학술논문

Deep Learning-Based Joint Pilot Design and Channel Estimation for OFDM Systems
Document Type
Periodical
Author
Source
IEEE Transactions on Communications IEEE Trans. Commun. Communications, IEEE Transactions on. 71(8):4577-4590 Aug, 2023
Subject
Communication, Networking and Broadcast Technologies
OFDM
Channel estimation
Symbols
Resource management
Deep learning
Receivers
Interference
Autoencoder
channel estimation
deep learning
pilot design
Language
ISSN
0090-6778
1558-0857
Abstract
We propose a non-uniform joint pilot design and channel estimation (JPDCE) scheme in closed-loop orthogonal frequency-division multiplexing (OFDM) systems. Specifically, the encoder of the proposed JPDCE scheme is composed of two sub-networks which are used for pilot location assignment and pilot power allocation, respectively. To ensure accurate learning of the pilots, the OFDM block consisting of intelligently customized layers is designed to constrain the output of the encoder. Furthermore, the decoder is used to learn the channel state information (CSI) based on the output of OFDM layers by minimizing the mean square error (MSE) of the channel estimation. The numerical results indicate that the JPDCE scheme considerably outperforms the traditional methods as well as two state-of-the-art deep learning (DL)-based ones, demonstrating its excellent ability to learn the statistical characteristics of the wireless channel. In addition, we demonstrate that the JPDCE scheme shows excellent robustness against various channel distortion and interference: when 1) there are limited pilots; 2) the cyclic prefix (CP) is removed; and/or 3) clipping noise is introduced.