| dc.description.abstract | RAN energy consumption is a major source of OPEX costs for mobile telecom operators, and 5G is expected to increase these costs by several folds. Moreover, paradigm-shifting aspects of the 5G RAN architecture like RAN disaggregation, virtualization and cloudification not only change the nature of the RAN energy efficiency optimization problem, but also makes it harder due to new variables to orchestrate and adapt to traffic variations. We tackle the problem of energy-efficient 5G RAN orchestration, targeting metro-scale scenarios. To this end, we present a first comprehensive virtualized RAN (vRAN) system model aligned with 5G RAN specifications, and embedding realistic and dynamic models for computational load and energy consumption costs. We then formulate the vRAN energy consumption optimization as an integer quadratic programming problem, whose NP-hard nature leads us to develop GreenRAN, a novel, computationally efficient and distributed solution that leverages Lagrangian decomposition and simulated annealing. Evaluations with real-world mobile traffic data for a large metropolitan area are another novel aspect of this work, and show that our approach yields energy efficiency gains up to 25% and 42%, over state-of-the-art and baseline traditional RAN approaches, respectively. | |
