Classification of Channel Encoders Using Convolutional Neural Network

In digital communication systems, channel encoders play a crucial role in rectifying random errors introduced by the channel. Typically, information about the type and parameters of channel encoders used at the transmitting end is available at the receiver. However, in non-cooperative scenarios like...

Full description

Saved in:
Bibliographic Details
Published in2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) pp. 590 - 593
Main Authors Ahamed, Nayim, B, Naveen, R, Swaminathan
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.01.2024
Subjects
AWGN channels
channel encoder classification
Convolutional neural network(CNN)
Convolutional neural networks
Deep learning
Digital communication
Military communication
Neural networks
non-cooperative scenarios
Receivers
Online AccessGet full text

Cover

More Information
Summary:In digital communication systems, channel encoders play a crucial role in rectifying random errors introduced by the channel. Typically, information about the type and parameters of channel encoders used at the transmitting end is available at the receiver. However, in non-cooperative scenarios like military communication systems, encoder types and parameters may be only partially known or entirely unknown. This paper explores the feasibility of employing a deep learning approach to classify four different types of encoders: block, convolutional, Bose-Chaudhuri-Hocquenghem (BCH), and polar encoders. Utilizing a convolutional neural network (CNN) model for classification, our proposed approach achieves classification accuracy exceeding 95% upto bit-error-rate (BER) value of 0.03. The results also indicate that the accuracy improves with the input sample length.
ISSN:2155-2509
DOI:10.1109/COMSNETS59351.2024.10427098