Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Conference on Lasers and Electro-Optics/Europe (CLEO/Europe 2023) and European Quantum Electronics Conference (EQEC 2023)
  • Technical Digest Series (Optica Publishing Group, 2023),
  • paper ci_p_1

Input Ordinal Invariant Neural Network based Eigenvalue Demodulator for On-Off Encoded 4096-ary Multi-Eigenvalue Signal

Not Accessible

Your library or personal account may give you access

Abstract

Eigenvalue-modulated signals have invariant carriers even after propagating through the nonlinear fiber channel. The eigenvalues are extracted based on inverse scattering transform (IST) [1]. IST makes a matrix and it is solved by using the QZ method. Then, the QZ outputs the eigenvalues in order of maximum absolute value in the almost case. In the demodulation of the on-off encoded eigenvalue signal, we have proposed employing neural network (NN) on the bit decision [2]. The NN is sensitive to the input order while the higher optical signal-to-noise ratio (OSNR) gain is obtained than the common hard decision. This paper proposes employing deep sets layer on neural eigenvalue demodulation. This paper reports the experimental result of the 50-km fiber transmission.

© 2023 IEEE

PDF Article
More Like This
Optical Eigenvalue Demodulation Using Neural Network and Estimation of the Number of Eigenvalues in the Preset Region

Kohei Nishida, Yuhei Terashi, Daisuke Hisano, Ken Mishina, and Akihiro Maruta
ci_6_3 The European Conference on Lasers and Electro-Optics (CLEO/Europe) 2023

Eigenvalue-domain Neural Network Receiver for 4096-ary Eigenvalue-modulated Signal

Hiroyuki Takeuchi, Ken Mishina, Yuhei Terashi, Daisuke Hisano, Yuki Yoshida, and Akihiro Maruta
Th3F.3 Optical Fiber Communication Conference (OFC) 2022

Joint Multi-Eigenvalue Demodulation Using Complex Moment-based Eigenvalue Solver and Artificial Neural Network

Yuhei Terashi, Daisuke Hisano, Ken Mishina, Yuki Yoshida, and Akihiro Maruta
W6A.13 Optical Fiber Communication Conference (OFC) 2021

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.