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Signal Detection in Satellite-Ground IoT Link Based on Blind Neural Network
Author(s) -
Qingyang Guan,
Shuang Wu
Publication year - 2021
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2021/5547989
Subject(s) - computer science , satellite , link (geometry) , artificial neural network , signal (programming language) , real time computing , telecommunications , computer network , remote sensing , artificial intelligence , engineering , programming language , aerospace engineering , geology
At present, there are many problems in satellite-ground IoT link signal detection. Due to the complex characteristics of the satelliteground 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.

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