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Signal Detection in Satellite-Ground IoT Link Based on Blind Neural Network

机译:基于盲神经网络的卫星接地IOT链路信号检测

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At present, there are many problems in satellite-ground IoT link signal detection. Due to the complex characteristics of the satellite-ground IoT link, including Doppler and multipath effect, especially in scenarios related to military fields, it is difficult to use traditional method and traditional cooperative communication methods for link signal detection. Therefore, this paper proposes an efficient detection of satellite-ground IoT link based on the blind neural network (BNN). The BNN includes two network structures, the data feature network and the error update network. Through multiple iterations of the error update network, the weight of BNN for blind detection is optimized and the optimal elimination solution is obtained. Through establishing a satellite-to-ground link model simulation of the low-orbit satellite, the proposed BNN algorithm can obtain better bit error rate characteristics.
机译:目前,卫星地面IOT链路信号检测存在许多问题。 由于卫星地面链路的复杂特性,包括多普勒和多路径效应,特别是在与军事领域相关的情景中,很难使用传统方法和传统的协作通信方法来链接信号检测。 因此,本文提出了基于盲神经网络(BNN)的卫星接地物联网链路的有效检测。 BNN包括两个网络结构,数据特征网络和错误更新网络。 通过对错误更新网络的多次迭代,优化了用于盲检测的BNN的权重,获得最佳消除解决方案。 通过建立低轨道卫星的卫星地面链路模型模拟,所提出的BNN算法可以获得更好的误码率特性。

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