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Energy-Efficient Joint Congestion Control and Resource Optimization in Heterogeneous Cloud Radio Access Networks

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Energy-Efficient Joint Congestion Control and Resource Optimization in Heterogeneous Cloud Radio Access Networks. / Li, Jian; Peng, M.; Yu, Y. et al.

In: IEEE Transactions on Vehicular Technology, Vol. 65, No. 12, 12.2016, p. 9873-9887.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Li J, Peng M, Yu Y, Ding Z. Energy-Efficient Joint Congestion Control and Resource Optimization in Heterogeneous Cloud Radio Access Networks. IEEE Transactions on Vehicular Technology. 2016 Dec;65(12):9873-9887. Epub 2016 Feb 18. doi: 10.1109/TVT.2016.2531184

Author

Li, Jian ; Peng, M. ; Yu, Y. et al. / Energy-Efficient Joint Congestion Control and Resource Optimization in Heterogeneous Cloud Radio Access Networks. In: IEEE Transactions on Vehicular Technology. 2016 ; Vol. 65, No. 12. pp. 9873-9887.

Bibtex

@article{4aabe698a10745a2a24dcadf6620d9ea,
title = "Energy-Efficient Joint Congestion Control and Resource Optimization in Heterogeneous Cloud Radio Access Networks",
abstract = "The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm that integrates the advantages of cloud radio access networks and heterogeneous networks. In this paper, we study joint congestion control and resource optimization to explore the energy efficiency (EE)-guaranteed trade-off between throughput utility and delay performance in a downlink slotted H-CRAN. We formulate the considered problem as a stochastic optimization problem, which maximizes the utility of average throughput and maintains the network stability subject to the required EE constraint and transmit power consumption constraints by traffic admission control, user association, resource block allocation, and power allocation. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem can be transformed and decomposed into three separate subproblems that can be concurrently solved at each slot. The third mixed-integer nonconvex subproblem is efficiently solved by utilizing the continuity relaxation of binary variables and the Lagrange dual decomposition method. Theoretical analysis shows that the proposal can quantitatively control the throughput-delay performance trade-off with the required EE performance. Simulation results consolidate the theoretical analysis and demonstrate the advantages of the proposal from the prospective of queue stability and power consumption. {\textcopyright} 2016 IEEE.",
keywords = "Congestion control, energy efficiency (EE), heterogeneous cloud radio access networks (H-CRANs), Lyapunov optimization, resource optimization",
author = "Jian Li and M. Peng and Y. Yu and Z. Ding",
year = "2016",
month = dec,
doi = "10.1109/TVT.2016.2531184",
language = "English",
volume = "65",
pages = "9873--9887",
journal = "IEEE Transactions on Vehicular Technology",
issn = "0018-9545",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "12",

}

RIS

TY - JOUR

T1 - Energy-Efficient Joint Congestion Control and Resource Optimization in Heterogeneous Cloud Radio Access Networks

AU - Li, Jian

AU - Peng, M.

AU - Yu, Y.

AU - Ding, Z.

PY - 2016/12

Y1 - 2016/12

N2 - The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm that integrates the advantages of cloud radio access networks and heterogeneous networks. In this paper, we study joint congestion control and resource optimization to explore the energy efficiency (EE)-guaranteed trade-off between throughput utility and delay performance in a downlink slotted H-CRAN. We formulate the considered problem as a stochastic optimization problem, which maximizes the utility of average throughput and maintains the network stability subject to the required EE constraint and transmit power consumption constraints by traffic admission control, user association, resource block allocation, and power allocation. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem can be transformed and decomposed into three separate subproblems that can be concurrently solved at each slot. The third mixed-integer nonconvex subproblem is efficiently solved by utilizing the continuity relaxation of binary variables and the Lagrange dual decomposition method. Theoretical analysis shows that the proposal can quantitatively control the throughput-delay performance trade-off with the required EE performance. Simulation results consolidate the theoretical analysis and demonstrate the advantages of the proposal from the prospective of queue stability and power consumption. © 2016 IEEE.

AB - The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm that integrates the advantages of cloud radio access networks and heterogeneous networks. In this paper, we study joint congestion control and resource optimization to explore the energy efficiency (EE)-guaranteed trade-off between throughput utility and delay performance in a downlink slotted H-CRAN. We formulate the considered problem as a stochastic optimization problem, which maximizes the utility of average throughput and maintains the network stability subject to the required EE constraint and transmit power consumption constraints by traffic admission control, user association, resource block allocation, and power allocation. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem can be transformed and decomposed into three separate subproblems that can be concurrently solved at each slot. The third mixed-integer nonconvex subproblem is efficiently solved by utilizing the continuity relaxation of binary variables and the Lagrange dual decomposition method. Theoretical analysis shows that the proposal can quantitatively control the throughput-delay performance trade-off with the required EE performance. Simulation results consolidate the theoretical analysis and demonstrate the advantages of the proposal from the prospective of queue stability and power consumption. © 2016 IEEE.

KW - Congestion control

KW - energy efficiency (EE)

KW - heterogeneous cloud radio access networks (H-CRANs)

KW - Lyapunov optimization

KW - resource optimization

U2 - 10.1109/TVT.2016.2531184

DO - 10.1109/TVT.2016.2531184

M3 - Journal article

VL - 65

SP - 9873

EP - 9887

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

SN - 0018-9545

IS - 12

ER -