Distributionally Robust Two-Stage Energy Management for Hybrid Energy Powered Cellular Networks

Energy harvesting has been recognized as a good paradigm to decrease traditional grid energy expenditure, which caters for 5G visions on the green evolution of cellular networks. However, the harvested energy at each base station is random and intermittent, which imposes a great challenge to reliably satisfy the time-varying users' wireless traffic requirements. What's worse, the perfect probability distributions of harvested energy and wireless traffic are hard to obtain in practical systems, making it difficult to apply the conventional optimization approach to cope with the uncertainties in the energy management problems. Inspired by this fact, we propose a distributionally robust two-stage stochastic optimization framework to minimize the expected total energy cost of mobile network operators (MNOs) in the finite time horizon. To circumvent the computational difficulty of this stochastic optimization problem, we define a novel ambiguity set to capture the uncertainties of the harvested energy and wireless traffic based on their first-and second-order statistics information. By employing the distributionally robust optimization (DRO) methodology, we equivalently transform the original two-stage stochastic programming problem into a computationally tractable second-order cone programming problem. Then, a distributionally robust two-stage energy management algorithm (DRTEMA) is proposed, which has good performance of low complexity and only requires partial information of the stochastic parameters. Rigorous computational complexity analysis and extensive numerical simulations are conducted to show the advantages of the proposed algorithm.