# Extreme Learning Machine Based Non-Iterative and Iterative Nonlinearity Mitigation for LED Communications

## TL;DR

This paper introduces ELM-based non-iterative and iterative receiver designs for LED communications to effectively mitigate LED nonlinearity and memory effects, significantly outperforming conventional methods and reducing training overhead.

## Contribution

It proposes novel ELM-based receiver architectures, including data-aided iterative design, for improved nonlinearity mitigation in LED communication systems.

## Key findings

- ELM-based receivers outperform polynomial-based ones.
- Iterative receivers achieve substantial performance gains.
- Data-aided approach reduces training overhead.

## Abstract

This work concerns receiver design for light emitting diode (LED) communications where the LED nonlinearity can severely degrade the performance of communications. We propose extreme learning machine (ELM) based non-iterative receivers and iterative receivers to effectively handle the LED nonlinearity and memory effects. For the iterative receiver design, we also develop a data-aided receiver, where data is used as virtual training sequence in ELM training. It is shown that the ELM based receivers significantly outperform conventional polynomial based receivers; iterative receivers can achieve huge performance gain compared to non-iterative receivers; and the data-aided receiver can reduce training overhead considerably. This work can also be extended to radio frequency communications, e.g., to deal with the nonlinearity of power amplifiers.