Nilakshee Rajule

Work place: D. Y. Patil institute of Technology, Pimpri, Pune-411018, India

E-mail: nilakshi.rajule@dypvp.edu.in

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Biography

Nilakshee Rajule, Assistant Professor, Department of Electronics &Telecommunication, Dr. D.Y. Patil Institute of Technology, Pimpri, Pune, SPPU, Maharashtra, India with 10 plus years of academic experience, for the graduate programme of Engineering under University of Pune, Ms. Nilakshee Rajule is currently working as Assistant Professor in Department of E & TC, at Dr. D. Y. Patil Institute of Technology, Pune. She has completed her master’s degree in Communication Networks and published 10 plus research papers in the area of wireless communication, Embedded Systems. She is an associate member of IETE.

Author Articles
Network Traffic Prediction with Reduced Power Consumption towards Green Cellular Networks

By Nilakshee Rajule Mithra Venkatesan Radhika Menon Anju Kulkarni

DOI: https://doi.org/10.5815/ijcnis.2023.06.06, Pub. Date: 8 Dec. 2023

The increased number of cellular network subscribers is giving rise to the network densification in next generation networks further increasing the greenhouse gas emission and the operational cost of network. Such issues have ignited a keen interest in the deployment of energy-efficient communication technologies rather than modifying the infrastructure of cellular networks. In cellular network largest portion of the power is consumed at the Base stations (BSs). Hence application of energy saving techniques at the BS will help reduce the power consumption of the cellular network further enhancing the energy efficiency (EE) of the network. As a result, BS sleep/wake-up techniques may significantly enhance cellular networks' energy efficiency. In the proposed work traffic and interference aware BS sleeping technique is proposed with an aim of reducing the power consumption of network while offering the desired Quality of Service (QoS) to the users. To implement the BS sleep modes in an efficient manner the prediction of network traffic load is carried out for future time slots. The Long Short term Memory model is used for prediction of network traffic load. Simulation results show that the proposed system provides significant reduction in power consumption as compared with the existing techniques while assuring the QoS requirements. With the proposed system the power saving is enhanced by approximately 2% when compared with the existing techniques. His proposed system will help in establishing green communication networks with reduced energy and power consumption.

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