Radio Parameter Optimization via Dynamic Network Configuration
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Solution Overview
Problem
Communication networks face dynamic changes due to various environmental factors, leading to fluctuations in network coverage and quality, which existing methods struggle to optimize effectively.
Innovation Solution
A system comprising a noise configuration server and a measurement analysis server that varies radio parameters of the network during active data trafficking, determines optimal signal quality parameters, and configures the network to use these parameters for improved coverage and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network parameters are varied to optimize signal quality, then network coverage and quality improve, but network stability during data trafficking deteriorates
Solution Approach 1:
The system dynamically adjusts network parameters in real-time based on measured signal quality, transforming the static network configuration into a dynamic adaptive system that responds to environmental changes while maintaining operational stability through controlled experimentation
Solution Approach 2:
The system implements periodic parameter variations during data trafficking periods, using time-divided experimentation where parameters are varied in controlled cycles to optimize signal quality without causing continuous instability, allowing the network to adapt rhythmically rather than chaotically
2Productivity
If multiple radio parameters are varied simultaneously to find optimal settings, then optimization effectiveness improves, but system complexity increases
Solution Approach 1:
The optimization process segments the parameter search space by dividing simultaneous parameter variations into coordinated groups, where the noise configuration server systematically varies parameters in a structured manner rather than randomly, reducing the effective complexity while maintaining comprehensive optimization coverage
Solution Approach 2:
The noise configuration server acts as an intermediary that manages the complexity of multi-parameter optimization by coordinating variations across frequency bands, modulation schemes, and transmit powers, shielding the network from the full complexity while enabling comprehensive parameter exploration through the measurement analysis server
3Measurement precision
If parameter variations are performed during active data trafficking, then real-world optimization accuracy improves, but interference with data transmission increases
Solution Approach 1:
The system uses feedback from the measurement analysis server that monitors signal quality metrics during active data trafficking, adjusting parameter variations based on real-time performance data to minimize interference while maintaining optimization accuracy, creating a closed-loop system that adapts to actual network conditions
Solution Approach 2:
The system carefully controls the magnitude and rate of parameter changes during data trafficking, using small incremental adjustments rather than large sudden changes, which reduces the harmful impact on data transmission while still enabling accurate measurement of optimization effects through the feedback mechanism
Data Source
AI summary
A method and apparatus for optimizing coverage of a network are provided. In the method and apparatus, parameters of the network are varied during a first period of time when the network is actively used for trafficking data in a geographic area. In response to varying the parameters, signal quality for the network and an optima for the signal quality are determined. A first set of parameters that result in the optima for the signal quality are identified and the network is configured to use the first set of parameters during a second period of time subsequent to the first period of time.