@inproceedings{dd15e7b80cdb474ba493d21dee91dd10,
title = "Enhancing Least Square Channel Estimation Using Deep Learning",
abstract = "Least square (LS) channel estimation employed in various communications systems suffers from performance degradation especially in low signal-to-noise ratio (SNR) regions. This is due to the noise enhancement in the LS estimation process. Minimum mean square error (MMSE) takes into consideration the noise effect and achieves better performance than LS with higher complexity. This paper proposes to correct the LS estimation error using deep learning (DL). Simulation results show that the proposed DL-based schemes perform better than both LS and MMSE channel estimation scheme, with less complexity than accurate MMSE.",
keywords = "Channel estimation, Channel estimation; Deep learning, DNN, DNN, Deep learning, LS, LS, MMSE, MMSE",
author = "Gizzini, {Abdul Karim} and Marwa Chafii and Ahmad Nimr and Gerhard Fettweis",
note = "Funding Information: ACKNOWLEDGMENT Authors acknowledge the Paris Seine Initiative (CY Cergy Paris Universit{\'e}, CNRS, ENSEA, ETIS lab) for the support of the project through the ASIA Chair of Excellence Grant (PIA/ANR-16-IDEX-0008). Publisher Copyright: {\textcopyright} 2020 IEEE.; 91st IEEE Vehicular Technology Conference, VTC Spring 2020 ; Conference date: 25-05-2020 Through 28-05-2020",
year = "2020",
month = may,
doi = "10.1109/VTC2020-Spring48590.2020.9128890",
language = "English (US)",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings",
}