An adaptive limiting method in LEO satellite multi-beam system based on neural network

doi:10.7523/j.ucas.2022.012

Abstract

Abstract: Aiming at the problem that the peak-to-average ratio is too high when the LEO satellite multi-beam system communicates with multiple target directional angles, a method for suppressing the peak-to-average ratio based on a deep neural network is proposed. This method can adaptively select the optimal threshold of the limiting method based on the input layer parameters such as the target direction angle position of the multi-beam communication system, the SNR of the receiving end, and the error range of the BER. The signal synthesized on the element is subjected to amplitude limiting operation, which reduces the peak-to-average ratio of the LEO satellite multi-beam system while ensuring the BER of the system. Finally, it is verified by simulation that this method can significantly improve the peak-to-average ratio within the error range of the BER compared with the traditional fixed-threshold limiting method.

Key words: LEO satellite multi-beam system, PAPR, neural network, limiting method

CLC Number:

TN927

LIU Zijian, JIANG Quanjiang, LIU Huijie. An adaptive limiting method in LEO satellite multi-beam system based on neural network[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(5): 670-676.

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