Research on the Evolution and Key Technologies of Mobile Communication Network Architecture in the Future

2021 ◽  
Author(s):  
Li Pengcheng
2014 ◽  
Vol 687-691 ◽  
pp. 2205-2209
Author(s):  
Xiu Rong Li ◽  
Shuang Zheng ◽  
Ya Li Liu

The Internet of things is the technology extension and expansion of computer, Internet and mobile communication network. We will strongly rely on the Internet of Things in the future. The Internet of Things provides more conveniences, but it also will face more threats. In this article, we describe architecture of the Internet of Things, the function of every layer and what components every layer need. Our emphasis is to analyze the threats for the security and privacy at different levels of architecture in the Internet of Things.


2011 ◽  
Vol 403-408 ◽  
pp. 1564-1567
Author(s):  
Xin Min Wu ◽  
Hao Shu Wu

This article first has carried on the brief elaboration to the 4G mobile communication system's network architecture and the key technologies, will face more and more threats facing future 4G mobile communication network security, has analyzed the communication network, the 4G network safety mechanism and 4G the correspondence development faced with the security threat question, next will adopt the mobile communication network security the protection technology, finally proposed in the future must study question and development direction.


Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4390
Author(s):  
Tingli Xiang ◽  
Hongjun Wang

In order to overcome the limitations of traditional road test methods in 5G mobile communication network signal coverage detection, a signal coverage detection algorithm based on distributed sensor network for 5G mobile communication network is proposed. First, the received signal strength of the communication base station is collected and pre-processed by randomly deploying distributed sensor nodes. Then, the neural network objective function is modified by using the variogram function, and the initial weight coefficient of the neural network is optimized by using the improved particle swarm optimization algorithm. Next, the trained network model is used to interpolate the perceptual blind zone. Finally, the sensor node sampling data and the interpolation estimation result are combined to generate an effective coverage of the 5G mobile communication network signal. Simulation results indicate that the proposed algorithm can detect the real situation of 5G mobile communication network signal coverage better than other algorithms, and has certain feasibility and application prospects.


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