ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Improved Resource Allocation in 5G Using Deep Learning


Affiliations
1 Department of Information Technology, Knowledge Institute of Technology, India
2 Department of Computer Science and Engineering, Presidency University, India
3 Department of Computer Science and Business Systems, Knowledge Institute of Technology, India
     

   Subscribe/Renew Journal


All wireless equipment, including terminals, base stations, and phones, functions as a component of a single interconnected system, it is subject to attacks from a wide variety of different forms of cyber threats. This elucidates the relevance of considering the aforementioned attacks in order to prevent them from finally acquiring control of the complete system environment. The term cybersecurity refers to the set of preventative measures that are implemented to safeguard an information technology infrastructure against being corrupted or destroyed. The results of a performance evaluation that was carried out on the DRL at several different node densities. When there is a requirement to analyze a network in terms of its throughput, delivery ratio, and latency, NS2 is the tool that is utilized. In this article, a comparison and contrast between the Lagrange Duality Method technique is presented.

Keywords

Wireless Equipment, Cybersecurity, Resource Allocation, Deep Learning
Subscription Login to verify subscription
User
Notifications
Font Size

  • D. Basu and R. Datta, “SoftDrone: Softwarized 5G Assisted Drone Networks for Dynamic Resource Sharing using Machine Learning Techniques”, Computers and Electrical Engineering, Vol. 101, pp. 107962-107975, 2022.

  • Qian Zhu, “Substrate-Integrated-Waveguide-Fed Array Antenna Covering 57-71GHz Band for 5G Applications”, IEEE Transactions on Antennas and Propagation, Vol. 65, No. 12, pp. 6298-6306, 2017.

  • I. Ahmad, W. Tan and H. Sun, “Latest Performance Improvement Strategies and Techniques Used in 5G Antenna Designing Technology, a Comprehensive Study”, Micromachines, Vol. 13, pp. 717-736, 2022

  • L. Yang, S. Shangguan and S. Li, “Location InformationAssisted Robust Beamforming Design for Ultra-Wideband Communication Systems”, Symmetry, Vol. 14, No. 6, pp. 1171-1178, 2022.

  • C.A. Balanis, “Antenna Theory: Analysis and Design”, John Wiley and Sons, 2016

  • M.S. Almutairi, “Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction”, Mathematical Problems in Engineering, Vol. 2022, pp. 1-9, 2022.

  • A. Adekunle, K.E. Ibe, M.E. Kpanaki, C.O. Nwafor, N. Essang and I.I. Umanah, “Evaluating the Effects of Radiation from Cell Towers and High-Tension Power Lines on Inhabitants of Buildings in Ota”, Journal for Sustainable Development, Vol. 3, No. 1, pp. 1-21, 2015.

  • Y. Fu and X. Wang, “Traffic Prediction-Enabled Energy-Efficient Dynamic Computing Resource Allocation in CRAN Based on Deep Learning”, IEEE Open Journal of the Communications Society, Vol. 3, pp. 159-175, 2022.

  • Yi. Huang and K. Boyle, “Antennas from Theory to Practice”, John Wiley and Sons, 2008.

  • L. Zhong and B. Ren, “Slice Allocation of 5G Network for Smart Grid with Deep Reinforcement Learning ACKTR”, Proceedings of International Conference on Intelligent Computing and Signal Processing, pp. 242-249, 2022.

  • M. Rajalakshmi, V. Saravanan and C. Karthik, “Machine Learning for Modeling and Control of Industrial Clarifier Process”, Intelligent Automation and Soft Computing, Vol. 32, No. 1, pp. 339-359, 2022.

  • H. Zhou and H.V. Poor, “Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing”, IEEE Transactions on Cognitive Communications and Networking, Vol. 8, No. 4, pp. 1925-1941, 2022.


Abstract Views: 198

PDF Views: 1




  • Improved Resource Allocation in 5G Using Deep Learning

Abstract Views: 198  |  PDF Views: 1

Authors

P. Sachidhanandam
Department of Information Technology, Knowledge Institute of Technology, India
V. Amirtha Preeya
Department of Computer Science and Engineering, Presidency University, India
B. H. Impa
Department of Computer Science and Engineering, Presidency University, India
M. Ranjith Kumar
Department of Computer Science and Business Systems, Knowledge Institute of Technology, India

Abstract


All wireless equipment, including terminals, base stations, and phones, functions as a component of a single interconnected system, it is subject to attacks from a wide variety of different forms of cyber threats. This elucidates the relevance of considering the aforementioned attacks in order to prevent them from finally acquiring control of the complete system environment. The term cybersecurity refers to the set of preventative measures that are implemented to safeguard an information technology infrastructure against being corrupted or destroyed. The results of a performance evaluation that was carried out on the DRL at several different node densities. When there is a requirement to analyze a network in terms of its throughput, delivery ratio, and latency, NS2 is the tool that is utilized. In this article, a comparison and contrast between the Lagrange Duality Method technique is presented.

Keywords


Wireless Equipment, Cybersecurity, Resource Allocation, Deep Learning

References