Hierarchical Energy Optimization With More Realistic Power Consumption and Interference Models for Ultra-Dense Networks

To fulfill the high capacity requirements of 5G and B5G, ultra-dense network (UDN) is a promising technology. As the network becomes denser, energy consumption becomes one of the primary concerns of the operators. To reduce the power consumption of UDN, in this paper, we propose a hierarchical energy optimization approach with two energy-efficient strategies: one is dynamic coverage for large time-scale scenario; the other is individual power adjustment based on weakly standard interference mapping for small time-scale scenario. Considering a more realistic power consumption model, the optimization problem is formulated as adapting network capacity to users' demands, so as to improve energy consumption and other network performances e.g., load balancing. The proposed approach owns the following advantages: First, it can obtain the optimal solution with low computational complexity; second, they are distributed and thus the considered problem can be solved in a parallel manner; all of these make the proposed methods suitable for UDN. Simulation results verify that the proposed approach can obtain the optimal solution in few iterations and the complexity of computation is low.