h 3,5 d 35 d 350 d
100M 15 ms 150 ms 1,5 s 15 s 2,5 min 25 min 4,2 h 1,7 d 17 d 170 d
500M 3 ms 30 ms 300 ms 3,0 s 30 s 5,0 min 50 min 8,3 h 3,5 d 35 d
1,0G 1,5 ms 15 ms 150 ms 1,5 s 15 s 2,5 min 25 min 4,2 h 1,7 d 17 d
10G 150 µs 1,5 ms 15 ms 150 ms 1,5 s 15 s 2,5 min 25 min 4,2 h 1,7 d
40G 38 µs 380 µs 3,8 ms 38 ms 380 ms 3,8 s 38 s 6,3 min 63 min 10,4 h
100G 15 µs 150 µs 1,5 ms 15ms 150 ms 1,5 s 15 s 2,5 min 25 min 4,2 h
The times given in Table 1 show that the direct measurement of the low BER values expected
from fibre optic systems is not practical during installation and maintenance operations. One
way of overcoming this difficulty is to artificially impair the signal-to-noise ratio at the receiver in
a controlled manner, thus significantly increasing the BER and reducing the measurement time.
The error performance is measured for various levels of impairment, and the results are then
extrapolated to a level of zero impairment using computational or graphical methods according

to theoretical or empirical regression algorithms.
The difficulty presented by the use of any regression technique for the determination of the
error performance is that the theoretical BER value is related to the level of impairment via
the inverse error function (erfc). This means that very small changes in the impairment
–15
lead to very large changes in BER; for example, in the region of a BER value of 10 a change
of approximately 1 dB in the level of impairment results in a change of three orders of
magnitude in the BER. A further difficulty is that a method based on extrapolation is unlikely
to reveal a levelling off of the BER at only about 3 orders of magnitude below the lowest
measured value.
It should also be noted that, in the case of digitally regenerated sections, the results obtained
apply only to the regenerated section whose receiver is under test. Errors generated in
upstream regenerated sections may generate an error plateau which may have to be taken into
account in the error performance evaluation of the regenerator section under test.

61280-2-8  IEC:2003(E) – 7 –
As noted above, two main methods for the determination of low BER values by making

accelerated measurements are described. These are the variable decision threshold method

(Clause 4) and the variable optical threshold method (Clause 5). In addition, a third method,

the sinusoidal interference method, is described in Annex B.

It should be noted that these methods are applicable to the determination of the error

performance in respect of amplitude-based impairments. Jitter may also affect the error per-
formance of a system, and its effect requires other methods of determination. If the error
performance is dominated by jitter impairments, the amplitude-based methods described in this

standard will lead to BER values which are lower than the actual value.

The variable decision threshold method is the procedure which can most accurately measure

the Q-factor and the BER for optical systems with unknown or unpredictable noise statistics. A
key limitation, however, to the use of the variable threshold method to measure Q-factor and
BER is the need to have access to the receiver electronics in order to manipulate the decision
threshold. For systems where such access is not available it may be useful to utilize the
alternative variable optical threshold method. Both methods are capable of being automated in
respect of measurement and computation of the results
3.2 Background to Q-factor
The Q-factor is the signal-to-noise ratio (SNR) at the decision circuit and is typically expressed
as [3] :
µ − µ
1 0
(1)
Q =
σ +σ
1 0
where µ and µ are the mean voltage levels of the “1” and “0” rails, respectively, and σ and
1 0 1
σ are the standard deviation values of the noise distribution on the “1” and “0” rails,
respectively.
An accurate estimation of a system’s transmission performance, or Q-factor, must take into
consideration the effects of all sources of performance degradation, both fundamental and
those due to real-world imperfections. Two important sources are amplified spontaneous
emission (ASE) noise and intersymbol interference (ISI). Additive noise originates primarily
from ASE of optical amplifiers. ISI arises from many effects, such as chromatic dispersion,
fibre non-linearities, multi-path interference, polarization-mode dispersion and use of
electronics with finite bandwidth. There may be other effects as well, for example, a poor
impedance match can cause impairments such as long fall times or ringing on a waveform.
One possible method to measure Q-factor is the voltage histogram method in which a digital
sampling oscilloscope is used to measure voltage histograms at the centre of a binary eye to

estimate the waveform’s Q-factor [4]. In this method, a pattern generator is used as a stimulus
and the oscilloscope is used to measure the received eye opening and the standard deviation
of the noise present in both voltage rails. As a rough approximation, the edge of visibility of the
noise represents the 3σ points of an assumed Gaussian distribution. The advantage of using
an oscilloscope to measure the eye is that it can be done rapidly on real traffic with a minimum
of equipment.
The oscilloscope method for measuring the Q-factor has several shortcomings. When used to
measure the eye of high-speed data (of the order of several Gbit/s), the oscilloscope’s limited
digital sampling rate (often in the order of a few hundred kilohertz) allows only a small minority
of the high-speed data stream to be used in the Q-factor measurement. Longer observation
times could reduce the impact of the slow sampling. A more fundamental shortcoming is that
the Q estimates derived from the voltage histograms at the eye centre are often inaccurate.
Various patterning effects and added noise from the front-end electronics of the oscilloscope
can often obscure the real variance of the noise.

Figures in square brackets refer to the bibliography.

– 8 – 61280-2-8  IEC:2003(E)
Figure 1 shows a sample eye diagram made on an operating system. It can be seen in this

figure that the vertical histograms through the centre of the eye show patterning effects (less

obvious is the noise added by the front-end electronics of the oscilloscope). It is difficult to

predict the relationship between the Q measured this way and the actual BER measured with

a test set.
Gaussian
approximation
Actual
Decision circuit operates in this region
distribution
IEC  042/03
NOTE The data for measuring the Q-factor is obtained from the tail of the Gaussian distributions.
Figure 1 – A sample eye diagram showing patterning effects
Figure 2 shows another possible way of measuring Q-factor using an oscilloscope. The idea is
to use the centre of the eye to estimate the eye opening and use the area between eye centres
to estimate the noise. Pattern effect contributions to the width of the histogram would then be
reduced. A drawback to this method is that it relies on measurements made on a portion of the
eye that the receiver does not really ever use.
Measure noise here
Measure eye opening here
μ − μ σ − σ
1 0 1 0
Noise estimate here excludes isolated “1’s”
IEC  043/03
Figure 2 – A more accurate measurement technique using a DSO
that samples the noise statistics between the eye centres

61280-2-8  IEC:2003(E) – 9 –
It is tempting to conclude that the estimates for σ and σ would tend to be overestimated and
1 0
that the resulting Q measurements would always form a lower bound to the actual Q for either

of these oscilloscope-based methods. That is not necessarily the case. It is possible that the

histogram distributions can be distorted in other ways, for example, skewed in such a way that

the mean values overestimate the eye opening – and the resulting Q will actually not be a lower

bound. There is, unfortunately, no easily characterized relationship between oscilloscope-
derived Q measurements and BER performance.

4 Variable decision threshold method

4.1 Overview
This method of estimating the Q-factor relies on using a receiver front-end with a variable
decision threshold. Some means of measuring the BER of the system is required. Typically the
measurement is performed with an error test set using a pseudo-random binary sequence
(PRBS), but there are alternate techniques which allow operation with live traffic. The
measurement relies on the fact that for a data eye with Gaussian statistics, the BER may be
calculated analytically as follows:
 
 | V − µ |  | V − µ |
th 1 th 0


BER(V ) = erfc  + erfc  (2)
th
   
 
2 σ σ
 1   0 
 
where
μ , μ and σ , σ are the mean and standard deviation of the “1” and “0” data rails;
1 0 1 0
V is the decision threshold level;
th
erfc(.) is the complementary error function given by
∞
2 2
1 1
−β / 2 − x /2
erfc(x) = e dβ ≅ e (3)
∫
2π x 2π
x
(The approximation is nearly exact for x > 3.)
The BER, given in equation 2, is the sum of two terms. The first term is the conditional
probability of deciding that a “0” has been received when a “1” has been sent, and the second
term is the probability of deciding that a “1” has been received when a “0” has been sent.

In order to implement this technique, the BER is measured as a function of the threshold
voltage (see Figure 3). Equation 2 is then used to convert the data into a plot of the Q-factor
versus threshold, where the Q-factor is the argument of the complementary error function of
either term in equation 2. To make the conversion, the approximation is made that the BER is
dominated by only one of the terms in equation 2 according to whether the threshold is closer
to the “1's” or the “0's” rail of the eye diagram.

– 10 – 61280-2-8  IEC:2003(E)

−
−6
−8
−10
−12
−14
−16
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8
Threshold voltage
IEC  044/03
Figure 3 – Bit error ratio as a function of decision threshold level
Figure 4 shows the results of converting the data in Figure 3 into a plot of Q-factor versus
threshold. The optimum Q-factor value as well as the optimum threshold setting needed to
achieve this Q-factor is obtained from the intersection of the two best-fit lines through the data.
This technique is described in detail in [2].
Optimum Q
|Slope| = 1/σ
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8
µ Threshold voltage µ
0 1
Optimum threshold
IEC  045/03
Figure 4 – Plot of Q-factor as a function of threshold voltage
BER
Q from BER
61280-2-8  IEC:2003(E) – 11 –

The optimum threshold as well as the optimal Q can be obtained analytically by making use of

the following approximation [1] for the inverse error function:

−1
  1 
(4)
log erfc ( x) ≈1,192 − 0,6681 x − 0,0162 x
 
 
 
 
where x is the log(BER).
–5 –10
NOTE Equation (4) is accurate to ±0,2 % over the range of BER from 10 to 10 .

After evaluating the inverse error function, the data is plotted against the decision threshold

level, V . As shown in Figure 4, a straight line is fitted to each set of data by linear regression.
th
The equivalent variance and mean for the Q calculation are given by the slope and intercept
respectively.
The minimum BER can be shown to occur at an optimal threshold, V , when the two
th-optimal
terms in the argument in equation 2 are equal, that is
(µ −V ) (V − µ )
1 th−optimal th−optimal 0
= = Q (5)
opt
σ σ
1 0
An explicit expression for V in terms of μ and σ can be derived from equation (5)
th-optimal 1,0 1,0
to be:
σ µ + σ µ
0 1 1 0
V = (6)
th−optimal
σ +σ
0 1
The value of Q is obtained from equation 1. The residual BER at the optimal threshold can
opt
be obtained from equation 2 and is approximately
−()Q / 2
opt
e
BER ≅ (7)
optimal
Q 2π
opt
NOTE This approximation is nearly exact for Q >3.
opt
It should be noted that even though the variable threshold method makes use of Gaussian
statistics, it provides accurate results for systems that have non-Gaussian noise statistics as
well, for example, the non-Gaussian statistics that occur in a typical optically amplified system
[4]. This can be understood by examining Figure 1. The decision circuit of a receiver operates
only on the interior region of the eye. This means that the only part of the vertical histogram
that it uses is the “tail” that extends into the eye. The variable decision threshold method
amounts to constructing a Gaussian approximation to the tail of the real distribution in the
centre region of the eye where it affects the receiver operation directly. As the example in
Figure 1 shows, this Gaussian approximation will not reproduce the actual histogram
distribution at all, but it does not need to, for purposes of Q estimation.
Another way to view the variable decision threshold technique is to imagine replacing the real
data eye with a fictitious eye having Gaussian statistics. The two eye diagrams have the same
BER versus decision threshold voltage behaviour, so it is reasonable to assign them the same
equivalent Q value, even though the details of the full eye diagram may be very different. Of
course, it does need to be kept in mind that this analysis will not work for systems dominated
by noise sources whose “tails” are not easily approximated to be Gaussian in shape; as, for
example, would occur in a system dominated by cross-talk or modal noise. In taking these
measurements, an inability to fit the data of Q-factor versus threshold to a straight line would
provide a good indication of the presence of such noise sources.
Experimentally it has been found that the Q values measured using the variable decision
threshold method have a statistically valid level of correlation with the actual BER
measurements.
...

IEC 61280-2-8 ®
Edition 1.0 2003-02
INTERNATIONAL
STANDARD
NORME
INTERNATIONALE
colour
inside
Fibre optic communication subsystem test procedures – Digital systems –
Part 2-8: Determination of low BER using Q-factor measurements

Procédures d’essai des sous-systèmes de télécommunication à fibres
optiques – Systèmes numériques –
Partie 2-8: Détermination de faible Taux d'Erreur Binaire (TEB) en utilisant
des mesures du facteur Q
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IEC 61280-2-8 ®
Edition 1.0 2003-02
INTERNATIONAL
STANDARD
NORME
INTERNATIONALE
colour
inside
Fibre optic communication subsystem test procedures – Digital systems –

Part 2-8: Determination of low BER using Q-factor measurements

Procédures d’essai des sous-systèmes de télécommunication à fibres

optiques – Systèmes numériques –

Partie 2-8: Détermination de faible Taux d'Erreur Binaire (TEB) en utilisant

des mesures du facteur Q
INTERNATIONAL
ELECTROTECHNICAL
COMMISSION
COMMISSION
ELECTROTECHNIQUE
PRICE CODE
INTERNATIONALE
U
CODE PRIX
ICS 33.180.10 ISBN 978-2-83220-354-5

– 2 – 61280-2-8  IEC:2003
CONTENTS
FOREWORD . 4

1 Scope . 6
2 Definitions and abbreviated terms . 6
2.1 Definitions . 6
2.2 Abbreviations . 6
3 Measurement of low bit-error ratios . 7
3.1 General considerations . 7
3.2 Background to Q-factor . 8
4 Variable decision threshold method . 10
4.1 Overview . 10
4.2 Apparatus . 13
4.3 Sampling and specimens . 13
4.4 Procedure . 13
4.5 Calculations and interpretation of results . 14
4.6 Test documentation . 18
4.7 Specification information . 18
5 Variable optical threshold method . 18
5.1 Overview . 18
5.2 Apparatus . 19
5.3 Items under test. 19
5.4 Procedure for basic optical link . 19
5.5 Procedure for self-contained system . 20
5.6 Evaluation of results . 21

Annex A (normative) Calculation of error bound in the value of Q . 23
Annex B (informative) Sinusoidal interference method . 25

Bibliography . 31

Figure 1 – A sample eye diagram showing patterning effects . 9
Figure 2 – A more accurate measurement technique using a DSO that samples the
noise statistics between the eye centres . 9
Figure 3 – Bit error ratio as a function of decision threshold level . 11
Figure 4 – Plot of Q-factor as a function of threshold voltage . 11
Figure 5 – Set-up for the variable decision threshold method . 13
Figure 6 – Set-up of initial threshold level (approximately at the centre of the eye) . 13
Figure 7 – Effect of optical bias . 18
Figure 8 – Set-up for optical link or device test . 20
Figure 9 – Set-up for system test . 20
Figure 10 – Extrapolation of log BER as function of bias . 22
Figure B.1 – Set-up for the sinusoidal interference method by optical injection . 26
Figure B.2 – Set-up for the sinusoidal interference method by electrical injection . 28
Figure B.3 – BER Result from the sinusoidal interference method
(data points and extrapolated line) . 29
Figure B.4 – BER versus optical power for three methods . 30

61280-2-8  IEC:2003 – 3 –
Table 1 – Mean time for the accumulation of 15 errors as a function of BER and bit rate . 7
Table 2 – BER as function of threshold voltage . 15
Table 3 – f as a function of D . 16
i i
Table 4 – Values of linear regression constants . 16
Table 5 – Mean and standard deviation . 17
Table 6 – Example of optical bias test . 21
Table B.1 – Results for sinusoidal injection . 27

– 4 – 61280-2-8  IEC:2003
INTERNATIONAL ELECTROTECHNICAL COMMISSION
____________
FIBRE OPTIC COMMUNICATION SUBSYSTEM TEST PROCEDURES –
DIGITAL SYSTEMS –
Part 2-8: Determination of low BER
using Q-factor measurements
FOREWORD
1) The IEC (International Electrotechnical Commission) is a worldwide organisation for standardisation comprising
all national electrotechnical committees (IEC National Committees). The object of the IEC is to promote
international co-operation on all questions concerning standardisation in the electrical and electronic fields. To
this end and in addition to other activities, the IEC publishes International Standards. Their preparation is
entrusted to technical committees; any IEC National Committee interested in the subject dealt with may
participate in this preparatory work. International, governmental and non-governmental organisations liasing with
the IEC also participate in this preparation. The IEC collaborates closely with the International Organisation for
Standardisation (ISO) in accordance with conditions determined by agreement between the two organisations.
2) The formal decisions or agreements of the IEC on technical matters express, as nearly as possible, an
international consensus of opinion on the relevant subjects since each technical committee has representation
from all interested National Committees.
3) The documents produced have the form of recommendations for international use and are published in the form
of standards, technical specifications, technical reports or guides and they are accepted by the National
Committees in that sense.
4) In order to promote international unification, IEC National Committees undertake to apply IEC International
Standards transparently to the maximum extent possible in their national and regional standards. Any
divergence between the IEC Standard and the corresponding national or regional standard shall be clearly
indicated in the latter.
5) The IEC provides no marking procedure to indicate its approval and cannot be rendered responsible for any
equipment declared to be in conformity with one of its standards.
6) Attention is drawn to the possibility that some of the elements of this International Standard may be the subject
of patent rights. The IEC shall not be held responsible for identifying any or all such patent rights.
International Standard IEC 61280-2-8 has been prepared by subcommittee 86C: Fibre optic
systems and active devices, of IEC technical committee 86: Fibre optics.
This bilingual version (2012-09) corresponds to the monolingual English version, published in
2003-02.
The text of this standard is based on the following documents:
FDIS Report on voting
86C/485/FDIS 86C/505/RVD
Full information on the voting for the approval of this standard can be found in the report on
voting indicated in the above table.
The French version of this standard has not been voted upon.
This publication has been drafted in accordance with the ISO/IEC Directives, Part 2.

61280-2-8  IEC:2003 – 5 –
The committee has decided that the contents of this publication will remain unchanged
until 2010. At this date, the publication will be
• reconfirmed;
• withdrawn;
• replaced by a revised edition, or
• amended.
IMPORTANT – The 'colour inside' logo on the cover page of this publication indicates
that it contains colours which are considered to be useful for the correct understanding
of its contents. Users should therefore print this document using a colour printer.

– 6 – 61280-2-8  IEC:2003
FIBRE OPTIC COMMUNICATION SUBSYSTEM TEST PROCEDURES –
DIGITAL SYSTEMS –
Part 2-8: Determination of low BER
using Q-factor measurements
1 Scope
This part of IEC 61280 specifies two main methods for the determination of low BER values by
making accelerated measurements. These include the variable decision threshold method
(Clause 4) and the variable optical threshold method (Clause 5). In addition, a third method,
the sinusoidal interference method, is described in Annex B.
2 Definitions and abbreviated terms
2.1 Definitions
For the purposes of this document, the following terms and definitions apply.
2.1.1
amplified spontaneous emission
ASE
impairment generated in optical amplifiers
2.1.2
bit error ratio
BER
the number bits in error as a ratio of the total number of bits
2.1.3
intersymbol interference
ISI
mutual interference between symbols in a data stream, usually caused by non-linear effects
and bandwidth limitations of the transmission path
2.1.4
Q-factor
Q
ratio of the difference between the mean voltage of the 1 and 0 rails, and the sum of their
standard deviation values
2.2 Abbreviations
cw Continuous wave (normally referring to a sinusoidal wave form)
DC Direct current
DSO Digital sampling oscilloscope
DUT Device under test
PRBS Pseudo-random binary sequence

61280-2-8  IEC:2003 – 7 –
3 Measurement of low bit-error ratios
3.1 General considerations
Fibre optic communication systems and subsystems are inherently capable of providing
exceptionally good error performance, even at very high bit rates. The mean bit error ratio
–12 –20
(BER) may typically lie in the region 10 to 10 , depending on the nature of the system.
While this type of performance is well in excess of practical performance requirements for
digital signals, it gives the advantage of concatenating many links over long distances without
the need to employ error correction techniques.
The measurement of such low error ratios presents special problems in terms of the time taken
to measure a sufficiently large number of errors to obtain a statistically significant result.
Table 1 presents the mean time required to accumulate 15 errors. This number of errors
can be regarded as statistically significant, offering a confidence level of 75 % with a variability
of 50 %.
Table 1 – Mean time for the accumulation of 15 errors
as a function of BER and bit rate
BER
–6 –7 –8 –9 –10 –11 –12 –13 –14 –15
10 10 10 10 10 10 10 10 10 10
Bits/s
1,0M 1,5 s 15 s 2,5 min 25 min 4,2 h 1,7d 17 d 170 d 4,7 years 47 years
2,0M 750 ms 7,5 s 75 s 750 s 2,1 h 21 h 8,8 d 88 d 2,4 years 24 years
10M 150 ms 1,5 s 15 s 2,5 min 25 min 4,2 h 1,7 d 17 d 170 d 4,7 years
50M 30 ms 300 ms 3,0 s 30 s 5,0 min 50 min 8,3 h 3,5 d 35 d 350 d
100M 15 ms 150 ms 1,5 s 15 s 2,5 min 25 min 4,2 h 1,7 d 17 d 170 d
500M 3 ms 30 ms 300 ms 3,0 s 30 s 5,0 min 50 min 8,3 h 3,5 d 35 d
1,0G 1,5 ms 15 ms 150 ms 1,5 s 15 s 2,5 min 25 min 4,2 h 1,7 d 17 d
10G 150 μs 1,5 ms 15 ms 150 ms 1,5 s 15 s 2,5 min 25 min 4,2 h 1,7 d
40G 38 μs 380 μs 3,8 ms 38 ms 380 ms 3,8 s 38 s 6,3 min 63 min 10,4 h
100G 15 μs 150 μs 1,5 ms 15ms 150 ms 1,5 s 15 s 2,5 min 25 min 4,2 h

The times given in Table 1 show that the direct measurement of the low BER values expected
from fibre optic systems is not practical during installation and maintenance operations. One
way of overcoming this difficulty is to artificially impair the signal-to-noise ratio at the receiver in
a controlled manner, thus significantly increasing the BER and reducing the measurement time.
The error performance is measured for various levels of impairment, and the results are then
extrapolated to a level of zero impairment using computational or graphical methods according
to theoretical or empirical regression algorithms.
The difficulty presented by the use of any regression technique for the determination of the
error performance is that the theoretical BER value is related to the level of impairment via
the inverse error function (erfc). This means that very small changes in the impairment
–15
lead to very large changes in BER; for example, in the region of a BER value of 10 a change
of approximately 1 dB in the level of impairment results in a change of three orders of
magnitude in the BER. A further difficulty is that a method based on extrapolation is unlikely
to reveal a levelling off of the BER at only about 3 orders of magnitude below the lowest
measured value.
It should also be noted that, in the case of digitally regenerated sections, the results obtained
apply only to the regenerated section whose receiver is under test. Errors generated in
upstream regenerated sections may generate an error plateau which may have to be taken into
account in the error performance evaluation of the regenerator section under test.

– 8 – 61280-2-8  IEC:2003
As noted above, two main methods for the determination of low BER values by making
accelerated measurements are described. These are the variable decision threshold method
(Clause 4) and the variable optical threshold method (Clause 5). In addition, a third method,
the sinusoidal interference method, is described in Annex B.
It should be noted that these methods are applicable to the determination of the error
performance in respect of amplitude-based impairments. Jitter may also affect the error per-
formance of a system, and its effect requires other methods of determination. If the error
performance is dominated by jitter impairments, the amplitude-based methods described in this
standard will lead to BER values which are lower than the actual value.
The variable decision threshold method is the procedure which can most accurately measure
the Q-factor and the BER for optical systems with unknown or unpredictable noise statistics. A
key limitation, however, to the use of the variable threshold method to measure Q-factor and
BER is the need to have access to the receiver electronics in order to manipulate the decision
threshold. For systems where such access is not available it may be useful to utilize the
alternative variable optical threshold method. Both methods are capable of being automated in
respect of measurement and computation of the results
3.2 Background to Q-factor
The Q-factor is the signal-to-noise ratio (SNR) at the decision circuit and is typically expressed
as [3] :
μ −μ
1 0
(1)
Q =
σ +σ
1 0
where µ and µ are the mean voltage levels of the “1” and “0” rails, respectively, and σ and
1 0 1
σ are the standard deviation values of the noise distribution on the “1” and “0” rails,
respectively.
An accurate estimation of a system’s transmission performance, or Q-factor, must take into
consideration the effects of all sources of performance degradation, both fundamental and
those due to real-world imperfections. Two important sources are amplified spontaneous
emission (ASE) noise and intersymbol interference (ISI). Additive noise originates primarily
from ASE of optical amplifiers. ISI arises from many effects, such as chromatic dispersion,
fibre non-linearities, multi-path interference, polarization-mode dispersion and use of
electronics with finite bandwidth. There may be other effects as well, for example, a poor
impedance match can cause impairments such as long fall times or ringing on a waveform.
One possible method to measure Q-factor is the voltage histogram method in which a digital
sampling oscilloscope is used to measure voltage histograms at the centre of a binary eye to
estimate the waveform’s Q-factor [4]. In this method, a pattern generator is used as a stimulus
and the oscilloscope is used to measure the received eye opening and the standard deviation
of the noise present in both voltage rails. As a rough approximation, the edge of visibility of the
noise represents the 3σ points of an assumed Gaussian distribution. The advantage of using
an oscilloscope to measure the eye is that it can be done rapidly on real traffic with a minimum
of equipment.
The oscilloscope method for measuring the Q-factor has several shortcomings. When used to
measure the eye of high-speed data (of the order of several Gbit/s), the oscilloscope’s limited
digital sampling rate (often in the order of a few hundred kilohertz) allows only a small minority
of the high-speed data stream to be used in the Q-factor measurement. Longer observation
times could reduce the impact of the slow sampling. A more fundamental shortcoming is that
the Q estimates derived from the voltage histograms at the eye centre are often inaccurate.
Various patterning effects and added noise from the front-end electronics of the oscilloscope
can often obscure the real variance of the noise.
____________
Figures in square brackets refer to the bibliography.

61280-2-8  IEC:2003 – 9 –
Figure 1 shows a sample eye diagram made on an operating system. It can be seen in this
figure that the vertical histograms through the centre of the eye show patterning effects (less
obvious is the noise added by the front-end electronics of the oscilloscope). It is difficult to
predict the relationship between the Q measured this way and the actual BER measured with
a test set.
NOTE The data for measuring the Q-factor is obtained from the tail of the Gaussian distributions.
Figure 1 – A sample eye diagram showing patterning effects
Figure 2 shows another possible way of measuring Q-factor using an oscilloscope. The idea is
to use the centre of the eye to estimate the eye opening and use the area between eye centres
to estimate the noise. Pattern effect contributions to the width of the histogram would then be
reduced. A drawback to this method is that it relies on measurements made on a portion of the
eye that the receiver does not really ever use.

Figure 2 – A more accurate measurement technique using a DSO
that samples the noise statistics between the eye centres

– 10 – 61280-2-8  IEC:2003
It is tempting to conclude that the estimates for σ and σ would tend to be overestimated and
1 0
that the resulting Q measurements would always form a lower bound to the actual Q for either
of these oscilloscope-based methods. That is not necessarily the case. It is possible that the
histogram distributions can be distorted in other ways, for example, skewed in such a way that
the mean values overestimate the eye opening – and the resulting Q will actually not be a lower
bound. There is, unfortunately, no easily characterized relationship between oscilloscope-
derived Q measurements and BER performance.
4 Variable decision threshold method
4.1 Overview
This method of estimating the Q-factor relies on using a receiver front-end with a variable
decision threshold. Some means of measuring the BER of the system is required. Typically the
measurement is performed with an error test set using a pseudo-random binary sequence
(PRBS), but there are alternate techniques which allow operation with live traffic. The
measurement relies on the fact that for a data eye with Gaussian statistics, the BER may be
calculated analytically as follows:
 
 | V −μ |  | V −μ |
th 1 th 0
 
BER(V )= erfc + erfc  (2)
th
   
 
2 σ σ
 1   0 
 
where
µ ,µ and σ , σ are the mean and standard deviation of the “1” and “0” data rails;
1 0 1 0
V is the decision threshold level;
th
erfc(.) is the complementary error function given by
∞
2 2
1 1
−β / 2 − x /2
erfc(x)= e dβ≅ e (3)
∫
2π x 2π
x
(The approximation is nearly exact for x > 3.)
The BER, given in equation 2, is the sum of two terms. The first term is the conditional
probability of deciding that a “0” has been received when a “1” has been sent, and the second
term is the probability of deciding that a “1” has been received when a “0” has been sent.
In order to implement this technique, the BER is measured as a function of the threshold
voltage (see Figure 3). Equation 2 is then used to convert the data into a plot of the Q-factor
versus threshold, where the Q-factor is the argument of the complementary error function of
either term in equation 2. To make the conversion, the approximation is made that the BER is
dominated by only one of the terms in equation 2 according to whether the threshold is closer
to the “1's” or the “0's” rail of the eye diagram.

61280-2-8  IEC:2003 – 11 –
Figure 3 – Bit error ratio as a function of decision threshold level
Figure 4 shows the results of converting the data in Figure 3 into a plot of Q-factor versus
threshold. The optimum Q-factor value as well as the optimum threshold setting needed to
achieve this Q-factor is obtained from the intersection of the two best-fit lines through the data.
This technique is described in detail in [2].

Figure 4 – Plot of Q-factor as a function of threshold voltage

– 12 – 61280-2-8  IEC:2003
The optimum threshold as well as the optimal Q can be obtained analytically by making use of
the following approximation [1] for the inverse error function:
−1
 1 
 
(4)
log erfc(x) ≈1,192− 0,6681x− 0,0162x
 
 
 
 
where x is the log(BER).
–5 –10
NOTE Equation (4) is accurate to ±0,2 % over the range of BER from 10 to 10 .
After evaluating the inverse error function, the data is plotted against the decision threshold
level, V . As shown in Figure 4, a straight line is fitted to each set of data by linear regression.
th
The equivalent variance and mean for the Q calculation are given by the slope and intercept
respectively.
The minimum BER can be shown to occur at an optimal threshold, V , when the two
th-optimal
terms in the argument in equation 2 are equal, that is
(μ −V ) (V −μ)
1 th−optimal th−optimal 0
= = Q (5)
opt
σ σ
1 0
An explicit expression for V in terms of µ and σ can be derived from equation (5)
th-optimal 1,0 1,0
to be:
σμ+σμ
0 1 1 0
V = (6)
th−optimal
σ +σ
0 1
The value of Q is obtained from equation 1. The residual BER at the optimal threshold can
opt
be obtained from equation 2 and is approximately
−(Q / 2)
opt
e
BER ≅ (7)
optimal
Q 2π
opt
NOTE This approximation is nearly exact for Q >3.
opt
It should be noted that even though the variable threshold method makes use of Gaussian
statistics, it provides accurate results for systems that have non-Gaussian noise statistics as
well, for example, the non-Gaussian statistics that occur in a typical optically amplified system
[4]. This can be understood by examining Figure 1. The decision circuit of a receiver operates
only on the interior region of the eye. This means that the only part of the vertical histogram
that it uses is the “tail” that extends into the eye. The variable decision threshold method
amounts to constructing a Gaussian approximation to the tail of the real distribution in the
centre region of the eye where it affects the receiver operation directly. As the example in
Figure 1 shows, this Gaussian approximation will not reproduce the actual histogram
distribution at all, but it does not need to, for purposes of Q estimation.
Another way to view the variable decision threshold technique is to imagine replacing the real
data eye with a fictitious eye having Gaussian statistics. The two eye diagrams have the same
BER versus decision threshold voltage behaviour, so it is reasonable to assign them the same
equivalent Q value, even though the details of the full eye diagram may be very different. Of
course, it does need to be kept in mind that this analysis will not work for systems dominated
by noise sources whose “tails” are not easily approximated to be Gaussian in shape; as, for
example, would occur in a system dominated by cross-talk or modal noise. In taking these
measurements, an inability to fit the data of Q-factor versus threshold to a straight line would
provide a good indication of the presence of such noise sources.
Experimentally it has been found that the Q values measured using the variable decision
threshold method have a statistically valid level of correlation with the actual BER
measurements.
61280-2-8  IEC:2003 – 13 –
4.2 Apparatus
An error performance analyser consisting of a pattern generator and a bit error rate detector.
4.3 Sampling and specimens
The device under test (DUT) is a fibre optic digital system, consisting of an electro-optical
transmitter at one end and an opto-electronic receiver at the other end. In between the
transmitter and the receiver can be an optical network with links via optical fibres (for example,
a DWDM network).
4.4 Procedure
Data for the “Q” measurement is collected at both the top “1” and bottom “0” regions of the eye
−5 −10
as BER (over the range 10 to 10 ) versus decision threshold. The equivalent mean (μ) and
variance (σ) of the 1s and 0s are determined by fitting this data to a Gaussian characteristic.
Detector/
Pattern generator DUT Clock recovery
preamp. Low-
circuit
pass
(Fiber-optic
Data
filter
transmitter
& link)
Clock
Clock
Data
Error detector/
Computer
(threshold
set here)
IEC  046/03
Figure 5 – Set-up for the variable decision threshold method
The Q-factor is then calculated using equation 1.
Connect the pattern generator and error detector to the system under test in accordance with
figure 5.
Set the clock source to the desired frequency.
Set up the pattern generator’s pattern, data and clock amplitude, offset, polarity and
termination as required.
Set up the error detector’s pattern, data polarity and termination as required.
Set the decision threshold voltage and data input delay to achieve a sampling point that is
approximately in the centre of the data eye as shown in Figure 6. This is the initial
sampling point.
Sampling point
IEC  047/03
Figure 6 – Set-up of initial threshold level (approximately at the centre of the eye)
Enable the error detector's gating function and set it to gate by errors, for a minimum of 10,
100 or 1 000 errors.
– 14 – 61280-2-8  IEC:2003
Adjust the error detector's decision threshold voltage in a positive direction until the measured
–10
BER increases to a value greater than 1 × 10 . Note the decision threshold voltage (V ) and
b1
BER.
–5
Increase the decision threshold voltage until the BER rises above 10 and note the decision
threshold voltage (V ) and the BER.
a1
Note the difference between the two threshold values V and V and choose a step size
a1 b1
(V ) that provides a reasonable number (greater than 5) of measurement points between
step1
these two decision threshold extremes. Starting from the threshold value V decrease the
a1
threshold value by the step size, V . At each step run a gating measurement on the error
step1
detector. Record the measured BER value and the corresponding decision threshold voltage.
The Gating measurement from the error detector accumulates data and error information until
the minimum number of errors (as specified in 5.5) have been recorded. Selecting a larger
minimum number of errors provides a statistically more accurate BER but at the expense of
measurement time, particularly when measuring the low BER values. For a statistically
significant result, the number of errors counted should not be less than 15.
–10
Continue until the measured BER falls below 10 . This set of decision threshold voltage
versus BER is the “1” data set.
Adjust decision threshold voltage back to the initial sampling point value and then continue in a
–10
negative direction until the BER increases again to greater than 10 . Note down the threshold
value (V ) and BER.
b0
–5
Decrease the decision threshold voltage until the BER rises above 10 and note the decision
threshold voltage (V ) and the BER.
a0
Note the difference between the two threshold values V and V and choose a step size
a0 b0
(V ) that provides reasonable number (greater than 5) of measurement points between
step0
these two decision threshold extremes. Starting from the threshold value V , increase the
a0
threshold value by the step size, V . At each step run a gating measurement on the error
step0
detector. Record the measured BER and the corresponding decision threshold voltage.
–10
Continue until the measured BER falls below 1 × 10 . This set of decision threshold voltage
versus BER is the “0” data set.
4.5 Calculations and interpretation of results
4.5.1 Sets of data
The procedure in 4.7 provides two sets (for the “0” and “1” rails) of data in the form:
D , BER 
1 1
 
D , BER
2 2
 
 
.
 
.
 
 
D , BER
n n
 
where
D is the decision threshold voltage for “i”-th reading (for i =1, 2…,n);
i
BER is the bit error rate for “i”-th reading (for i = 1, 2…,n);
i
n is the total number of data pairs.
NOTE The total number of data pairs for the “0” and “1” rails need not be equal.
As an example, the following voltage and BER values were obtained in a real-life experiment.

61280-2-8  IEC:2003 – 15 –
Table 2 – BER as function of threshold voltage
“1” rail “0” rail
Threshold voltage BER Threshold voltage BER
V V
–1,75 5,18E-05 –4,37 8,76E-05
–1,80 2,09E-05 –4,34 1,90E-05
–1,85 7,33E-06 –4,31 5,18E-06
–1,90 2,77E-06 –4,28 1,06E-06
–1,95 9,61E-07 –4,25 2,12E-07
–2,00 1,96E-07 –4,22 3,45E-08
–2,05 6,30E-08 –4,19 3,52E-09
–2,10 1,95E-08 –4,16 2,77E-10
–2,15 3,45E-09
–2,20 1,39E-09
4.5.2 Convert BER using inverse error function
Each BER value is then converted through an inverse error function, using the following
approximation given in equation 4.
−1
 
1 
(8)
f = log erfc(x ) = 1,192− 0,6681{x}− 0,0162{x}
 
i i i i
 
  
n
where x = log (BER ).
i 10 i
This will produce two sets of data (for the “1” and “0”) of the form:
D , f 
1 1
 
D , f
2 2
 
 
.
 
.
 
 
D , f
n n
 
that should approximately fit a straight line.
Using the values given in Table 2, we get the following sets of data.

– 16 – 61280-2-8  IEC:2003
Table 3 – f as a function of D
i i
“1” rail “0” rail
D (volts) f D (volts) f
i i i i
–1,75 3,7578 –4,37 3,6360
–1,80 3,9638 –4,34 3,9847
–1,85 4,1956 –4,31 4,2706
–1,90 4,4043 –4,28 4,6052
–1,95 4,6257 –4,25 4,9293
–2,00 4,9449 –4,22 5,2757
–2,05 5,1629 –4,19 5,6823
–2,10 5,3799 – 4,16 6,0975
–2,15 5,6858
–2,20 5,8390
4.5.3 Linear regression
Using the above data, a linear regression technique is used to fit, in turn, each set of data
to a straight line with an equation of the form:
Y= A+ BX
where
Y = erf (BER) (inverse error function of BER),
c
X = D (decision threshold voltage)
With n points of data per set, then, for both the top (“1”) and bottom (“0”) data sets, the
following calculations should be performed [6]:
 
( X)( Y)
∑ ∑
 
( )( )
X Y
∑ ∑ XY−
∑
XY−  
n
∑
n 2  
B= R =
2 2
  
( X)
( X) ( Y)
∑   
2 ∑ ∑
2 2
X −
X − Y −
∑   
∑ ∑
n
n n
  
  
Y X
∑ ∑
A= − B
n n
where
R is the coefficient of determination (a measure of how well the data fits a straight line);
is the sum of values from 1 to n.
∑
Using the values given in Table 3, we get:
Table 4 – Values of linear regression constants
“1” rail “0” rail
A B R A B R
–4,6125 –4,7638 0,9989 53,989 11,5307 0,9984

61280-2-8  IEC:2003 – 17 –
4.5.4 Standard deviation and mean
σ=− (standard deviation of “1” or “0” noise region),
B
−A
µ= (mean of '1' or '0' noise region).
B
Calculate μ ,σ from the “1” set of data and μ , σ from the “0” set of data.
1 1 0 0
Using the example in Table 4, we get:
Table 5 – Mean and standard deviation
“1” rail “0” rail
μ σ μ σ
1 1 0 0
–0,9682 0,2099 –4,6822 0,0867249

4.5.5 Optimum decision threshold
µ −µ
1 0
Q =
opt
σ +σ
1 0
σ µ +σ µ
0 1 1 0
And thus the optimum decision threshold =
σ +σ
1 0
For the example given earlier, using the value derived for Q of 12.52, the optimal decision
opt
threshold is –3,596 volts.
4.5.6 BER optimum decision threshold
Also the predicted residual BER at the optimum decision threshold is given by
 
Q
 
−
 
 
e
BER=
Q 2π
opt
Assuming the value of 12,52 for Q in our example data, the residual BER is calculated to be
opt
–18
less than 1 × 10 .
4.5.7 BER non-optimum decision threshold
The BER value at decision threshold voltages other than the optimum can be calculated from
the following formula:
2 2
 μ −D  μ −D
 1 0 
   
   
σ σ
  1   0  
− −
2 2
 
1 e e
+
BER(D)=
 
2 μ− D μ − D
1 0
 
  
2π  2π
   
 
σ σ
 1   0 
 
– 18 – 61280-2-8  IEC:2003
4.5.8 Error bound
Using the formula in Annex A (equation A.5), one can derive the error bound on the derived
value of Q-optimum.
For the example shown, the absolute error bound on Q is ±0,5.
4.6 Test documentation
Report the following information for each test.
a) Test date
b) This document number
c) Specimen/sample (that is, optical transmission system being tested) identification
d) Two sets of data; one above the optimal threshold and the other below
e) Each data set should contain at least 5 readings of threshold versus BER (for BER
–5 –10
values varying from 10 to 10 )
f) Report optimal Q as well as the optimal decision threshold
g) Report possible error range in the value of Q
4.7 Specification information
The following details shall be specified.
a) IEC document number
b) Any special test requirements
c) Failure or acceptance criteria
5 Variable optical threshold method
5.1 Overview
This method consists of the optical addition of an interfering pre-set bias light to the received
optical signal in order to increase the measured BER. Measurements taken at several values of
bias light are extrapolated to zero bias, to evaluate the BER value for normal operation. This
method is applicable to d.c.-coupled receivers only. The effect of adding a pre-set bias is
shown in Figure 7.
Figure 7 – Effect of optical bias

61280-2-8  IEC:2003 – 19 –
The method can be used to evaluate the error performance of an optical link or active device
as shown in Figure 1. Alternatively, the error performance of a complete system can be
evaluated using the set-up shown in Figure 2. The advantage of this method is that no internal
access to equipment is required and that any internal error monitoring facility of the system
under test can be utilized. If this is not available, conventional error-measuring equipment can
be connected to the data input and output terminations of the system.
5.2 Apparatus
Common to all methods is the conventional error measurement equipment: a pattern generator
and an error detector.
a) Conventional error measuring test equipment consisting of a pattern generator and
separate error detector suitable for remote use. This is not required for system evaluation
with self-contained error-monitoring facility.
b) A pre-set light source, stable to 0,1 dB over 1 h, of a wavelength similar to the system
under test.
c) An optical attenuator stable to 0,1dB over 1 h. An additional attenuator with equivalent
stability may be required in the case of high signal levels at the receiver, for example, when
testing a transmitter receiver pair.
d) An optical splitter/combiner with split ratios typically between 50:50 and 10:90, and with
fibre compatible with that of the system and the pre-set bias light source.
5.3 Items under test
The item under test may be a digital fibre optic system consisting of a digital transmitter and a
d.c. coupled digital receiver which are connected via an optical link consisting of fibre or cable
and may also include passive or active components. If a transmitter/receiver pair alone is to be
tested, they should be connected via a fixed or variable optical attenuator.
The item under test may also be a self-contained transmission system comprising transmit and
receive terminals connected via an optical link which itself may contain active devices such as
regenerators and/or optical amplifiers. Such system may include internal error monitoring
facilities.
5.4 Procedure for basic optical link
Refer to Figure 8.
a) Operate the transmitter and receiver, adjusting the received signal with the optical
attenuator. It may be necessary to monitor the input power of the optical signal at the
receiver.
–4
b) Adjust the pre-set bias light until a predetermined high value of BER, such as 10
, is
reached.
c) Decrease the bias a step at a time, and at each step reco
...