Reinforcement Learning Neural Networks for Optical Communications.

Abstract

The objective of this work is to utilize neural networks to find new methods for optimizing high performance fiber-optic communication links. In typical broadband analog optical communication links, the dominant distortion comes from the transmitter. The electrical-to-optical transfer characteristics of both electro-optic external modulators and semiconductor lasers are nonlinear and create both odd and even-order harmonic distortions of the modulating signal. One cost-effective method to cancel device non-linearities in direct modulated lasers is by electronic predistortion. For our previous work, based on the simulated annealing learning algorithm utilized for neural network learning, a novel algorithm was developed to obtain the initial parameters of predistortion and laser circuits, and it has been used to linearize the Distributed FeedBack (DFB) semiconductor laser transmitters.

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Document Details

Document Type
Technical Report
Publication Date
Apr 02, 1995
Accession Number
ADA299796

Entities

People

  • Michael Salour

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Algorithms
  • Distortion
  • Distributed Feedback Lasers
  • Fiber-Optic Communications
  • Lasers
  • Learning
  • Neural Networks
  • Optical Communications
  • Reinforcement Learning
  • Semiconductor Lasers
  • Semiconductors
  • Transmitters

Readers

  • Electronics Engineering
  • Neural Network Machine Learning.
  • Optical Physics and Photonics.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Directed Energy
  • Microelectronics