A capability of prediction of the eye-diagram width and height with using artificial
neural network (ANN) was investigated. For this purpose, were simulated more than 750
examples of telecommunication channels with different transfer functions. Eye-diagrams
were composed for all examples by means of convolution of random pulse sequence and
pulse response and parameters of these eye-diagrams were measured. Some ANN was
learned. Their input variables were transient characteristic delay time, raise time, magnitude
of voltage peak and oscillation duration as well as a gain value at the half of clock rate. For
each of predicted parameters distinct ANN was chosen for different ranges of input variables.
Root mean square errors of eye-diagram parameters prediction using these ANN were in the range of 2 - 4%. Correlation coefficient of predicted and known values was more then 0,98.
Sufficient decreasing of computational time is achieved compare with estimation of the eye
width and height using eye-diagram modeling. This method can be used for optimization of
communication channel characteristics when eye-diagram parameters are the components of
the goal function.
1. Prokis Dj. Tsyfrovaia svias`. Per. s angl. / Pod red. D.D.Klovskogo. - M.:
Radio i svias`, 2000. – 800 s.
2. ITU-T Recommendation G.957. Optical interfaces for equipments and
systems relating to the synchronous digital hierarchy. 2006.
3. OFSTP-4. Optical Eye Pattern Measurement Procedure, TIA/EIA-526-4,
1993.
4. Diakonov V.P. Sovremennaya apparatura testirovaniia i kontrolia
sverhskorostnyh sistem i linij sviazy // Besprovodnye tehnologii, 2010,
No.4, pp.52-59.
5. Leib H., Pasupathi S. Digital transmission performance of standard analog
filters. // IEEE Transaction on Communications. Vol.40, Iss.1, Jan,1992. P.
42 – 50.
6. Torres-Ferrera P., Ferrero V., Valvo M., Gaudino R. Impact of the overall
electrical filter shaping in next-generation 25G and 50G PON. // URL:
https://arxiv.org/ pdf/1801.08584, 25 Jan. 2018.
7. Haykin S. Neironnye seti: polnyi kurs. 2-e izd. ispr. : Per. s angl. – M.:
OOO "I.D.Vil`iams". 2006. -1104 p.
8. Ambasana N., Anand G, Mutnury B., Gope D. Application of artificial
neural networks for eye-height/width prediction from S-parameters. // 2014
IEEE 23rd Conference on Electrical Performance of Electronic Packaging
and Systems. 26-29 Oct. 2014.
9. Ambasana N., Anand G, Mutnury B., Gope D. Eye height/width prediction
from S-parameters using learning-based models. // IEEE Transactions on
Components, Packaging and Manufacturing Technology. Vol.6, Iss.6, June
2016, P. 873-885.
10. Ambasana N., Anand G, Mutnury B., Gope D. Eye height/width prediction
from S-parameters using learning-based models. // IEEE Transactions on
Components, Packaging and Manufacturing Technology. Vol.6, Iss.6, June
2016, P. 873-885.
11. Lu T., Wu K., Yang Zh., Sun J. High-speed channel modeling with deep
neural networks for signal integrity analysis. // URL:
https://research.google.com/pubs/archive/46433.pdf.
12. Smirnov A.V. Optimal`nye po Pareto approximatsii peredatochnyh
funktsii elektricheskih fil`trov // Aktual`nye problemy gumanitarnyh i
estestvennyh nauk. 2015. №05 (76). Chast` 1, pp.74-78.
13. Smirnov A.V. Metod poiska optimal`nyh drobno-chebyshevskih
approximatsii AChH // Zhurnal radioelektroniki [elektronnyi zhurnal].
2018. No.3. URL: http://jre.cplire.ru /jre/mar18/7/text.pdf.
14. URL: http://www.mathwork.com.
Smirnov alexander vitalievich Smirnov alexander vitalievich Smirnov alexander vitalievich
Candidate of Technical Sciences, Associate Professor
Email: avs_ramb@rambler.ru
Russia technological university
Moscow, Russian Federation
