Analysis of multiple sleeps and N-policy on a M/G/1/K user request queue in 5g networks base station
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.2.21Keywords:
Mobile Network, Wireless Network, Energy Consumption, Multiple Sleeps, N- Policy, Finite capacity.Dimensions Badge
Issue
Section
License
Copyright (c) 2023 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The primary purpose of green communication is to reduce energy use. The base station (BS) is a radio receiver/transmitter that acts as the wireless network’s hub. It serves as a link between a wired and wireless network. To receive and transmit messages, BS uses a lot of energy. The use of effective sleep and wake-up/setup activities with an acceptable delay helps reduce base station power consumption. In this paper, the BS’s service process is modelled as a finite buffer queue with close down, sleep, and setup. After a certain number of user requests (URs) have accumulated in the system, to awaken the BS from multiple sleeps (MS) the -Policy is implemented. To produce probability generating functions, the supplementary variable approach is applied. The UR’s mean delay and the BS’s mean power consumption are calculated using simulation. According to computational studies, multiple sleeps with -policy consume less power than multiple sleeps without -policy.Abstract
How to Cite
Downloads
Similar Articles
- Neha Saini, Rashmi Verma, Rabia Basri Aziz, Ashmita Bhatt, Hem Chandra Pant, Naveen Gaurav, Effect of Growth Regulators on Direct Clonal Propagation and Analysis of Total Phenolic Content of Wild and Propagated Mucuna pruriens , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- R Prabhu, S Sathya, P Umaeswari, K Saranya, Lung cancer disease identification using hybrid models , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Jhankar Moolchandani, Kulvinder Singh, English language analysis using pattern recognition and machine learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Temesgen A. Asfaw, Batch size impact on enset leaf disease detection , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Nilay Shukla, Ketan Desai, Study on the right to education with special references to public private partnerships , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Aman Bora, Akhilesh Dwivedi, From Protectionism to Green Multilateralism: Trade Diplomacy and Environmental Accountability in the Global South , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- A. Kamatchi, Dr. V. Maniraj, An early classification of Alzheimer’s Disease with deep Features using Advanced Deep Learning Method (Graph Convolutional Neural Networks) , The Scientific Temper: Vol. 17 No. 03 (2026): The Scientific Temper
- Anjum Parvez, Seema Yadav, Sandhya Verma, Electronic Record as Evidence in the Courts: An Analysis , The Scientific Temper: Vol. 12 No. 1&2 (2021): The Scientific Temper
- Payal Dalal, The Silent Scars: Child Sexual Abuse and the Burden of Memory in Anuradha Roy’s Sleeping on Jupiter , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- Arunima Dey, Kankana Ghosh, Debangana Chakrabarti, Mahul Brahma, Re-envisioning the mainstream: A study on the acceptance of LGBTQIA+ Protagonists on a Bengali OTT platform , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
<< < 23 24 25 26 27 28 29 30 31 32 > >>
You may also start an advanced similarity search for this article.

