Resource Allocation for Green Cloud Radio Access Networks With Hybrid Energy Supplies

In this paper, we study sustainable resource allocation for cloud radio access networks (CRANs) powered by hybrid energy supplies (HES). Specifically, the central unit (CU) in the CRANs distributes data to a set of radio units (RUs) powered by both on-grid energy and energy harvested from green sour...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2018-02, Vol.67 (2), p.1684-1697
Main Authors: Zhang, Deyu, Chen, Zhigang, Cai, Lin X., Zhou, Haibo, Duan, Sijing, Ren, Ju, Shen, Xuemin, Zhang, Yaoxue
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Batteries
Buffers
Cellular networks
Clean energy
Cloud radio access network
Computer simulation
Energy
Energy costs
Energy harvesting
Energy management
hybrid energy supplies
Indexes
Optimization
Radio
Resource allocation
Resource management
stochastic optimization
Sustainability
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Summary:In this paper, we study sustainable resource allocation for cloud radio access networks (CRANs) powered by hybrid energy supplies (HES). Specifically, the central unit (CU) in the CRANs distributes data to a set of radio units (RUs) powered by both on-grid energy and energy harvested from green sources, and allocates channels to the selected RUs for downlink transmissions. We formulate an optimization problem to maximize the net gain of the system which is the difference between the user utility gain and on-grid energy costs, taking into consideration the stochastic nature of energy harvesting process, time-varying on-grid energy price, and dynamic wireless channel conditions. A resource allocation framework is developed to decompose the formulated problem into three subproblems, i.e., the hybrid energy management, data requesting, and channel and power allocation. Based on the solutions of the subproblems, we propose a net gain-optimal resource allocation (GRA) algorithm to maximize the net gain while stabilizing the data buffers and ensuring the sustainability of batteries. Performance analysis demonstrates that the GRA algorithm can achieve close-to-optimal net gain with bounded data buffer and battery capacity. Extensive simulations validate the analysis and demonstrate that GRA algorithm outperforms other algorithms in terms of the net gain and delay performance.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2017.2754273