2020 VOL.3 Feb No.1

• Title: Research on the Active Disturbance Rejection of Nodes in Mobile Communication Networks Based on Artificial Intelligence
• Name: Beheshti Shekofteh
• Company: Myongji University
• Abstract:

  With the increasingly complex network environment and the interference of various other radio waves, the quality of mobile communication networks has been seriously affected. Aiming at the above problems, a method of autonomous disturbance rejection for mobile communication network nodes based on artificial intelligence is studied. The method is divided into two parts: the first part uses artificial intelligence to identify and analyze interference factors of mobile communication network nodes, and the second part processes the results of identification analysis to complete the anti-interference work of mobile communication network nodes. Finally, a mobile communication quality simulation experiment is performed to test the performance of the method. The results show that: Compared with the traditional method without artificial intelligence to participate in the identification and analysis of interference factors, users are more satisfied with the quality of mobile communication processed by this method, which proves the performance of this method in terms of anti-interference.

• Keyword: Artificial Intelligence; Mobile Communication Network; Anti-Interference
• DOI: 10.12250/jpciams2020010120
• Citation form: Beheshti Shekofteh.Research on the Active Disturbance Rejection of Nodes in Mobile Communication Networks Based on Artificial Intelligence[J]. Computer Informatization and Mechanical System, 2020, vol. 3, pp. 100-106.

[1] Wu Dongmin, Xie Tao. Mobile Communication Network Relay Node Deployment Optimization Method Research [J].Computer simulation, 2017, 34 (3): 301-304. 

[2] Huang Jiwen, Huang Xufang. Research on topological location method of optimal communication node in mobile communication network [J]. Modern Electronics Technique, 2017, 40 (16): 55-57+60.

[3] Zhu Wei, Chen Huihui, Tian Siyuan, et al. Artificial Intelligence：New Blue Ocean from a Scientific Dream---Analysis and Countermeasures of the Development Situation of AI Industry [J]. Science & Technology Progress and Policy, 2016, 33 (21): 66-70. 

[4] Cao Hang, Zhang Yunlong. The Signal Processing Methods of Optimization Research of High-Speed Mobile Communication [J]. Computer Simulation, 2016, 33 (8): 177-180. 

[5] Wang Haichao, Wang Yue, Liu Jiequn, et al. Research on Inter-Cell Interference Management Strategy Based on Frequency and Time [J]. Mobile Communications, 2016 (1): 54-58.

[6] Bao Tianyue.Research on Anti-jamming positioning method for mobile wireless sensor networks[J].Digital technology and application, 2017(3): 88-89.

[7] Lou Li, Fan Jianhua, Xu Cheng. Research on Topologically Optimized Anti-jamming Technology for Tactical MANETs [J]. Acta Armamentarii, 2016, 37 (9): 1662-1669.

[8] Chen Changqing, Wei Bo. RResearch on Method of Removing Noise in Network Optical Fiber Communication [J]. Computer Simulation, 2016, 33 (10): 166-169. 

[9] Liu Jun. Introduction of Mobile Communications Interference Test in Guangzhou Metro[J]. Urban Mass Transit, 2017, 20 (8): 168-170. 

[10] Chen Ying, Hu Yong. Interference Suppression Algorithm Based on Signal Blind Source Separation[J]. Science Technology and Engineering, 2016, 16 (21): 255-260.