Abstract
In order to improve the correct recognition rate of signals transmitted in satellite communication system, three different structures of artificial neural network (ANN), including feed forward network (FFN), cascade forward network (CFN) and competitive neural network (CNN) are investigated in this paper. Then their performance of correct recognition rate and performance of convergence rate are compared. Results of simulation indicate that typical FFN’s performance dramatically deteriorates in the case of Rician fading, CFN’s performance is similar to the former one while it has higher convergence rate. CNN’s performance of correct recognition rate is the best among these three nets, but in the training process, its performance of convergence rate is not good.
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Acknowledgment
The paper is sponsored by National Natural Science Foundation of China (No. 91538104; No. 91438205) and Open Research fund Program of CETC key laboratory of aerospace information applications (No. EX166290013).
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Zhang, Y., Yang, M., Liu, X. (2018). Artificial-Neural-Network-Based Automatic Modulation Recognition in Satellite Communication. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_39
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DOI: https://doi.org/10.1007/978-3-319-73564-1_39
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