Deep Learning based Intelligent Recognition Method in Heterogeneous Communication Networks
Hao Gu, Yu Wang, Sheng Hong, Yongjun Xu, Guan Gui · 2020
Friendly signal coexistence problem over unlicensed bands has been received strongly attention in design next-generation wireless communication systems. Typically, it is very challenge to recognize wireless fidelity (WiFi) signal and long-term evolution (LTE) signal over the unlicensed bands (LTE-U) in heterogeneous communication networks. The main reason is that LTE-U may occupy the spectrum resources of WiFi. Hence it is necessary to solve this problem and then to lay the foundation for the friendly coexistence of LTE-U and WiFi technology. In this paper, we proposed a deep learning based intelligent recognition method for identifying LTE-U and WiFi signals in heterogeneous communication networks. First, we collect LTE-U and WiFi signal samples and introduce random phase offset and two data forms to them. Second, we use deep learning algorithms to train these samples to get the best preprocessing method and neural network algorithm parameters. Finally, experiments are conducted to show that our proposed method can efficiently recognize LTE and WiFi signals with excellent recognition accuracy and robustness.