Orthogonal Time Frequency Space Channel Estimation Based on Model-driven Deep Learning

PU Xumin; LIU Yanxiang; SONG Mixue; CHEN Qianbin

doi:10.11999/JEIT230072

Volume 46 Issue 2

Feb. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(2): 680-687

PU Xumin, LIU Yanxiang, SONG Mixue, CHEN Qianbin. Orthogonal Time Frequency Space Channel Estimation Based on Model-driven Deep Learning[J]. Journal of Electronics & Information Technology, 2024, 46(2): 680-687. doi: 10.11999/JEIT230072

Citation:

PU Xumin, LIU Yanxiang, SONG Mixue, CHEN Qianbin. Orthogonal Time Frequency Space Channel Estimation Based on Model-driven Deep Learning[J]. Journal of Electronics & Information Technology, 2024, 46(2): 680-687. doi: 10.11999/JEIT230072

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Orthogonal Time Frequency Space Channel Estimation Based on Model-driven Deep Learning

doi: 10.11999/JEIT230072 cstr: 32379.14.JEIT230072

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61701062), The China Postdoctoral Science Foundation (2019M651649), The Jiangsu Planned Projects for Postdoctoral Research Funds (2018K041c), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202100649, KJQN202000612)

Received Date: 2023-02-20
Rev Recd Date: 2023-05-15

Available Online: 2023-05-23

Publish Date: 2024-02-29

Abstract

Abstract

In this paper, a channel estimation scheme based on model-driven deep learning algorithm is proposed for Single Input Single Output (SISO) Orthogonal Time Frequency Space (OTFS) modulation systems. First, the Denoising Approximate Message Passing (DAMP) algorithm is considerably expanded. Then the traditional denoiser is replaced by the Denoising Convolutional Neural Network (DnCNN) to estimate the delay-Doppler channel with additive white Gaussian noise. The State Evolution (SE) equation is provided to predict the theoretical Normalized Mean Square Error (NMSE) performance of the Learned Denoising based Approximate Message Passing (LDAMP) algorithm. Simulation results show that the scheme performs well under a low Signal-to-Noise Ratio (SNR) and has great robustness compared with other estimation schemes. When the total number of channel paths is invariant, increasing the number of OTFS two-dimensional grid points can effectively improve channel estimation accuracy.
- Orthogonal Time Frequency Space(OTFS),
- Channel estimation,
- Delay Doppler channel,
- Deep learning

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References(25)

References

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