  Self Organizing Networks (SONs) are considered as vital deployments towards
upcoming dense cellular networks. From a mobile carrier point of view,
continuous coverage optimization is critical for better user perceptions. The
majority of SON contributions introduce novel algorithms that optimize specific
performance metrics. However, they require extensive processing delays and
advanced knowledge of network statistics that may not be available. In this
work, a progressive Autonomous Coverage Optimization (ACO) method combined with
adaptive cell dimensioning is proposed. The proposed method emphasizes the fact
that the effective cell coverage is a variant on actual user distributions. ACO
algorithm builds a generic Space-Time virtual coverage map per cell to detect
coverage holes in addition to limited or extended coverage conditions.
Progressive levels of optimization are followed to timely resolve coverage
issues with maintaining optimization stability. Proposed ACO is verified under
both simulations and practical deployment in a pilot cluster for a worldwide
mobile carrier. Key Performance Indicators show that proposed ACO method
significantly enhances system coverage and performance.
