TY - GEN
T1 - Pilot assignment and channel estimation via deep neural network
AU - Lee, Seunghwan
AU - Ju, Hyungyu
AU - Shim, Byonghyo
N1 - Funding Information:
This work was sponsored by the National Research Foundation of Korea (NRF) grant funded by the Korean government(MSIP) (2016R1A2B3015576).
Publisher Copyright:
© 2018 IEEE.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - In orthogonal frequency division multiplexing (OFDM) systems, channel estimation is by far the most important operation in the receiver to ensure the accurate detection and decoding. Over the years, pilot-aided channel estimation has been widely used for this purpose. In open-loop systems, since there is no feedback link between the transmitter and receiver, an approach based on the equi-spaced pilot assignment has been widely employed. In this paper, we propose a closed-loop non-uniform pilot allocation strategy based on deep neural network (DNN) technique. From the numerical evaluations, we show that the proposed autoencoder-based pilot allocation technique outperforms conventional approaches by a large margin, demonstrating its ability to learn the statistical characteristics of the wireless channel.
AB - In orthogonal frequency division multiplexing (OFDM) systems, channel estimation is by far the most important operation in the receiver to ensure the accurate detection and decoding. Over the years, pilot-aided channel estimation has been widely used for this purpose. In open-loop systems, since there is no feedback link between the transmitter and receiver, an approach based on the equi-spaced pilot assignment has been widely employed. In this paper, we propose a closed-loop non-uniform pilot allocation strategy based on deep neural network (DNN) technique. From the numerical evaluations, we show that the proposed autoencoder-based pilot allocation technique outperforms conventional approaches by a large margin, demonstrating its ability to learn the statistical characteristics of the wireless channel.
UR - http://www.scopus.com/inward/record.url?scp=85062872413&partnerID=8YFLogxK
U2 - 10.1109/APCC.2018.8633453
DO - 10.1109/APCC.2018.8633453
M3 - Conference contribution
AN - SCOPUS:85062872413
T3 - 2018 24th Asia-Pacific Conference on Communications, APCC 2018
SP - 454
EP - 458
BT - 2018 24th Asia-Pacific Conference on Communications, APCC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th Asia-Pacific Conference on Communications, APCC 2018
Y2 - 12 November 2018 through 14 November 2018
ER -