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Link to original content: https://doi.org/10.1007/s11432-023-3822-9
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Robust channel estimation based on the maximum entropy principle

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Abstract

Channel estimation (CE) is one of the crucial and fundamental elements of signal processing, especially considering the requirement of high accuracy in future wireless communication systems. Most traditional CE algorithms are explored under the assumption of Gaussian white noise, which limits the algorithms performance in real wireless communication situations. In this work, a novel self-adaptive CE algorithm based on the maximum entropy principle (MEP) was studied, which analyzes the statistical components of an arbitrary noise environment. In addition, an MEP channel-based signal estimation algorithm was studied. Furthermore, the statistical characteristics of channels were considered the regularization terms in the objective function for providing prior information and further increasing the accuracy. It was found that the proposed algorithm not only provides accurate CE but also reduces pilot consumption by using estimated signal data as pseudo pilots. The superior features of the proposed method concerning CE accuracy, pilot consumption, and robustness were confirmed through Monte Carlo simulations.

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Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2020YFA0713900), Macao Science and Technology Development Fund (Grant No. 061/2020/A2), National Natural Science Foundation of China (Grant Nos. 61721002, 12226004, 62076196), and in part by Major Key Project of Peng Cheng Laboratory (Grant No. PCL2023AS1-2).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049, China

    Zhengyang Hu, Jiang Xue, Feng Li, Qian Zhao, Deyu Meng & Zongben Xu

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  1. Zhengyang Hu
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  2. Jiang Xue
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  3. Feng Li
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  4. Qian Zhao
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  5. Deyu Meng
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  6. Zongben Xu
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Correspondence to Jiang Xue.

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Hu, Z., Xue, J., Li, F. et al. Robust channel estimation based on the maximum entropy principle. Sci. China Inf. Sci. 66, 222304 (2023). https://doi.org/10.1007/s11432-023-3822-9

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  • DOI: https://doi.org/10.1007/s11432-023-3822-9

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