eyeContour

Contour of eye opening from eye diagram

Since R2024a

Description

Use the eye contour object to store data related to a set of contours at the specified symbol error rate (SER). The eye contours are generated from an eye diagram.

Creation

Syntax

c = eyeContour(eyeObj)

c = eyeContour(eyeObj,Name=Value)

Description

c = eyeContour(eyeObj) creates an eye contour object from the eye diagram object eyeObj using default values.

c = eyeContour(eyeObj,Name=Value) sets the Properties (Mixed-Signal Blockset) of the eye contour object to the specified values.

For example, c = eyeContour(eyeObj,SER=1e-3,Extrapolation='None') creates an eye contour object from the eye diagram object eyeObj at the symbol error rate 1e-3.

Input Arguments

eyeObj — Eye diagram object

eye diagram object

Eye diagram object used to create the eye contour object. The eye diagram object is created as an accumulation of 2-D histograms from the eye diagram data.

Properties

Contour — Numeric contour data for each eye opening

[] (default) | 3-D array

Numeric contour data consisting of upper and lower contours for each eye opening, specified as a 3-D array. Contours are row vectors with one element for each of the TimeBins properties from the original eyeDiagramSI object. The object indexes the contours from the lowest eye center amplitude to the highest.

By default, the property is automatically set from the value defined in the eyeObj eye diagram object.

Example

Contour(1, :, 3) represents the lower contour of the third eye.

Contour(2, :, 1) represents the upper contour of the first eye.

Data Types: double

OuterContour — Numeric contour data of outer contours

[] (default) | vector

Numeric contour data consisting of two outer (below and above) contours for the whole eye diagram, specified as a vector.

OuterContour(1, :) represents the lower limit of the eye. Contour(2, :) represents the upper limit of the eye.

By default, the property is automatically set from the value defined in the eyeObj eye diagram object.

Data Types: double

SER — Symbol error rate to generate contour

0 | scalar

Symbol error rate to generate contours, specified as a scalar.

Data Types: double

Extrapolation — Extrapolation method to generate contour

None (default) | Dual-Dirac

Extrapolation method to generate contours, specified as None or Dual-Dirac. For more information, see Extrapolation (Mixed-Signal Blockset).

Data Types: char

SampleInterval — Time between adjacent samples in input data

1 (default) | scalar

Elapsed time between adjacent samples in the input data, specified as a scalar in seconds.

By default, the property is automatically set from the value defined in the eyeObj eye diagram object.

Data Types: double

SymbolTime — Time span of one symbol

8 (default) | scalar

Time span of one symbol, specified as a scalar in seconds.

By default, the property is automatically set from the value defined in the eyeObj eye diagram object.

Data Types: double

SymbolThresholds — Nominal center amplitude for eye opening

vector

Nominal center amplitude for each eye opening in the eye diagram, specified as a vector.

By default, the property is automatically set from the value defined in the eyeObj eye diagram object.

Data Types: double

Object Functions

eyeHeight (Mixed-Signal Blockset)	Measure vertical eye opening
eyeWidth (Mixed-Signal Blockset)	Measure horizontal eye opening
eyeArea (Mixed-Signal Blockset)	Measure eye area
upperContour (Mixed-Signal Blockset)	Measure upper contour of eye diagram
lowerContour (Mixed-Signal Blockset)	Measure lower contour of eye diagram
closedContour (Mixed-Signal Blockset)	Measure closed contour of eye diagram

More About

Extrapolation

Extrapolation method is used to constitute the relation between specific SER values and the shape of the 2-D histogram. It is the core technology behind the generation of bathtub plots and eye contours.

During extrapolation method, the data is pre-processed, usually one symbol at a time on only a 1D slice of the said symbol.

The None extrapolation method is a previous neighbor interpolation of the cumulative sum an eye slice. For horizontal eye slices, the extrapolation uses the timing origins. For vertical eye slices, the extrapolation uses the symbol thresholds.

When moving outward from the center of the eye, it is a previous neighbor interpolation. When moving across the eye from one side to the other, it appears as a next neighbor interpolation that switches to a previous neighbor interpolation as you pass the center of the eye. This way, the result for a symbol error rate is a conservative estimate from the perspective of the eye opening, based on the data.

The Dual-Dirac extrapolation algorithm first fits the Dual-Dirac PDF to a column of the split histogram. Then it uses those coefficients to calculate the inverse Dual-Dirac CDF for the specified SER(s). It is only applicable to systems with an exponential impulse response whose time constant is on the order of the time for one symbol, or less. The algorithm is also significantly slower that the None extrapolation method.

Version History

Introduced in R2024a