Title

An energy efficient resource management and planning system for 5G networks

Document Type

Conference Proceeding

Publisher

Institute of Electrical and Electronics Engineers Inc.

Place of Publication

United States

Comments

Originally published as: Mowla, M. M., Ahmad, I., Habibi, D., & Phung, Q. V. (2017, January). An energy efficient resource management and planning system for 5G networks. In Consumer Communications & Networking Conference (CCNC), 2017 14th IEEE Annual (pp. 216-224). IEEE. Available here.

Abstract

The concept of heterogeneous networks and small cells is likely to be a key addition to the fifth generation (5G) wireless communication standard, the prospect of which has motivated 5G researchers to investigate the coverage and integration of various small cells. Heterogeneous networks with small cells offer key benefits such as high frequency reuse, huge data rates and low power consumption for mobile nodes. High frequency reuse is particularly important given the exponential growth of data rates and the looming frequency scarcity problem. Another major consideration for 5G is the energy efficiency as with the exponential growth of demand, power consumption in the information and communication technology (ICT) sector is also expected to significantly increase. While the concept of small cells in heterogeneous networks addresses the frequency scarcity problem to a great extent, unless otherwise carefully managed, a large number of uncoordinated and lightly loaded small cells significantly increase the overall power consumption, contrary to the green communication target of the 5G standard. In this paper, we focus on an energy efficient resource management and planning system that investigates the impact of growing number of small cells in a macro cell. We present an analytical model for calculating the optimum number of small cells that need to be kept awake at various hours of a day for meeting the quality of service demands from all users. Simulation results show significant power consumption improvement than an existing model.

DOI

10.1109/CCNC.2017.7983108

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