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

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 strategies. However, such confidential measurement data must be concealed from others. We therefore propose a novel method with masking noise. The proposed algorithm is designed based on distributed optimization, which each SBS locally solves divided optimization problem and learns how much global constraint satisfied or violated temporal solutions. important feature this control robustness against analyze tradeoff between confidentiality accuracy, using notion differential privacy. Numerical simulations show that outperforms standard centralized presence