On Designing Energy-Efficient Heterogeneous Cloud Radio Access Networks

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Abstract
Heterogeneous cloud radio access network (H-CRAN) promises higher energy efficiency (EE) than the conventional cellular networks by centralizing the baseband signal processing into the baseband unit (BBU) pool hosted by cloud computing platforms. Because of the difference between H-CRAN and conventional cellular networks, existing energy-efficient networking mechanisms designed for conventional cellular networks cannot fully leverage H-CRAN in terms of reducing the network energy consumption. In this paper, we bridge this gap by proposing a radio resource management scheme to optimize the network energy efficiency (NEE) of H-CRAN. We develop a network energy consumption model that characterizes the energy consumption of radio access points (RAPs), fronthaul, and the BBU pool in H-CRAN. Based on the network energy consumption model, we formulate the NEE optimization problem with the consideration of the capacity constrained fronthaul. The NEE optimization problem is a mixed integer non-linear programming problem (MINLP). We propose the H-CRAN energy-efficient radio resource management (HERM) algorithm to solve the NEE optimization problem efficiently. Various properties of the proposed solution are derived and extensive simulations are conducted. The simulation results show that the HERM algorithm significantly improves the NEE of H-CRAN. As compared with a baseline algorithm in which the radio resource management is not optimized, HERM boosts the NEE by 59% under the dynamic network traffic. As compared to an energy-efficient radio resource allocation (ERA) algorithm which does not optimize the energy consumption of the BBU pool, the NEE of H-CRAN achieved by the HERM algorithm is up to 51% better than that by the ERA algorithm with network traffic dynamics.
Funding Information
  • National Science Foundation (1731675, 1320468)
  • University of North Carolina at Charlotte

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