In this letter, we study the channel state information (CSI) feedback based on the deep autoencoder (AE) considering the feedback errors and feedback delay in the frequency division duplex massive multiple-input multiple-output system. We construct the deep AE by modeling the CSI feedback process, which involves feedback transmission errors and delays. The deep AE is trained by setting the delayed version of the downlink channel as the desired output. The proposed scheme reduces the impact of the feedback errors and feedback delay. Simulation results demonstrate that the proposed scheme achieves better performance than other comparable schemes.
Bibliographical noteFunding Information:
Manuscript received December 19, 2018; revised January 16, 2019; accepted January 16, 2019. Date of publication January 24, 2019; date of current version June 19, 2019. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), in part by the Institute for Information and Communications Technology Promotion (IITP) funded by the Korea Government [Ministry of Science and Information and Communications Technology (MSIT), Development on the Core Technologies of Transmission, Modulation and Coding With Low-Power and Low-Complexity for Massive Connectivity in the IoT Environment] under Grant 2016-0-00181, in part by the National Research Foundation of Korea (NRF) funded by the Korea Government (MSIT) under Grant 2017R1A2B3006025, and in part by the International Research and Development Program of the NRF funded by the MSIT under Grant 2017K1A3A1A12030076. The associate editor coordinating the review of this paper and approving it for publication was C.-K. Wen. (Corresponding author: Sooyong Choi.) Y. Jang, G. Kong, M. Jung, and S. Choi are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: email@example.com).
© 2012 IEEE.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering