Open AccessOverestimation problem with ANN and VSTF in optical communication systems
Author(s) -
Ikuta K.,
Otsuka Y.,
Fukumoto Y.,
Nakamura M.
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.2008
Subject(s) - equaliser , pseudorandom binary sequence , volterra series , transfer function , artificial neural network , binary number , feed forward , sequence (biology) , computer science , backpropagation , series (stratigraphy) , algorithm , control theory (sociology) , feedforward neural network , mathematics , artificial intelligence , nonlinear system , engineering , control engineering , arithmetic , physics , paleontology , decoding methods , control (management) , quantum mechanics , biology , electrical engineering , genetics
The authors investigated the problem of overestimation with the Volterra series transfer function (VSTF) and an artificial neural network (ANN), which are used for non‐linear equalisers in optical communication systems. The results revealed that the risk of predicting a pseudo‐random binary sequence (PRBS) pattern, which causes overestimation of the equaliser performance, occurs not only with an ANN but also with the VSTF. When using PRBS9, PRBS11 and PRBS15, the number of taps of a feedforward tapped delay line, which is required in the VSTF to predict the PRBS pattern, was the same as that with the ANN. When the second‐order Volterra kernels were omitted, a larger number of taps was required in the VSTF to observe the overestimation.