Machine Learning-based Methods for Joint {Detection-Channel Estimation} in OFDM Systems
This work addresses signal processing challenges in wireless communications, offering incremental improvements for OFDM systems.
The paper tackles joint detection-channel estimation in OFDM systems by proposing DNN and ELM architectures, showing they outperform conventional methods like MF with MMSE/LS in BER performance versus computational complexity trade-offs.
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.