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

• URL: http://arxiv.org/abs/2304.12189v1
• Date: Sat, 8 Apr 2023 19:30:23 GMT
• Title: Machine Learning-based Methods for Joint {Detection-Channel Estimation} in OFDM Systems
• Authors: Wilson de Souza Junior, Taufik Abrao
• Abstract summary: Two machine learning (ML)-based structures for joint detection-channel estimation in OFDM systems are proposed and extensively characterized. The bit-error-rate (BER) performance vs. computational complexity trade-off is analyzed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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