Profitable and Energy-Efficient Resource Optimization for Heterogeneous Cloud-Based Radio Access Networks

• Published in: IEEE access Vol. 7; pp. 34719 - 34737
• Main Authors: Kim, Taewoon, Chang, J. Morris
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Base stations; Cloud computing; Cloud-RAN; Computer architecture; Energy efficiency; Environmental impact; heterogeneous C-RAN; Infrastructure; Operators; Optimization; Power consumption; Power demand; Profit maximization; Radio access networks; Resource allocation; resource optimization; Stations; stochastic programming; Task analysis; Uncertainty
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2904766

As network operators invest more and more in infrastructure to keep up with the ever-increasing traffic demand, it has become important for them to operate networks in a profitable manner. The resulting expansion of network infrastructure also increases power consumption, which has a negative impact on both the environment and revenue. In this regard, the cloud-based radio access network (C-RAN), which is a promising next-generation network architecture, has gained much attention as a solution. In addition, by utilizing the macro base stations for coverage, the resulting heterogeneous C-RAN (H-CRAN) can further help optimize the network while increasing the complexity of resource optimization. In this paper, we study an optimal resource allocation for C-RANs to maximize profit while minimizing power consumption considering the inherent network uncertainties. Moreover, by allowing network operators to share networking resources among themselves, we show that the service outage can be minimized without installing additional bandwidth or base stations. The proposed multi-stage stochastic programming model makes a robust optimal decision that effectively responds to uncertainties from users' mobility and service demand while maximizing both profit and energy efficiency. The extensive evaluation and comparison results show that the proposed solution can maximize profit and energy efficiency while minimizing the service outage under network uncertainties.