Journal ArticleDOI
Deep Learning for Security Problems in 5G Heterogeneous Networks
Zhihan Lv,Amit Singh,Jinhua Li +2 more
TLDR
In this article, a combination of deep learning technology, modulation information recognition, and beam formation is introduced to solve the security problem of the 5G heterogeneous network, which can effectively reduce the computational complexity under different numbers of transmitting antennas, which verifies the superiority of the unsupervised beamforming algorithm based on deep learning proposed in this research.Abstract:
With increasingly complex network structure, requirements for heterogeneous 5G are also growing. The aim of this study is to meet the network security performance under the existing high-capacity and highly reliable transmission. In this context, deep learning technology is adopted to solve the security problem of the 5G heterogeneous network. First, the security problems existing in 5G heterogeneous networks are presented, mainly from two aspects of the physical layer security problems and application prospects of deep learning in communication technology. Then the combination of deep learning and 5G heterogeneous networks is analyzed. The combination of deep learning technology, modulation information recognition, and beam formation is introduced. The application of deep learning in communications technology is analyzed, and the modulation information recognition and beamforming based on deep learning are introduced. Finally, the challenges of solving security problems in 5G heterogeneous networks by deep learning are explored. The results show that the deep learning model can solve the modulation recognition problem well, and the modulation mode of the convolutional neural network can well identify the modulation signals involved in the experiment. Therefore, deep learning has a good advantage in solving modulation recognition. In addition, compared to the traditional algorithm, the unsupervised beamforming algorithm based on deep learning proposed in this research can effectively reduce the computational complexity under different numbers of transmitting antennas, which verifies the superiority of the unsupervised beamforming algorithm based on deep learning proposed in this research. Therefore, the present work provides a good idea for solving the security problem of 5G heterogeneous networks.read more
Citations
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Journal ArticleDOI
Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India
Rabin Chakrabortty,Subodh Chandra Pal,Fatemeh Rezaie,Alireza Arabameri,Saro Lee,Paramita Roy,Asish Saha,Indrajit Chowdhuri,Hossein Moayedi +8 more
TL;DR: In this article, the authors used a remote-sensing and geographic informati cation to map the flood-susceptibility mapping is an important component of flood risk management to control the effects of natural hazards and prevention of injury.
Journal ArticleDOI
5G Security Challenges and Solutions: A Review by OSI Layers
TL;DR: In this paper, the authors provide an objective overview of 5G security issues and the existing and newly proposed technologies designed to secure the 5G environment, categorizing security technologies using Open Systems Interconnection (OSI) layers and discuss vulnerabilities, threats, security solutions, challenges, gaps and open research issues.
Journal ArticleDOI
Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree
Romulus Costache,Alireza Arabameri,Hossein Moayedi,Hossein Moayedi,Quoc Bao Pham,M. Santosh,M. Santosh,Hoang Nguyen,Manish Pandey,Binh Thai Pham +9 more
TL;DR: The Izvorul Dorului river basin from Romania is investigated, to identify slop, which contributes to flash floods in various regions of the world, causing serious damage to life and property.
Journal ArticleDOI
Blockchain-based federated learning methodologies in smart environments.
TL;DR: In this article, the authors proposed a systematic study on the discussion of privacy and security in the field of blockchain-based FL methodologies on the scientific databases to provide an objective road map of the status of this issue.
Journal ArticleDOI
Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption
TL;DR: The results showed that the performance of the A-LSTM model was better than other baseline models, it could provide accurate and reliable hourly forecasting for HVAC energy consumption, and the Tree-structured of Parzen Estimators (TPE) algorithm was introduced.
References
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