Open Loop Power Control Parameter Optimization
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Solution Overview
Problem
Current uplink power control methods in cellular networks face challenges in efficiently optimizing open loop power control parameters, leading to suboptimal network performance due to the combinatorial nature of parameter selection and slow convergence of analytical models, which can result in inefficient resource usage and user experience issues.
Innovation Solution
A self-organizing network architecture that collects key performance indicators from base stations, determines a statistical model, and adjusts open loop power control parameters to maximize network-wide key performance indicators, using techniques like Gaussian processes and Bayesian optimization to iteratively improve parameter configurations without causing temporary performance losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If analytical models are used to optimize open loop power control parameters, then optimization can be performed systematically, but convergence is slow and temporary performance losses occur during exploration
Solution Approach 1:
The system performs preliminary exploration of the parameter space by collecting KPI data for multiple parameter configurations before committing to optimization. This preliminary action builds a statistical model in advance, avoiding the need for slow iterative exploration during actual optimization, thus reducing convergence time while maintaining reliability
Solution Approach 2:
The patent replaces traditional analytical optimization models with a statistical model based on collected empirical data. This substitution allows the system to learn optimal parameters from actual network behavior rather than relying on theoretical models, achieving faster convergence without temporary performance degradation
2Measurement precision
If exhaustive parameter exploration is performed to find optimal configurations, then optimization accuracy improves, but resource consumption and complexity increase
Solution Approach 1:
The system enables base stations to autonomously collect and report their own KPI data for different parameter configurations. This self-service approach eliminates the need for complex centralized control and exhaustive exploration, as each base station independently contributes data that feeds into the statistical model, achieving high accuracy with reduced system complexity
Solution Approach 2:
The system implements a feedback mechanism where KPI measurements from actual network operation are continuously collected and used to update the statistical model. This feedback loop allows the system to progressively refine parameter recommendations based on real performance data, achieving high optimization accuracy without requiring complex a priori knowledge or exhaustive exploration
3Reliability
If traditional power control parameter adjustment is used, then network performance can be improved, but resource usage efficiency remains suboptimal due to slow adaptation
Solution Approach 1:
The system dynamically adapts power control parameters based on real-time network conditions and collected performance data. Rather than using static or slowly-adjusted parameters, the statistical model continuously learns from KPI feedback and recommends updated parameter configurations, enabling fast adaptation to changing network conditions and optimizing resource usage efficiency while maintaining high network performance
Data Source
AI summary
Disclosed is a method for determining open loop power control parameters. A first set of key performance indicators associated with a first set of open loop power control parameters is obtained from one or more first base stations. A statistical model is determined based at least partly on the first set of key performance indicators and the first set of open loop power control parameters. A second set of open loop power control parameters is determined based at least partly on the statistical model.