scholarly journals Explore the development direction of mobile communication network optimization

2020 ◽  
Vol 1684 ◽  
pp. 012092
Author(s):  
Yuan Feng
Keyword(s):  
Communication Network ◽  
Mobile Communication ◽  
Network Optimization ◽  
Mobile Communication Network ◽  
Development Direction

4G Mobile Communication Network System Security Faced with Threat Question Research

2011 ◽  
Vol 403-408 ◽  
pp. 1564-1567
Author(s):  
Xin Min Wu ◽  
Hao Shu Wu
Keyword(s):  
Communication Network ◽  
Network Security ◽  
Mobile Communication ◽  
Network Architecture ◽  
Security Threat ◽  
Mobile Communication Network ◽  
Safety Mechanism ◽  
Development Direction ◽  
Protection Technology ◽  
4G Mobile Communication

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.

scholarly journals Mobile Communication Network Optimization System Based on Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Chao Duan ◽  
Jun Luo
Keyword(s):  
Artificial Intelligence ◽  
Communication Network ◽  
Communication Networks ◽  
Mobile Communication ◽  
Network Optimization ◽  
Specific Content ◽  
Optimization System ◽  
Mobile Communication Network ◽  
Artificial Intelligence Technology ◽  
Intelligent Technology

The objective of this paper is to study the key technology of the mobile communication network optimization system based on artificial intelligence technology. Specific Content. This paper designs the artificial intelligence agent- (IA-) type mobile communication network optimization tool iOS2CMCN, analyzes the relevant intelligent technology introduced into the system, and analyzes the feasibility and practicability of iOS2CMCN through application examples. The results show that the optimization of mobile communication networks is one of the important links in the construction of communication networks and ensuring the quality of network service. In the form of an artificial intelligence agent, iOS2CMCN absorbs the experience and knowledge of a large number of network optimization engineers and experts and realizes the intelligence and automation of mobile communication network optimization. It is proven that the fuzzy technology is introduced in theory, the practical problem is reasonably modeled, and the rule and case reasoning are used to simulate the thinking mode of a human being when solving the problem. It reduces the dependence of network optimization on humans, improves the efficiency of network optimization, and provides a new idea for practical mobile communication network optimization.

scholarly journals Research on Distributed 5G Signal Coverage Detection Algorithm Based on PSO-BP-Kriging

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4390
Author(s):  
Tingli Xiang ◽  
Hongjun Wang
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Mobile Communication ◽  
Detection Algorithm ◽  
Base Station ◽  
Distributed Sensor ◽  
Mobile Communication Network ◽  
The Neural Network ◽  
Signal Coverage ◽  
5G Mobile Communication

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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