The tradeoff between coverage and capacity in dynamic optimization of 3G cellular networks

Self organising network techniques to maximise traffic offload onto a 3G/WCDMA small cell network using MDT UE measurement reports

“…References [4,5,6] for example present three typical approaches to SON when applied to antenna tilt in macrocellular networks, namely brute force, simulated annealing and methods based on network Key Performance Indicator (KPI) feedback. While all macrocell methods presented in the literature generally result in an improved network performance, none specifically address the problem of traffic offload to an under laid low power microcellular layer by applying SON techniques to the tilt and power settings of the macrocell layer as is proposed here in this paper.…”

Wireless network analysis using per call measurement data

“…While this could be a consequence of optimal base station placement, it is suspected that higher signal strengths will generally result in higher network utilization. This is an area of investigation in the dynamic optimization program [1,4,5]. GLASS tools have also been developed to generate traffic meshes for direct import into optimization tools like Alcatel-Lucent's Ocelot ® software [3] in support of closed-loop dynamic optimization systems with automatic feedback of subscriber experiences.…”

Dynamic optimization in future cellular networks

“…The assignment of users to cells is determined by an independent process, which is uncoordinated with channel power allocation and can therefore lead to large variations in power demand from one cell to the next. As shown in numerous predictions and field trials, network capacity can be substantially improved by balancing these load variations through proper reassignment of users from overloaded to lightly loaded cells [5,6,8]. Such a procedure is performed during the network optimization process, where cell boundaries are shifted through adjustments of antenna configuration or pilot power to balance the long-term average loading of cells.…”

“…Originally, these optimizations were performed through a manual, iterative process relying on network planning tools and drive testing. Bell Labs subsequently introduced the concept of predictive optimization in the Ocelot ® optimization tool, which computes optimum network parameters directly according to well-defined performance metrics [5,7,8]. Its introduction has translated into faster network rollouts, improved network performance, and higher capacity.…”