Machine Learning-based Methods for Joint {Detection-Channel Estimation} in OFDM Systems

Wilson de Souza Junior; Taufik Abrao

doi:10.1002/itl2.404

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arXiv · 2304.12189

Machine Learning-based Methods for Joint {Detection-Channel Estimation} in OFDM Systems

Wilson de Souza Junior·Taufik Abrao

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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.

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BibTeX RIS

Wilson de Souza Junior, Taufik Abrao. 2023-04-08. Machine Learning-based Methods for Joint {Detection-Channel Estimation} in OFDM Systems. https://doi.org/10.1002/itl2.404

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