Optimization of adaptive antenna system parameters in self-organizing LTE networks

摘要

In wireless communications the demand for wide range of services is leading to a rapid increase in network performance requirements. Hence, today’s cellular radio technologies are designed to operate closer to Shannon capacity bound which sets the ultimate upper limit for the wireless channel capacity. Yet, good link level performance does not necessarily mean that network resources are used efficiently as the cellular capacity and coverage performance may not be optimal resulting from dynamic conditions in radio network environment such as urbanization, insertion or deletion of base stations, and malfunctioning nodes. Due to the fact that reacting on those inherent problems manually is very expensive and time consuming, automated optimization of cellular coverage and capacity by means of self-optimization of adaptive antenna system parameters could be an attractive solution from the network operator’s point of view. Furthermore, suboptimal antenna parameter selection in long term evolution (LTE) network planning or the reuse of the sites and antenna parameters of a preceding access technology requires optimization of adaptive antenna system parameters. In this article we propose a novel centralized self-optimization approach that can be used for adapting antenna system parameters in order to automatically control network capacity and coverage in a macro-cellular deployment. In the proposed approach we present case-based reasoning (CBR) based self-optimization aided by an exemplary rule-based scheme which is required during the training phase of CBR. Dynamic system level downlink simulator is developed to validate the performance of the proposed approach in a realistic macro-cellular scenario. In performance evaluations the 3rd generation partnership project LTE system framework is assumed and propagation is modeled in three dimensions.