TY - GEN
T1 - Evaluating Energy-Efficient Cloud Radio Access Networks for 5G
AU - Sigwele, Tshiamo
AU - Alam, Atm Shafiul
AU - Pillai, Prashant
AU - Hu, Y. Fun
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015
Y1 - 2015
N2 - Next-generation cellular networks such as fifth-generation (5G) will experience tremendous growth in traffic. To accommodate such traffic demand, there is a necessity to increase the network capacity that eventually requires the deployment of more base stations (BSS). Nevertheless, BSS are very expensive and consume a significant amount of energy. Meanwhile, cloud radio access networks (C-RAN) has been proposed as an energy-efficient architecture that leverages cloud computing technology where baseband processing is performed in the cloud, i.e., the computing servers or baseband processing units (BBUs) are located in the cloud. With such an arrangement, more energy saving gains can be achieved by reducing the number of BBUs used. This paper proposes a bin packing scheme with three variants such as First-fit (FT), First-fit decreasing (FFD) and Next-fit (NF) for minimizing energy consumption in 5G C-RAN. The number of BBUs are reduced by matching the right amount of baseband computing load with traffic load. In the proposed scheme, BS traffic items that are mapped into processing requirements, are to be packed into computing servers, called bins, such that the number of bins used are minimized and idle servers can then be switched off to save energy. Simulation results demonstrate that the proposed bin packing scheme achieves an enhanced energy performance compared to the existing distributed BS architecture.
AB - Next-generation cellular networks such as fifth-generation (5G) will experience tremendous growth in traffic. To accommodate such traffic demand, there is a necessity to increase the network capacity that eventually requires the deployment of more base stations (BSS). Nevertheless, BSS are very expensive and consume a significant amount of energy. Meanwhile, cloud radio access networks (C-RAN) has been proposed as an energy-efficient architecture that leverages cloud computing technology where baseband processing is performed in the cloud, i.e., the computing servers or baseband processing units (BBUs) are located in the cloud. With such an arrangement, more energy saving gains can be achieved by reducing the number of BBUs used. This paper proposes a bin packing scheme with three variants such as First-fit (FT), First-fit decreasing (FFD) and Next-fit (NF) for minimizing energy consumption in 5G C-RAN. The number of BBUs are reduced by matching the right amount of baseband computing load with traffic load. In the proposed scheme, BS traffic items that are mapped into processing requirements, are to be packed into computing servers, called bins, such that the number of bins used are minimized and idle servers can then be switched off to save energy. Simulation results demonstrate that the proposed bin packing scheme achieves an enhanced energy performance compared to the existing distributed BS architecture.
UR - http://www.scopus.com/inward/record.url?scp=84964489774&partnerID=8YFLogxK
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U2 - 10.1109/DSDIS.2015.73
DO - 10.1109/DSDIS.2015.73
M3 - Conference contribution
AN - SCOPUS:84964489774
T3 - Proceedings - 2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE International Conference on Internet of Things, DSDIS/CPSCom/GreenCom/iThings 2015
SP - 362
EP - 367
BT - Proceedings - 2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE International Conference on Internet of Things, DSDIS/CPSCom/GreenCom/iThings 2015
A2 - Yang, Laurence T.
A2 - Chen, Jinjun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE International Conference on Internet of Things, DSDIS/CPSCom/GreenCom/iThings 2015
Y2 - 11 December 2015 through 13 December 2015
ER -