Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach

Abstract

Cell zooming has been becoming an essential enabler for off-grid small cell networks. Traditional models often utilize the numbers of active users in order to determine cell zooming strategies. However, such confidential measurement data must be concealed from others. We therefore propose a novel cell zooming method with masking noise. The proposed algorithm is designed based on distributed optimization, in which each SBS locally solves a divided optimization problem and learns how much a global constraint is satisfied or violated for temporal solutions. The important feature of this distributed control method is robustness against masking noise. We analyze the trade-off between confidentiality and optimization accuracy, using the notion of differential privacy. Numerical simulations show that the proposed distributed control method outperforms a standard centralized control method in the presence of masking noise.