Dynamic Base Station Operation in Large-Scale Green Cellular Networks

In this paper, to minimize the on-grid energy cost in a large-scale green cellular network, we jointly design the optimal base station (BS) ON/OFF operation policy and the on-grid energy purchase policy from a network-level perspective. We consider that the BSs are aggregated as a microgrid with hybrid energy supplies and an associated central energy storage, which can store the harvested renewable energy and the purchased on-grid energy over time. Due to the fluctuations of the on-grid energy prices, the harvested renewable energy, and the network traffic loads over time, as well as the BS coordination to hand over the traffic offloaded from the inactive BSs to the active BSs, it is generally NP-hard to find a network-level optimal adaptation policy that can minimize the on-grid energy cost over a long-term and yet assures the downlink transmission quality at the same time. Aiming at the network-level dynamic system design, we jointly apply stochastic geometry (Geo) for large-scale green cellular network analysis and dynamic programming (DP) for adaptive BS ON/OFF operation design and on-grid energy purchase design, and thus propose a new Geo-DP design approach. By this approach, we obtain the optimal BS ON/OFF policy, which shows that the optimal BSs' active operation probability in each horizon is just sufficient to assure the required downlink transmission quality with time-varying load in the large-scale cellular network. However, due to the curse of dimensionality of the DP, it is of high complexity to obtain the optimal on-grid energy purchase policy. We thus propose a suboptimal on-grid energy purchase policy with low complexity, where the low-price on-grid energy is over purchased in the current horizon only when the current storage level and the future renewable energy level are both low. Simulation results show that the suboptimal on-grid energy purchase can achieve near-optimal performance. We also compare the proposed policy with the existing schemes to show that our proposed policy can more efficiently save the on-grid energy cost over time.