Multi-Shot Network Parameter Optimization for Base Stations
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Solution Overview
Problem
Current network parameter optimization methods for RF parameters, such as common beam and handover parameters, require additional information like UE locations or 3D maps, leading to implementation complexity and incompatibility with existing base stations.
Innovation Solution
The method generates Key Performance Indicators (KPI) constraints to adjust common beam and handover A2 and A5 parameters within specified thresholds, allowing for full power UL MIMO operation without the need for additional information, enabling direct optimization at enhanced Node-Bs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional information such as UE locations, 3D maps, or Sounding reference signals is used to assist base station performance estimation, then network parameter optimization accuracy is improved, but implementation complexity increases and compatibility with existing base stations deteriorates
Solution Approach 1:
The patent extracts and removes the requirement for additional information (UE locations, 3D maps, SRS) from the optimization process. Instead of using these complex external inputs, the system relies solely on existing KPI data that is already available at the base station, thereby maintaining optimization accuracy while eliminating implementation complexity and ensuring compatibility with existing infrastructure
Solution Approach 2:
The base station performs self-optimization using its own internal KPI measurements without requiring external assistance or additional signaling. The system serves itself by leveraging existing performance data to automatically adjust network parameters, eliminating the need for complex external information gathering and processing mechanisms
2Measurement precision
If additional information such as UE locations, 3D maps, or Sounding reference signals is used to assist base station performance estimation, then network parameter optimization accuracy is improved, but compatibility with existing base stations deteriorates
Solution Approach 1:
The patent makes the optimization method universal by designing it to work with existing base station capabilities without requiring additional information infrastructure. The system uses KPI data that is universally available in modern base stations, allowing the optimization to be deployed across existing networks without modification, thereby achieving both accuracy and broad compatibility
Solution Approach 2:
Instead of making existing base stations adapt to new complex information requirements, the patent inverts the approach by making the optimization method adapt to existing base station capabilities. The system is designed to work with what is already available (KPI data) rather than requiring base stations to acquire and process new types of information, ensuring backward compatibility
3Reliability
If common beam parameters and handover parameters are adjusted based on KPI constraints, then network performance is improved, but optimization process complexity increases
Solution Approach 1:
The patent systematically changes network parameters (common beam parameters, handover A2 and A5 parameters) based on KPI constraints through a structured multi-shot optimization process. The method divides the optimization into discrete shots where parameters are adjusted in controlled steps, making the complex optimization process manageable and systematic rather than chaotic or overly complicated
Solution Approach 2:
The optimization process is segmented into multiple discrete shots or stages, each focusing on specific parameter adjustments. This segmentation breaks down the complex optimization task into manageable chunks, where each shot addresses particular KPI constraints and parameter sets, making the overall process more systematic and less overwhelming
Data Source
AI summary
A system and method of a base station are configured to enable a multi-shot network parameter optimization. The method includes generating one or more specified Key Performance Indicators (KPI) constraints based on a selected set of KPIs. The method also includes adjusting common beam parameters to tune a common beam based on the selected set of KPIs. The common beam is tuned to satisfy the one or more specified KPI constraints. The method also includes adjusting handover A2 and A5 parameters based on searching within a three-dimensional space defined by specified A2 and A5 thresholds. The method further includes transmitting one or more signals based on the adjusted the common beam parameters and the adjusted handover A2 and A5 parameters.