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

Autor:	Junior, Wilson de Souza, Abrao, Taufik
Rok vydání:	2023
Předmět:	Computer Science - Information Theory Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control Statistics - Applications
Zdroj:	Internet Technology Letters. 2023;e404
Druh dokumentu:	Working Paper
DOI:	10.1002/itl2.404
Popis:	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. Comment: 13 pages, 8 figures, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.12189 Zobrazit plný text záznamu View this record from Arxiv