DocumentCode :
131150
Title :
Cognitive green backhaul deployments for future 5G networks
Author :
Jialu Lun ; Grace, David
Author_Institution :
Dept. of Electron., Univ. of York, York, UK
fYear :
2014
fDate :
2-4 Sept. 2014
Firstpage :
1
Lastpage :
5
Abstract :
This paper introduces a cognitive green topology management scheme for future 5Gnetworks, which can be used to reduce energy consumption in low traffic scenarios. The scheme is based on a backhaul link selection algorithm which aims to concentrate distributed traffic on fewer backhaul links by exploiting backhaul link diversity from other cells. A reinforcement learning based resource assignment algorithm has been introduced to work in conjunction with the topology management scheme. It is shown that total energy consumption can be reduced by up to 35% with marginal Quality of Service compromises. In addition, the tradeoff between energy saving and control overhead is also explored in this paper.
Keywords :
cellular radio; cognitive radio; learning (artificial intelligence); power consumption; quality of service; radio links; telecommunication network topology; telecommunication power management; telecommunication traffic; 5G networks; backhaul link diversity; backhaul link selection algorithm; cognitive green topology management scheme; control overhead; distributed traffic concentration; energy consumption; energy saving; learning reinforcement; quality of service; resource assignment algorithm; Energy consumption; Energy efficiency; Green products; Mobile communication; Network topology; Telecommunication traffic; Topology; Backhaul Link Diversity; Cognitive Networks; Green Topology Management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cognitive Cellular Systems (CCS), 2014 1st International Workshop on
Conference_Location :
Germany
Type :
conf
DOI :
10.1109/CCS.2014.6933790
Filename :
6933790
Link To Document :
بازگشت