Boosting the Cellular Network Coverage Optimization in Accordance With the Metric Structure of Antenna Variables

Abstract

Cellular networks are bound to connect an ever-increasing number of subscribers. The issue of securing both sufficient capacity and reliable coverage remains to be resolved. This paper introduces the maximum coverage problem in wireless cellular networks and gets insight into the metric structure of the solution space for antenna orientation variables. We construct two metric spaces in mathematical views, in which both the deterministic search method (e.g., Nelder-Mead simplex algorithm) and the stochastic search method (e.g., Genetic Algorithm) have been fully discussed without using gradient information. Accordingly, we propose the improved deterministic and stochastic search methods to boost the coverage optimization procedure. Experiments show that the proposed algorithms not only obtain the close-to-optimal solution but greatly improve the convergence speed by reason that redundant exploration is avoided in the tailored solution spaces. Metric structure, as an essential topology in the antenna orientation solution space, therefore, provides a new perspective to settle other antenna orientation-related coverage and capacity optimization problems.