Symbol detection aided channel prediction in fast‐varying massive MIMO systems: Framework and performance analysis

Abstract The channel in massive multiple‐input multiple‐output systems is fast‐varying that the pilot signal needs to be sent frequently. To obtain timely channel state information with less pilot overheads, a symbol detection aided channel prediction scheme is proposed in this paper. Then, the prediction error lower bound of the proposed scheme within one interval of effective prediction is analysed. Besides, the approximate close‐form post‐processing signal to noise ratio is derived for zero‐forcing detector with imperfect channel predictions. Numerical simulations are implemented to verify the validity of theoretical analysis. The results show that the theoretical expressions have a close match with the real simulated performance under various simulation parameter settings. In addition, the frequency of transmitting the pilot signals can be significantly reduced when adopting this proposed method. Moreover, the application of the proposed scheme can be further expanded when combining it with channel coding, thereby greatly improving the spectrum efficiency of the system.

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Publication Details

Journal
IET Communications
Published
2024-10-28
DOI
https://doi.org/10.1049/cmu2.12843
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Symbol detection aided channel prediction in fast‐varying massive MIMO systems: Framework and performance analysis

Wei Gao, Chuan Liu, Wei Peng, Jiayang Xiao
IET Communications
Advanced MIMO Systems Optimization
article

Symbol detection aided channel prediction in fast‐varying massive MIMO systems: Framework and performance analysis

Wei Gao, Chuan Liu, Wei Peng, Jiayang Xiao
article en

Abstract

Abstract The channel in massive multiple‐input multiple‐output systems is fast‐varying that the pilot signal needs to be sent frequently. To obtain timely channel state information with less pilot overheads, a symbol detection aided channel prediction scheme is proposed in this paper. Then, the prediction error lower bound of the proposed scheme within one interval of effective prediction is analysed. Besides, the approximate close‐form post‐processing signal to noise ratio is derived for zero‐forcing detector with imperfect channel predictions. Numerical simulations are implemented to verify the validity of theoretical analysis. The results show that the theoretical expressions have a close match with the real simulated performance under various simulation parameter settings. In addition, the frequency of transmitting the pilot signals can be significantly reduced when adopting this proposed method. Moreover, the application of the proposed scheme can be further expanded when combining it with channel coding, thereby greatly improving the spectrum efficiency of the system.

IET CommunicationsVol. 19(1)
North China Electric Power University (CN), Hainan University (CN), China Electric Power Research Institute, Huazhong University of Science and Technology (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Affordable and clean energy
Openalex Percentile: Top 83%
Advanced MIMO Systems Optimization
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Symbol detection aided channel prediction in fast‐varying massive MIMO systems: Framework and performance analysis — Wei Gao, Chuan Liu, et al. · IET Communications (2024) | TGRS Research Map | TGRS