Resource Allocation Method of Coastal Wireless Communication Network Based on Machine Learning

2019 ◽  
Vol 97 (sp1) ◽  
pp. 223
Author(s):  
Xudong Liu ◽  
Wenbo Yang ◽  
Yijun Chen
Keyword(s):  
Machine Learning ◽  
Communication Network ◽  
Resource Allocation ◽  
Wireless Communication ◽  
Wireless Communication Network ◽  
Allocation Method

scholarly journals Backhaul-Aware Resource Allocation and Optimum Placement for UAV-Assisted Wireless Communication Network

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1397
Author(s):  
Yishi Xue ◽  
Bo Xu ◽  
Wenchao Xia ◽  
Jun Zhang ◽  
Hongbo Zhu
Keyword(s):  
Communication Network ◽  
Resource Allocation ◽  
Wireless Communication ◽  
Large Scale ◽  
Optimization Problem ◽  
Matrix Theory ◽  
Base Station ◽  
Achievable Rate ◽  
Multiple User ◽  
Wireless Communication Network

Driven by its agile maneuverability and deployment, the unmanned aerial vehicle (UAV) becomes a potential enabler of the terrestrial networks. In this paper, we consider downlink communications in a UAV-assisted wireless communication network, where a multi-antenna UAV assists the ground base station (GBS) to forward signals to multiple user equipments (UEs). The UAV is associated with the GBS through in-band wireless backhaul, which shares the spectrum resource with the access links between UEs and the UAV. The optimization problem is formulated to maximize the downlink ergodic sum-rate by jointly optimizing UAV placement, spectrum resource allocation and transmit power matrix of the UAV. The deterministic equivalents of UE’s achievable rate and backhaul capacity are first derived by utilizing large-dimensional random matrix theory, in which, only the slowly varying large-scale channel state information is required. An approximation problem of the joint optimization problem is then introduced based on the deterministic equivalents. Finally, an algorithm is proposed to obtain the optimal solution of the approximate problem. Simulation results are provided to validate the accuracy of the deterministic equivalents, and the effectiveness of the proposed method.

scholarly journals Wireless communication network security intelligent monitoring system based on machine learning

2021 ◽  
Vol 2083 (3) ◽  
pp. 032045
Author(s):  
Hongkun Liu ◽  
Nianci Wang ◽  
Sirong Liang
Keyword(s):  
Machine Learning ◽  
Communication Network ◽  
Network Security ◽  
Data Collection ◽  
Wireless Communication ◽  
Monitoring System ◽  
Intelligent Monitoring ◽  
Security Vulnerability ◽  
Wireless Communication Network ◽  
Monitoring Accuracy

Abstract Aiming at the problems of traditional wireless communication network security vulnerability monitoring systems such as low monitoring accuracy and time-consuming, a machine learning-based intelligent monitoring system for wireless communication network security vulnerabilities is proposed. In the hardware design of the monitoring system, based on the overall architecture of the wireless communication network and the data characteristics of the wireless communication network, it is divided into a vulnerability data collection module, a vulnerability data scanning module, and a network security vulnerability intelligent monitoring module. In the vulnerability data collection module, the wireless data collector is used to collect vulnerability data in the vulnerability database, and according to the attributes of the vulnerability data, the XSS vulnerability detection plug-in is connected to the vulnerability scanner to scan for wireless communication network vulnerabilities; When the communication network vulnerability data signal is traced, the system session operation of monitoring the vulnerability data. The software part introduces the neural network algorithm in the machine learning intelligent algorithm to process the hidden data in the security vulnerability data. The experimental results show that the wireless communication network security vulnerability intelligent monitoring system based on machine learning can effectively improve the system monitoring accuracy and the efficiency of wireless communication network security vulnerability monitoring.

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