# Extreme Learning Machine-Based Receiver for MIMO LED Communications

## TL;DR

This paper introduces an extreme learning machine-based receiver for MIMO LED communications that effectively manages LED nonlinearity and interference while reducing computational complexity using FFT.

## Contribution

The paper proposes a novel ELM-based receiver with circulant input weights for MIMO LED systems, improving handling of nonlinearity and interference with lower complexity.

## Key findings

- Efficiently handles LED nonlinearity and interference
- Reduces receiver complexity using FFT and circulant weights
- Demonstrates improved performance in MIMO LED communication scenarios

## Abstract

This work concerns receiver design for light-emitting diode (LED) multiple input multiple output (MIMO) communications where the LED nonlinearity can severely degrade the performance of communications. In this paper, we propose an extreme learning machine (ELM) based receiver to jointly handle the LED nonlinearity and cross-LED interference, and a circulant input weight matrix is employed, which significantly reduces the complexity of the receiver with the fast Fourier transform (FFT). It is demonstrated that the proposed receiver can efficiently handle the LED nonlinearity and cross-LED interference.