高效的基于数据与模型的信道估计算法

doi:10.11959/j.issn.1000-436x.2022019

Abstract

Abstract:

For orthogonal frequency division multiplexing (OFDM) systems, a hybrid model and data driven channel estimation algorithm was proposed.Combined with two existing channel estimation methods, including a low complex learning-based channel estimation method and the linear minimum mean square error (LMMSE) channel estimation, the estimator with the ability was facilitated to employ online training to improve estimation performance.Meanwhile, the pilot overhead consumed by generating online training data was saved due to the use of the model-based method in the proposed algorithm, which improved the spectrum efficiency.The simulation results demonstrate that the proposed algorithm has better performance under low signal-to-noise ratio (SNR) and better adaptation to practical imperfections compared with conventional channel estimation methods.

Key words: machine learning, hybrid model and data driven, OFDM, channel estimation

CLC Number:

TN929.5

Kai MEI, Haitao ZHAO, Xiaoran LIU, Jun LIU, Jun XIONG, Baoquan REN, Jibo WEI. Efficient model-and-data based channel estimation algorithm[J]. Journal on Communications, 2022, 43(1): 59-70.

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References 22

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参数	取值
传输带宽/MHz	8
OFDM符号周期/μs	125
最大多径时延/μs	8
虚拟子载波数	102
导频符号数	1
子载波间隔/kHz	15.6
CP时间/μs	15.625
FFT点数	512
帧长度	5
数据符号数	4

Efficient model-and-data based channel estimation algorithm

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