Abstract
For directional modulation (DM) based on antenna arrays, the associated weight coefficients are normally calculated by some traditional optimisation methods. In this paper, a radial basis function (RBF) based neural network is proposed for fast directional modulation design, which is trained by data corresponding to sets of transmission direction and interference direction angles with the corresponding weight coefficients. The proposed method takes advantage of the strong nonlinear function approximation capability of the RBF neural network, and simulation results are provided to show the effectiveness of the proposed design.
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Acknowledgments
The work was partially supported by the National Natural Science Foundation of China (62101383) and Science and technology project of Hedong District (TJHD-JBGS-2022-08).
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Zhou, M. et al. (2023). Radial Basis Function Neural Network Based Fast Directional Modulation Design. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2022. Lecture Notes in Electrical Engineering, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-99-1260-5_4
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DOI: https://doi.org/10.1007/978-981-99-1260-5_4
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