Data-Driven Small Cell Planning for Traffic Offloading with Users' Differential Privacy

The development of 5G network and rapid growth of mobile traffic bring lucrative opportunities for Micro Operators (mu Os), the novel local operators who own local spectrum, deploy and manage small cells (e.g., femtocells) in a specific area. Collaborating with traditional mobile network operators (MNOs), mu Os gain profits from helping to offload the traffic carried by macro base stations and providing the MNOs customers with seamless service. However, due to the demand uncertainty and the sensitivity of individual user's demand profile, it is challenging for mu Os to allocate the resource properly to avoid the under- and over-utilized situations. To address this issue, in this paper, we propose to employ data-driven approach to characterize the demand uncertainty, exploit differential privacy protocols to protect user's demand profile, and formulate the small cell planning problem into two-stage stochastic programming optimization with the objective of minimizing the capital and operational costs of mu Os. Based on the formulated problem, we develop feasible solutions and conduct extensive simulations using real-world base station accessing real cellular data (i.e., data of 4G LTE network in Zhengzhou city, China) to verify the effectiveness of the proposed model.