Anju V. Kulkarni

Work place: Department of Electronics and Telecommunication Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India

E-mail: anju_k64@yahoo.co.in

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Biography

Professor and Head, Department, Dr. Anju V. Kulkarni, Department of Electronics & Telecommunication, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India.

With 30 plus years of academic experience, for the graduate and post graduate programmers of Engineering under University of Pune. She has been a Visiting Faculty for Work Integrated programmed of BITS-Pilani. Anju Kulkarni is registered guide for Ph.D. Programmed under SPPU and external referee for M. E and Ph. D examinations for various universities. She has published 80 plus research papers in the area of Wireless Communication, 5G Networks, Cognitive Radios & SDRs and Pervasive Computing and also has Patents filed to her credit. She is a fellow of IETE and IEEE and member of IEEE Education society.

Author Articles
Energy Efficient Resource Allocation in 5G RAN Slicing with Grey Wolf Optimization

By Dhanashree Kulkarni Mithra Venkatesan Anju V. Kulkarni

DOI: https://doi.org/10.5815/ijcnis.2023.05.07, Pub. Date: 8 Oct. 2023

The massive connections and the real time control applications have different requirement on delay, energy, rate and reliability of the system. In order to meet the diversified 5G requirements, network slicing technique guarantees on the wide scale applications. In this paper, we have proposed a dynamic resource allocation system with two time scale. The one time scale is used for the resource allocation in the system and the other is used for optimized use of latency and power. Lyapunov drift function is used for the balance between the power consumption and the user satisfaction. Further, Grey Wolf Optimization (GWO) is used for the resource allocation strategy so as to gain the reliability of the system with heterogeneous requirements. The proposed methodology shows the improvement of 27% in user satisfaction and 17.5% in power consumption. The proposed framework can be utilized for the rate as well as latency sensitive applications in 5G.

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