Self-Organizing Network Power Adjustment via Channel Attenuation Estimation
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Solution Overview
Problem
Current communication systems lack efficient methods for self-optimization of transmission power in wireless networks, leading to suboptimal signal quality and increased interference between base stations and user devices.
Innovation Solution
A computing system estimates wireless channel attention levels between base stations and user devices, analyzing signal quality data and transmission power levels to provide control signals for base stations to auto-adjust their transmission parameters, minimizing interference and optimizing channel quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If base stations use fixed transmission power levels, then network operation is simple and stable, but signal quality is suboptimal and interference between base stations increases
Solution Approach 1:
The system implements self-service through automated power optimization where the computing system autonomously analyzes signal quality data, estimates channel attenuation, and adjusts transmission power levels without manual intervention. This self-organizing network capability allows base stations to automatically optimize their own parameters based on real-time network conditions, improving signal quality while maintaining operational simplicity.
Solution Approach 2:
The patent employs feedback mechanisms by continuously monitoring signal quality measurements from user devices and using this information to dynamically adjust transmission power levels. The computing system collects signal quality data, processes it to estimate channel attenuation, and feeds back optimized power settings to base stations, creating a closed-loop control system that adapts to changing network conditions.
2Reliability
If manual power adjustment is performed, then signal quality can be optimized, but network operation complexity and time consumption increase
Solution Approach 1:
The system eliminates manual intervention by implementing automated power optimization algorithms that continuously analyze signal quality data and adjust transmission parameters. The self-organizing network capability allows the system to autonomously perform measurements, estimate channel conditions, and optimize power levels without human involvement, significantly reducing the time required for network optimization while maintaining improved signal quality.
Solution Approach 2:
The patent applies preliminary action by pre-configuring the automated optimization system with measurement capabilities and adjustment algorithms. The computing system is prepared in advance to continuously monitor signal quality and automatically respond to changing conditions, eliminating the need for time-consuming manual analysis and adjustment processes.
3Reliability
If transmission power is increased to improve signal coverage, then channel quality improves, but power consumption and interference to neighboring cells increase
Solution Approach 1:
The patent implements local quality by estimating channel attenuation specifically for each base station-user device pair based on measured signal quality data. Instead of uniformly increasing power across the network, the system calculates location-specific attenuation values and adjusts power levels locally to match actual channel conditions, improving channel quality only where needed while minimizing unnecessary power consumption and interference.
Solution Approach 2:
The system dynamically changes transmission power parameters based on estimated channel attenuation levels. By continuously monitoring signal quality and adjusting power levels according to actual channel conditions rather than using fixed high power settings, the system optimizes the balance between channel quality and power consumption, increasing power only when and where channel attenuation requires it.
4Productivity
If automated power optimization is implemented, then network performance improves, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the network into individual base station units, each with its own channel attenuation estimation and power optimization process. The computing system processes each base station's signal quality data independently to estimate its specific channel conditions and determine appropriate power levels. This segmented approach improves network throughput through localized optimization while managing system complexity by breaking down the overall optimization problem into smaller, independent tasks.
Data Source
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AI summary
Methods, systems, and apparatus, including computer-readable media, for making power adjustments in self-organizing networks. In some implementations, signal quality data is received for user devices that each communicate wirelessly with at least one base station in a set of base stations. Transmission power data indicating transmission power levels of the base stations is also received. Based on the signal quality data and the transmission power data, signal quality levels for the user devices are determined for different times corresponding to different combinations of transmission power levels of the base stations. Channel attenuation levels are estimated based on differences among signal quality levels of the multiple user devices corresponding to the different combinations of transmission power levels of the base stations. Operating parameters are provided to one or more of the base stations based on the estimated channel attenuation levels.