Intra-Cluster Autonomous Coverage Optimization For Dense LTE-A Networks

TL;DR

This paper introduces a progressive autonomous coverage optimization method for dense LTE-A networks that adapts to user distributions, effectively detecting and resolving coverage issues with minimal delays, verified through simulations and real deployment.

Contribution

It presents a novel ACO algorithm that builds a virtual coverage map for each cell, enabling timely coverage optimization without extensive network knowledge.

Findings

Significant coverage enhancement demonstrated in simulations.

Effective coverage hole detection and resolution in practical deployment.

Improved key performance indicators in dense LTE-A networks.

Abstract

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.