Cart (Loading....) | Create Account
Close category search window
 

Deployment Strategies and Energy Efficiency of Cellular Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Koutitas, G. ; Sch. of Sci. & Technol., Int. Hellenic Univ., Thessaloniki, Greece ; Karousos, A. ; Tassiulas, L.

The energy efficiency of cellular networks is explored for different network deployment strategies and traffic conditions. The total network power consumption and the ratios Watts/kbps, Watts/user are used as metrics to quantify the performance and it is shown that efficiency depends on the deployment strategy and the state of operation of the network (underutilized-overutilized). As a general conclusion it is shown that a microcell based network, comprising a large number of low power stations, is the most efficient strategy but it does not present traffic proportional characteristics. For the purpose of the investigation, the paper presents a pre-processing of the database 3D ray tracing algorithm that is enhanced with an image test procedure and multiple slope diffraction mechanisms to achieve fast and accurate predictions. The algorithm is used for channel estimations over a real urban environment described in a vector format. In addition, a power control algorithm is developed that explicitly considers power levels of neighbor base stations and is used for the network power consumption estimation. Finally, network planning is achieved through an evolutionary optimization technique that combines the above mentioned algorithms.

Published in:

Wireless Communications, IEEE Transactions on  (Volume:11 ,  Issue: 7 )

Date of Publication:

July 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.