New Structure of CCR with an AOANN Threshold

In this paper, artificial neural network-based adaptive optimal threshold estimation for a two-dimensional optical code division multiple access conventional correlation receiver is proposed. A multilayer perceptron neural network with back-propagation learning algorithm is considered. This estimato...

Full description

Saved in:
Bibliographic Details
Published in:Journal of optical communications 2021-01, Vol.42 (1), p.103-109
Main Authors: Rabehi, Abdelhalim, Djebbari, Ali, Hafaifa, Ahmed, Souahlia, Abdelkerim, Taleb-Ahmed, Abdelmalik
Format: Article
Language:English
Subjects:
2-D OCDMA
Algorithms
ANN
Artificial neural networks
Back propagation networks
CCR
Code Division Multiple Access
Correlation coefficients
Engineering Sciences
Machine learning
MAI
Mean square errors
MLP
Multilayer perceptrons
MWOOC
Neural networks
optimal threshold
Root-mean-square errors
Signal to noise ratio
SNR
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, artificial neural network-based adaptive optimal threshold estimation for a two-dimensional optical code division multiple access conventional correlation receiver is proposed. A multilayer perceptron neural network with back-propagation learning algorithm is considered. This estimator uses the weight ( ) and the length ( ) of the code word, the number of active users ( ) and the signal to noise ratio as inputs to estimate the required optimal threshold. We have evaluated the proposed approach on a data set of 46,200 samples. We have found that it gives accurate results: 0.029 for the root mean square error, 0.37% for the relative root mean square error and 99.984% for the correlation coefficient (R), which reflects the efficiency of the proposed optimal threshold estimator.
ISSN:0173-4911
1943-0620
2191-6322
1943-0639
DOI:10.1515/joc-2018-0028