16 Annex B informative Error in measuring noise level due to signal spectral width and wavelength filtering .... • Annex B has been added to further explain error in measuring noise leve
General
An optical spectrum analyzer (OSA) is essential for measuring the signal and noise powers needed for accurate analysis There are three prevalent methods to implement an OSA: utilizing a diffraction grating, employing a Michelson interferometer, and other techniques.
Diffraction grating-based OSA
A simplified diagram of a diffraction grating-based Optical Spectrum Analyzer (OSA) illustrates the process where expanded input light strikes a rotatable diffraction grating The diffracted light exits at an angle that correlates with its wavelength and subsequently passes through an aperture to reach a photodetector The dimensions of both the input and output apertures, along with the beam size on the diffraction grating, influence the spectral width of the resulting filter, thereby affecting the OSA's resolution Finally, A/D conversion and digital processing yield the familiar display associated with OSAs.
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Figure 3 – Diffraction grating-based OSA
Michelson interferometer-based OSA
A variant of Optical Spectrum Analysis (OSA) utilizes the Michelson interferometer, where the input signal is divided into two distinct paths: one with a fixed length and the other with a variable length.
The Michelson interferometer generates an interference pattern by combining a signal with a delayed version of itself at the photodetector This interference pattern, known as an interferogram, represents the autocorrelation of the input signal By applying a Fourier transform to the autocorrelation, the optical spectrum can be obtained The resolution of this optical spectrum analyzer (OSA) is determined by the differential path delay of the interferometer.
Figure 4 – Michelson interferometer-based OSA
Fabry-Perot-based OSA
A third type of Optical Spectrum Analyzer (OSA) utilizes a Fabry-Perot etalon, as illustrated in Figure 5 In this setup, a collimated beam traverses the Fabry-Perot etalon, which is designed with a free spectral range (FSR) that exceeds the channel plan, while the finesse is selected to achieve the desired resolution bandwidth.
(RBW) Piezo-electric actuators control the Fabry-Perot mirror spacing and provide spectral tuning Digital signal processing provides any combination of spectral display or tabular data
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Figure 5 – Fabry-Perot-based OSA
OSA performance requirements
General
Refer to IEC 62129 for calibration details.
Wavelength range
The wavelength range must adequately encompass the channel plan, including an additional half grid spacing at both ends of the band, to effectively assess the noise levels of the lowest and highest channels.
Sensitivity
The sensitivity of an Optical Spectrum Analyzer (OSA) refers to the minimum spectral power level that can be accurately measured It is crucial for the OSA's sensitivity to be adequate to detect the lowest anticipated noise levels This is particularly important when considering Optical Signal-to-Noise Ratio (OSNR).
Required sensitivity (dBm) = Minimum channel level (dBm) – OSNR (dB) (3)
For example, the sensitivity required for a minimum channel level of –10 dBm in order to measure a 35-dB OSNR is
Resolution bandwidth (RBW)
The connection between the measured peak power and the total signal power is influenced by the signal's spectral characteristics and the resolution bandwidth To accurately assess the power level of each modulated channel, the resolution bandwidth must be adequately wide.
The appropriate resolution bandwidth (RBW) setting is influenced by the bit rate of the signal For instance, a laser modulated at an OC-192 (STM-64) rate with zero chirp exhibits a signal power that is 0.8 dB lower when measured with a 0.1-nm RBW compared to a wider RBW This reduction occurs because part of the modulation envelope's spectral power falls outside the 0.1-nm RBW If the RBW is further reduced to 0.05 nm, the signal power decreases by 2.5 dB The impact of this effect is exacerbated by laser chirp and mitigated by additional bandwidth limiting in the modulation circuitry of the transmitter laser, with further details provided in Annex A.
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When signals spread spectrally between channels due to high modulation rates, it is crucial to have a sufficiently narrow resolution to exclude signal power from noise measurements, ensuring accurate results at the given noise level For instance, with OC-192 (STM-64) signals spaced 0.2 nm apart on a 25 GHz grid, a 0.1-nm resolution bandwidth (RBW) would include 17% of the signal power in the noise measurement, leading to a maximum measurable optical signal-to-noise ratio (OSNR) of only about 7 dB Further details on this topic can be found in Annex B.
Resolution bandwidth accuracy
The accuracy of the noise measurement is directly impacted by the accuracy of the OSA’s
RBW For best accuracy, the OSA’s noise equivalent bandwidth, B m , must be calibrated
RBW differs from B M primarily because the optical spectrum analyzer's filter characteristic is non-rectangular The calibration procedure for B M is outlined in IEC 61290-3-1, where it is specifically termed optical bandwidth.
Dynamic range
The dynamic range of an Optical Spectrum Analyzer (OSA) indicates its capability to measure low-level signals and noise that are near large signals in wavelength It's crucial to understand that reducing the Resolution Bandwidth (RBW) does not automatically lead to an improved dynamic range RBW represents the 3-dB bandwidth or noise equivalent bandwidth of the OSA's filter characteristics.
Dynamic range is a key metric that indicates the steepness of a filter's characteristic and the OSA noise floor It is defined as the ratio, measured in decibels (dB), between the filter's transmission characteristic at the center wavelength, \$\lambda_i\$, and at a distance of one-half a grid spacing, \$\lambda_{I \pm \Delta\lambda}\$.
Figure 6 shows two channels of a multichannel spectrum, the OSA filter characteristic, the
The sensitivity limit of the Optical Signal Analyzer (OSA) and the noise within the transmission system are critical factors to consider For precise measurements at the designated noise measurement wavelength, the dynamic range needs to exceed the Optical Signal-to-Noise Ratio (OSNR) significantly The uncertainty in these measurements can be estimated using a specific equation.
The uncertainty in Optical Signal-to-Noise Ratio (OSNR) can be calculated using the formula \$\text{Uncertainty in OSNR} = 10 \log(1 + 10^{-D/10})\$ dB, where \$D\$ represents the difference in dB between the OSA dynamic range and the actual OSNR For instance, with an OSNR of 30 dB and a dynamic range of 40 dB at the ITU grid spacing, the resulting error would be 0.42 dB.
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OSA dynamic range at 1/2 a grid spacing
Figure 6 – Illustration of insufficient dynamic range as another source of measurement uncertainty
The measurement of Optical Signal-to-Noise Ratio (OSNR) is generally constrained by either the sensitivity limit of the Optical Spectrum Analyzer (OSA) or its dynamic range Typically, a Michelson interferometer-based OSA is restricted by its sensitivity limit, while a diffraction grating-based OSA is limited by its dynamic range.
Scale fidelity
Scale fidelity, also called display linearity, is the relative error in amplitude that occurs over a range of input power levels Scale fidelity directly contributes to the OSNR measurement uncertainty.
Polarization dependence
Typically, the signal, P i will be highly polarized while the noise, N i is unpolarized OSA polarization dependence will directly contribute to uncertainty in signal measurement.
Wavelength data points
The minimum number of data points collected by the OSA shall be at least twice the wavelength span divided by the noise equivalent bandwidth
The device under test (DUT) is a multichannel fibre-optic transmission system The measurement apparatus can connect to the network either directly through the optical fibre or via a broadband monitoring port However, measurement points located after wavelength-selective components, like an add-drop multiplexer, may not be suitable due to the noise filtering effect outlined in Annex B.
To accurately measure signal power and noise levels, connect the Optical Spectrum Analyzer (OSA) to the transmission fiber or a monitor port, and select Resolution Bandwidth (RBW) values that are wide enough to ensure precise measurements while providing sufficient dynamic range to assess noise at ±Δλ from the peak channel wavelength.
Licensed to MECON Limited for internal use in Ranchi and Bangalore, this document outlines the procedure for measuring optical signal-to-noise ratio (OSNR) It specifies that Δλ should be half the ITU grid spacing or less for improved accuracy in OSNR due to noise filtering Additionally, the wavelength range must include all channels, with at least half a grid spacing below the lowest channel and above the highest channel Finally, the power level at the signal peak for each channel should be measured, represented as \( P_i + N_i \).
To measure the noise at ±Δλ from the signal peak wavelength, utilize a calibrated resolution bandwidth (RBW) with a noise equivalent bandwidth, B m The noise values obtained are N(λ i -Δλ) and N(λ i +Δλ) Subsequently, calculate the interpolated noise value for each channel wavelength using Equation (2).
N (5) g) Calculate P i by subtracting N i from the value obtained in step d) h) Repeat steps d) through g) for all n channels
This procedure can be performed using two different RBW settings: one that is wide enough to capture the total signal power, and another that offers sufficient dynamic range to measure noise at ± Δ λ from the peak channel wavelengths.
• For each of the n channels, calculate the interpolated value of noise power, N i , using step f) and P i using step g) in Clause 6
• For each of the n channels, calculate OSNR from Equation (1) r m i
Measurement uncertainty should be calculated based upon the ISO/IEC Guide to the expression of uncertainty in measurement [5]
Uncertainty contributions that must be considered are as follows:
• modulated signal power (4.5.4 and Annex A);
Report the following information for each test:
• identification of the transmission system being tested and the test location
• description of the equipment used
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• offset wavelength for noise measurement, Δλ, and ITU grid spacing
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Error in measuring signal level due to signal spectral width
The spectral width of each channel is broadened from that of the CW laser due to several causes:
• intensity modulation for signal transmission
• modulation to suppress stimulated Brillouin scattering (SBS)
In dense WDM systems utilizing external modulation, laser chirp is not a significant concern The broadening effects from SBS suppression and SPM are generally minor when compared to the broadening caused by signal modulation at rates of 2.5 Gb/s and above.
Figures A.1 and A.2 show the calculated spectra of an intensity modulated laser for 10 Gb/s and 2,5 Gb/s line rates respectively The modulation is an NRZ PRBS with a word length of
2 7 -1 Optical and electrical filtering values are indicated in Table A.1 For reference, a typical
OSA filter characteristic for a 0,1-nm RBW is also shown
The Optical Spectrum Analyzer (OSA) fails to capture a portion of the signal power, leading to inaccuracies in the measured signal power Figures A.3 and A.4 illustrate the extent of this error for a 10 Gb/s signal.
Table A.1 – Filtering used in simulation to determine signal power level error
Electrical filter bandwidth 30 GHz 7,5 GHz Optical filter bandwidth 0,64 nm 0,36 nm
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Power spectrum of 10 Gb/s modulated signal
Figure A.1 – Power spectrum of a 10 Gb/s, 2 7 −
1 PRBS signal showing the considerable amount of power not captured in a 0,1 nm RBW with 0,64 nm filtering after the signal
Power spectrum of 2,5 Gb/s modulated signal
1 PRBS with 0,36 nm filtering with considerably less power outside the 0,1 nm OSA RBW
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Figure A.3 – Signal power error versus RBW for a 10 Gb/s modulated signal
Figure A.4 – Signal power error versus RBW for a 2,5 Gb/s modulated signal
To minimize the error in the signal power measurement, a resolution bandwidth of sufficient width should be chosen Table A.2 shows the RBW values that cause less than 0,1 dB error
Table A.2 – RBW to achieve less than 0,1 dB error in signal power
Modulation rate 10 Gb/s 2,5 Gb/s or lower
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Error in measuring noise level due to signal spectral width and wavelength filtering
The spectral width of a signal can impact the accuracy of noise level measurements When substantial signal power exists at the midpoint between channels, the Optical Spectrum Analyzer (OSA) struggles to differentiate this from noise power levels This challenge is particularly pronounced when higher modulation rates, such as 40 Gb/s, are combined with closely spaced channels.
In a 100 GHz grid, noise measurement requires a precision of 0.4 nm, with signal strength comparable to that at 0.1 nm from a 10 GHz signal, as outlined in section 4.5.4 Optical filtering from multiplexing can diminish this noise, akin to the signal depicted in Figure A.1 The effectiveness of this filtering is crucial in determining whether the Optical Signal-to-Noise Ratio (OSNR) method can accurately measure these signals within the required uncertainty limits.
A second influence of advanced optical networks is the effect of using optical add-drop multiplexers (OADM) and other components that have strong wavelength dependence
The use of reconfigurable optical add-drop multiplexers (ROADMs) allows for the separation and recombination of channels transmitted over different spans However, the demultiplexing and remultiplexing processes often lead to a reduction in power levels between channels, complicating the estimation of noise levels at the signal wavelength Additionally, adjacent channels from different spans may contribute varying levels of optical signal-to-noise ratio (OSNR), making it challenging to accurately measure noise power for interpolation between channels.
Figure B.1 illustrates four amplified channels spaced 200 GHz apart, processed through a multiplexer with a specific combined loss curve The output spectrum demonstrates effective noise filtering between channels, making it impossible to interpolate the noise level at the signal wavelength In this scenario, the unmodulated signals are narrower than the passbands, revealing noise plateaus that remain largely unfiltered when sufficient resolution is applied This allows for OSNR measurements by adjusting the interpolation offset to assess noise on these plateaus However, when narrower channels are utilized alongside high modulation rates, accurate OSNR values become unmeasurable.
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Mux loss Noise Modified signal
Figure B.1 – Example for noise filtering between channels for a 200 GHz grid
The effects outlined in this annex may render the standard method unsuitable, regardless of the instrumentation employed In such cases, more advanced techniques, such as polarization extinction, could be explored for measuring OSNR However, if the time and equipment needed to assess the OSNR across multiple channels are extensive, the benefits of utilizing spectral OSNR evaluation for channel characterization become evident.
RIN and eye diagrams may be reduced
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[1] ITU-T Recommendation G.692 (1998), Optical interfaces for multichannel systems with optical amplifiers
[2] ITU-T Recommendation G.697 (2004), Optical monitoring for DWDM systems
[3] ITU-T Supplement 39 to G-series Recommendations (2006): Optical system design and engineering considerations
[4] ITU-T Recommendation G.694.1, Spectral grids for WDM applications: DWDM frequency Grid
[5] ISO/IEC MISC UNCERT: 1995, Guide to the expression of uncertainty in measurement
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4.2 OSA réalisé à partir d'un réseau de diffraction 29
4.3 OSA réalisé à partir d'un interféromètre Michelson 30
4.4 OSA réalisé à partir du Fabry-Perot 30
4.5.4 Largeur de bande de résolution (RBW) 31
4.5.5 Précision de la largeur de bande de résolution 32
4.5.9 Points de données de longueurs d'onde 33
Annexe A (informative) Erreur de mesure du niveau de signal du fait de la largeur spectrale du signal 36
Annexe B (informative) Erreur de mesure du niveau de bruit du fait de la largeur spectrale du signal et du filtrage de la longueur d’onde 40
Figure 1 – Spectre optique type au niveau d'une interface optique dans un système de transmission multivoie 28
Figure 2 – OSNR de chaque voie , dérivé des mesures directes du spectre optique 29
Figure 3 – OSA réalisé à partir d'un réseau de diffraction 30
Figure 4 – OSA réalisé à partir d'un interféromètre Michelson 30
Figure 5 – OSA réalisé à partir d'un Fabry-Perot 31
Figure 6 – Illustration de l'insuffisance de la plage dynamique comme autre source d'incertitude de mesure 33
Figure A.1 – Spectre de puissance d'un signal PRBS de 10 Gb/s, 2 7 − 1 montrant une puissance considérable non capturée dans une RWB de 0,1 nm avec un filtrage de
Figure A.2 – Spectre d'un PRBS de 2,5 Gb/s 2 7 − 1 avec un filtrage de 0,36 nm avec beaucoup moins de puissance à l'extérieur de la RBW de l'OSA de 0,1 nm 37
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Figure A.3 – Erreur de puissance du signal par rapport à la RBW pour un signal modulé de 10 Gb/s 38
Figure A.4 – Erreur de puissance de signal par rapport à la RBW pour un signal modulé de 2,5 Gb/s 38
Figure B.1 – Exemple de filtrage du bruit entre des voies pour une grille de 200 GHz 41
Tableau A.1 – Filtrage utilisé en simulation pour déterminer l'erreur du niveau de puissance du signal 36
Tableau A.2 – RBW afin d'obtenir une erreur de moins de 0,1 dB sur la puissance du signal 39
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PROCÉDURES D'ESSAI DES SOUS-SYSTÈMES DE TÉLÉCOMMUNICATIONS À FIBRES OPTIQUES –
Partie 2-9: Systèmes numériques – Mesure du rapport signal sur bruit optique pour les systèmes multiplexés à répartition en longueur d'onde dense
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