Ericsson AAS Cell Shaper rApp
Ericsson | Network Optimization
Advanced Antenna System (AAS) optimization based on AI Reinforcement Learning techniques. Optimizing cell level digital tilt (vertical plane) and beamwidth (horizontal plane). Automatically adapts to the characteristics of each cell and the surrounding network leading to optimized radio environment and traffic distribution significantly improving the end user experience.
OPEX reductions: Optimization automation and continuous adaptation to traffic profile
Automates complex cell shaping activities in AAS at cluster/market level as it involves a 2-dimensional problem at both vertical and horizontal domains. Significantly reduces the need of manual activities by automating the whole process of data collection, data processing, digital tilt + beamwidth proposals and actuation.
Performance: Enhancing user satisfaction and reduce churn
Improving performance in challenging scenarios such as congested areas and high interference to name a few. Analysis is beyond mere coverage hole and overshooting detection as it takes a comprehensive analysis considering impact on neighbors, different layers.
CAPEX avoidance: Delayed need of capacity expansions
Increases traffic served in congested cells with the same available radio capacity. This is achieved by intelligent traffic shifting from congested cells to neighbors with room to accept additional load without performance impact
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