Work place: Dayanand Sagar College of Engineering, Bangalore-560111, India
Dr. Anju Kulkrni Professor, India with 30 plus years of academic experience, for the graduate and post graduate programmes of Engineering under University of Pune, Prof. (Dr.) Anju Kulkarni has worked as Dean - Research and Professor in E & TC, at Dr. D. Y. Patil Institute of Technology, Pune. Curently she is working as Head of E&TC Department, DSCE Bangalore. She is associated with University of Pune as Academic Counicil Member, 2010-15. She has been a Visiting Faculty for Work Integrated programme of BITS-Pilani. Anju Kulkarni is registered guide for Ph. D Programme under SPPU and external referee for M. E and Ph. D examinations for various universities. She has published 70 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.
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.[...] Read more.
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