Machine Learning-based Methods for Joint {Detection-Channel Estimation} in OFDM Systems
Information Theory
2023-04-25 v1 Machine Learning
Systems and Control
Systems and Control
math.IT
Applications
Abstract
In this work, two machine learning (ML)-based structures for joint detection-channel estimation in OFDM systems are proposed and extensively characterized. Both ML architectures, namely Deep Neural Network (DNN) and Extreme Learning Machine (ELM), are developed {to provide improved data detection performance} and compared with the conventional matched filter (MF) detector equipped with the minimum mean square error (MMSE) and least square (LS) channel estimators. The bit-error-rate (BER) performance vs. computational complexity trade-off is analyzed, demonstrating the superiority of the proposed DNN-OFDM and ELM-OFDM detectors methodologies.
Cite
@article{arxiv.2304.12189,
title = {Machine Learning-based Methods for Joint {Detection-Channel Estimation} in OFDM Systems},
author = {Wilson de Souza Junior and Taufik Abrao},
journal= {arXiv preprint arXiv:2304.12189},
year = {2023}
}
Comments
13 pages, 8 figures, 1 table