Open AccessSignal Sorting Algorithm of Hybrid Frequency Hopping Network Station Based on Neural Network
Author(s) -
Zhongyong Wang,
Bei-bei Zhang,
Zhengyu Zhu,
Ke Gong
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012091
Subject(s) - sorting , frequency hopping spread spectrum , cluster analysis , computer science , algorithm , artificial neural network , convergence (economics) , signal (programming language) , sorting algorithm , process (computing) , hybrid algorithm (constraint satisfaction) , artificial intelligence , telecommunications , economics , programming language , economic growth , operating system , constraint logic programming , constraint satisfaction , probabilistic logic
In a non-cooperative frequency hopping communication system, the frequency hopping network station sorting of the received hybrid signals plays an important role and becomes an active research area in recent years. In order to solve the problem that the currently widely used clustering algorithm can not achieve satisfactory accuracy. In this paper, we propose a signal sorting method for hybrid frequency hopping network stations by applying the neural network to classify the frequency hopping description words of signals. Additionally, the conjugate gradient algorithm is utilized in the neural network training process to improve the convergence speed. Simulation results demonstrate that when compared with the clustering algorithm, the proposed algorithm converges with fewer iterations and delivers better sorting accuracy, especially in a low signal to noise ratio environment.