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optPulseMetric

Pulse response metric for optimization routines

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Syntax

metric = optPulseMetric(P,N,dt,B)

Description

example

metric= optPulseMetric(P,N,dt,B)calculates the eye height, eye width, eye area, and channel operating margin (COM) metrics at the target bit error rate (BER) levelB. The function calculates these metrics from the pulse response matrixP, using the number of samples per symbolN, and the uniform sampling intervaldt.

If there is no eye at the target BER levelB, then theoptPulseMetricfunction increases the BER until a positive height is realized.

The resolution of theoptPulseMetricfunction is limited by number of samples per symbolN.

The nonreturn to zero (NRZ) eye metrics are estimated at a BER level, where both the center and maximum eye heights are reported. If the eye is not open at the target BER, the BER is increased until a measurable eye contour is found. The actual BER used is reported by the valueusedBERreported in the outputmetric.

Examples

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Open Live Script

Define the system exploration parameters. Set the target bit error rate (BER) to 1e-9, symbol time to 100 ps, number of samples per symbol to 16, and channel loss to 4 dB.

targetBER = 1e-9; SymbolTime = 100e-12; N = 16; dBLoss = 4;

Calculate the sample interval.

dt = SymbolTime/N;

创建通道模型the specified loss.

channelModel = serdes.ChannelLoss('Loss',dBLoss,'dt',dt,...'TargetFrequency',1/SymbolTime/2);

Define a CTLE System object™.

ctle = serdes.CTLE('WaveType','Impulse',...'SampleInterval',dt,'SymbolTime',SymbolTime,'Mode'2);

Process the channel impulse response with the CTLE System object.

impulse = ctle(channelModel.impulse);

Convert the impulse response to a pulse response.

P = impulse2pulse(impulse,N,dt);

Calculate the fast optimization pulse metric.

metric = optPulseMetric(P,N,dt,targetBER);

Obtain the result of theoptPulseMetricfunction throughout the whole eye.

In order to do that, first find the number of complete unit intervals in the pulse response.

努伊=地板(大小(P,1)/N); prMatrixSort = sort(abs(reshape(P(1:N*nUI),N,[])).',1,'descend');

Then convert the BER into the number of largest pulse response samples to get theusedBERcontour. The mean height is the first row (largest value) of the sorted pulse response. The noise is the sum of sorted pulse response's nextnBERsample intervals.

nBER = floor(min(abs(log(targetBER)/log(2)),nUI-1)); meanEyeHeight = prMatrixSort(1,:); Noise =sum (prMatrixSort(2:nBER+1,:),1);

Calculate the statistical eye and the eye metrics.

[stateye,vh,th] = pulse2stateye(P,N,2); th2 = th*SymbolTime*1e12; [~,~,contours,~,EHmax,~,~,~,~,EW,~,~,~,~,eyeAreas,~,COM] =...serdes.utilities.calculatePAMnEye(2,targetBER,...th2(1),th2(end),vh(1),vh(end),stateye);

Plot the statistical eye and the results of theoptPulseMetricfunction.

figure, subplot(2,1,1) holdallimagesc(th2,vh,stateye) plot(th2,contours,'m:') title('Statistical Eye and Contour') axis('xy'); si_eyecmap = serdes.utilities.SignalIntegrityColorMap; colormap(si_eyecmap) xlabel('[ps]') ylabel('[V]') ax = axis; subplot(2,1,2) th4 = [(0:N-1),NaN,(0:N-1)]*dt*1e12; plot(th4,[meanEyeHeight,NaN,-meanEyeHeight]/2,'r--',...th4,[meanEyeHeight-Noise,NaN,Noise-meanEyeHeight]/2,'m:') legend('Mean eye level','BER eye contour') title('Fast Optimization Pulse Metric') ylabel('[V]') xlabel('[ps]') axis(ax)

Figure contains 2 axes objects. Axes object 1 with title Statistical Eye and Contour contains 3 objects of type image, line. Axes object 2 with title Fast Optimization Pulse Metric contains 2 objects of type line. These objects represent Mean eye level, BER eye contour.

Compare the results of the statistical eye metrics and the fast optimization pulse metrics.

comparisonTable = table([metric.maxEyeHeight*1e3;metric.eyeWidth*1e12;...metric.eyeArea*1e12;metric.centerCOM; metric.usedBER],...[EHmax*1e3; EW;eyeAreas; COM; targetBER],...'VariableNames',{'optPulseMetric','StatisticalEye'},...'RowNames',{'Max Eye Height (mV)','Eye Width (ps)','Eye Area [V*ps]',...'Center COM','BER'}); disp(comparisonTable)
optPulseMetric StatisticalEye ______________ ______________ Max Eye Height (mV) 379.36 367.69 Eye Width (ps) 93.75 79.347 Eye Area [V*ps] 17.902 17.847 Center COM 11.597 10.853 BER 1e-09 1e-09

Input Arguments

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Pulse response, specified as a column vector. The pulse response vector can be calculated from the channel impulse response vector.

Data Types:double

Number of samples per symbol, specified as a real positive scalar.

Data Types:double

Uniform sampling interval of the waveform, specified as a real positive scalar in seconds.

Data Types:double

Target bit error rate (BER), specified as a real positive scalar.

Data Types:double

Output Arguments

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Pulse response metrics, returned as a structure with these fields:

Field Description
maxEyeHeight Maximum height of the inner eye in theusedBERcontour curve.
maxMeanEyeHeight Mean eye level height at the maximum inner eye height position.
maxCOM Channel operating margin (COM) at the maximum inner eye height position.
eyeArea Area inside theusedBERcontour.
eyeWidth Eye width of theusedBERcontour.
centerEyeHeight Eye height at the center of theusedBERcontour.
centerMeanEyeHeight Mean eye level height at the center of the eye.
centerCOM Channel operating margin (COM) at the center of the eye.
usedBER The bit error rate (BER) level used in the metric calculation that gives the nonzero eye height.

Data Types:struct

More About

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Channel Operating Margin

The channel operating margin (COM) is a ratio between the signal and the noise[1]and is given by the equation:

COM = 20 log 10 Signal Noise

optPulseMetricestimates the signal amplitude from the pulse response cursor voltage (the mean eye level height)[2]. The noise amplitude is estimated at a given BER by the sum of intersymbol interference (ISI) voltages.

References

[1] IEEE 802.3bj 93 A.

[2] Common Electrical I/O (CEI) Implementation Agreement OIF-CEI-04.0, section 16.3.10.2.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Version History

Introduced in R2020a