CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground Scenario

Cheng GUO; Le YU; Lidong ZHU

doi:10.11959/j.issn.2096-8930.2022030

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CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground Scenario

Special Issue: Intelligent+Satellite Internet | 更新时间：2024-06-03

- CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground Scenario
- Space-Integrated-Ground Information Networks Vol. 3, Issue 3, Pages: 37-45(2022)
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- DOI：10.11959/j.issn.2096-8930.2022030
  CLC： TP393
- Published Online：2022-09，
  
  Published：20 September 2022
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Cheng GUO, Le YU, Lidong ZHU. CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground Scenario[J]. Space-integrated-ground information networks, 2022, 3(3): 37-45.
DOI：

Cheng GUO, Le YU, Lidong ZHU. CNN-Based Channel Estimation Method for OTFS System in Satellite-Ground Scenario[J]. Space-integrated-ground information networks, 2022, 3(3): 37-45. DOI： 10.11959/j.issn.2096-8930.2022030.

摘要

正交时频空技术具有良好的多普勒频侈和时延适应性，在高多普勒通信场景下得到充分应用。针对OTFS系统的信道估计方法存在复杂度高、误码性能较差等不足的问题，基于卷积神经网络的方法，提出星地场景下基于CNN的OTFS系统信道估计方法。仿真结果表明，星地场景下，基于深度学习的方法在算法复杂度以及误码率方面比传统方法更优，从而证明深度学习在OTFS系统进行信道估计方面是可行的。

Abstract

Orthogonal time frequency space (OTFS) is fully applied in high Doppler communication scenarios due to its good Doppler frequency bias and time delay adaptability.The channel estimation methods for OTFS systems have shortcomings such as high complexity and poor BER performance.A CNN-based channel estimation method for OTFS systems in the terrestrial-satellite scenario using a convolutional neural network (CNN) approach was proposed.Simulation results showed that the deep learning-based method outperformed the conventional method in terms of algorithm complexity and BER in the terrestrial-satellite scenario

thus demonstrating that deep learning is a promising tool for channel estimation in OTFS systems.

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references

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