Affiliations: School of Network and Communication, Nanjing College of Information Technology, Nanjing, Jiangsu 210023, China | E-mail: wangjuan@njcit.cn
Correspondence:
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Corresponding author: School of Network and Communication, Nanjing College of Information Technology, Nanjing, Jiangsu 210023, China. E-mail: wangjuan@njcit.cn.
Abstract: When the wireless communication network is interfered, the communication effect will be affected. In order to improve the interference signal processing effect and the identification accuracy of the interference signal, a wireless communication network interference suppression algorithm based on joint estimation is proposed. Using the deep learning method to identify the interference signal, obtain the effective interference signal of wireless communication network, improve the accuracy of interference signal identification, and track and parameter modulation the identified signal; The node model of wireless communication network is established, and the joint estimation method is used to suppress the interference signal for the nodes in the model. The interference suppression of wireless communication network is realized through the state estimation of single tone interference and narrowband interference. The experimental results show that the proposed algorithm has a high accuracy of interference signal recognition, the highest value reaches 98%, and the wireless communication data packet loss rate is low, the highest value is only 0.37, which verifies its interference suppression effect.
Keywords: Joint estimation, wireless communication network, deep learning, parameter modulation, signal tracking
