[WP-B] Poster Session 2

[WP-B-11] Activation-Function-Dependent Characteristics of Complex-Valued Reservoir-Computing-Based Nonlinear Equalizer for Fiber-Optic Nonlinearity Compensation

○Takumi Yamamoto¹, Kai Ikuta¹, Yuta Ito¹, Tsuyoshi Yamada¹, Shunya Uchide¹, Soya Shimomura¹, Moriya Nakamura¹ (1.Meiji University)

Keywords:

Artificial intelligence and machine learning for optical transmission systems and subsystems

We investigated the performance of a CVRC-based nonlinear equalizer designed to compensate for fiber-optic nonlinearity when varying the activation function. The performance tended to deteriorate particularly when using an activation function with discontinuous slope changes.