Affiliation:
1. Beijing Institute of Technology
2. Yangtze Delta Region Academy of Beijing Institute of Technology
Abstract
In this work, a low-complexity data-driven
characterized-long-short-term-memory (C-LSTM)-aided channel modeling
technique is proposed for optical single-mode fiber (SMF)
communications. To fully utilize the sequence correlation learning
ability of traditional long short-term memory (LSTM) networks and
solve the gradient explosion problem, the feature information is
introduced into the traditional LSTM input layer to better
characterize the intersymbol interference caused by dispersion in SMF
modeling. The simulation results show that the proposed C-LSTM can
effectively alleviate the gradient explosion problem with a stable and
ultimately lower mean square error (MSE) than traditional LSTM.
Compared with the split-step Fourier method (SSFM) and the conditional
generative adversarial network (CGAN), the proposed C-LSTM has
superior computational complexity. Moreover, due to the sequence
correlation learning ability inherent to C-LSTM, coupled with the
flexibility of feature information selection, the proposed
C-LSTM-aided modeling technique has a higher modeling accuracy than
traditional LSTM. Moreover, the C-LSTM-aided modeling technique can be
effectively extended to other channel modeling applications with
strong sequence correlations.
Funder
National Key R&D Program of China
from the Ministry of Science and Technology
National Natural Science Foundation of
China
Funds for Creative Research Groups of
China
Subject
Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering