TY - JOUR
T1 - Compressive Sampled CSI Feedback Method Based on Deep Learning for FDD Massive MIMO Systems
AU - Wang, Jie
AU - Gui, Guan
AU - Ohtsuki, Tomoaki
AU - Adebisi, Bamidele
AU - Gacanin, Haris
AU - Sari, Hikmet
N1 - Funding Information:
Manuscript received January 24, 2021; revised April 18, 2021; accepted May 30, 2021. Date of publication June 4, 2021; date of current version September 16, 2021. This work was supported in part by the JSPS KAKENHI under Grant JP19H02142, Major Project of the Ministry of Industry and Information Technology of China under Grant TC190A3WZ-2, National Natural Science Foundation of China under Grant 61901228, the Summit of the Six Top Talents Program of Jiangsu under Grant XYDXX-010, the Program for High-Level Entrepreneurial and Innovative Team under Grant CZ002SC19001, the project of the Key Laboratory of Universal Wireless Communications (BUPT) of Ministry of Education of China under Grant KFKT-2020106. The associate editor coordinating the review of this article and approving it for publication was F. Gao. (Corresponding authors: Guan Gui; Hikmet Sari.) Jie Wang, Guan Gui, and Hikmet Sari are with the College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China (e-mail: 2018010223@njupt.edu.cn; guiguan@njupt.edu.cn; hikmet@njupt.edu.cn).
Publisher Copyright:
© 1972-2012 IEEE.
PY - 2021/9
Y1 - 2021/9
N2 - Accurate downlink channel state information (CSI) is required to be fed back to the base station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems in order to achieve maximum antenna diversity and multiplexing. However, downlink CSI feedback overhead scales with the number of transceiver antennas, a major hurdle for practical deployment of FDD massive MIMO systems. To solve this problem, we propose a compressive sampled CSI feedback method based on deep learning (SampleDL). In SampleDL, the massive MIMO channel matrix is sampled uniformly in time/frequency dimension before being fed into neural networks (NNs), which will reduce the computational resource/time at user equipment (UE) as well as enhance the CSI recovery accuracy at the BS. Both theoretical analysis and normalized mean square errors (NMSE) results confirm the advantages of the proposed method in terms of time complexity and recovery accuracy. Besides, a suitable CSI feedback period is explored by link level simulations, which aims to further reduce the overhead of CSI feedback without degrading the communication quality.
AB - Accurate downlink channel state information (CSI) is required to be fed back to the base station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems in order to achieve maximum antenna diversity and multiplexing. However, downlink CSI feedback overhead scales with the number of transceiver antennas, a major hurdle for practical deployment of FDD massive MIMO systems. To solve this problem, we propose a compressive sampled CSI feedback method based on deep learning (SampleDL). In SampleDL, the massive MIMO channel matrix is sampled uniformly in time/frequency dimension before being fed into neural networks (NNs), which will reduce the computational resource/time at user equipment (UE) as well as enhance the CSI recovery accuracy at the BS. Both theoretical analysis and normalized mean square errors (NMSE) results confirm the advantages of the proposed method in terms of time complexity and recovery accuracy. Besides, a suitable CSI feedback period is explored by link level simulations, which aims to further reduce the overhead of CSI feedback without degrading the communication quality.
KW - Channel state information
KW - deep learning
KW - feedback
KW - frequency division duplexing
KW - massive MIMO
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U2 - 10.1109/TCOMM.2021.3086525
DO - 10.1109/TCOMM.2021.3086525
M3 - Article
AN - SCOPUS:85107388050
VL - 69
SP - 5873
EP - 5885
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
SN - 1558-0857
IS - 9
ER -