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Performance analysis of the nonlinear self-interference cancellation for full-duplex communications

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Abstract

As the impairments of hardware circuits, such as the nonlinearities of power amplifiers (PAs), limit the self-interference suppression performance in full-duplex systems, nonlinear self-interference cancellation (SIC) has attracted much research attention. According to some existing studies, nonlinear SIC in full-duplex systems can be implemented with either nonlinear modeling or radio frequency (RF) signal feedback. However, to the best of our knowledge, there is no theoretical analysis and comparison of the cancellation performance with the two methods. In this paper, the performance of the digital nonlinear SIC with RF signal feedback and nonlinear modeling is analyzed and compared for the first time. The theoretical SIC capabilities of the two methods are derived, and the closed-form solutions are obtained. The factors affecting the performance of the two methods are discussed with the theoretical analysis. Then, by simulations, the theoretical results are verified and the performances of the nonlinear SIC with the two methods are compared in different environments.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61771107, 61701075, 61601064) and National Key R&D Program of China (Grant No. 2018YFB1801903).

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Authors and Affiliations

  1. National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Nanzhou Hu, Shanghui Xiao, Wensheng Pan, Qiang Xu, Shihai Shao & Youxi Tang

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  1. Nanzhou Hu
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  2. Shanghui Xiao
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  3. Wensheng Pan
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  4. Qiang Xu
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  5. Shihai Shao
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  6. Youxi Tang
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Correspondence to Shihai Shao.

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Hu, N., Xiao, S., Pan, W. et al. Performance analysis of the nonlinear self-interference cancellation for full-duplex communications. Sci. China Inf. Sci. 65, 212301 (2022). https://doi.org/10.1007/s11432-021-3303-0

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  • DOI: https://doi.org/10.1007/s11432-021-3303-0

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