Distributed Coverage Optimization in Wireless Networks
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Solution Overview
Problem
Current wireless communication systems rely heavily on centralized infrastructure for self-optimization, leading to inefficiencies in coverage and capacity optimization, which increases costs and reduces Return on Investment (ROI) for network operators.
Innovation Solution
A distributed coverage optimization method and apparatus that enables network elements to perform self-optimization based on information exchanged with other network elements and wireless terminals, facilitating autonomous coverage and capacity optimization without extensive human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If centralized infrastructure is used for self-optimization, then network control and management is simplified, but operational costs increase and efficiency decreases
Solution Approach 1:
The patent segments the centralized optimization function into distributed optimization entities deployed across multiple network elements. Each network element runs local optimization algorithms independently, dividing the monolithic centralized system into autonomous segments that operate in parallel, thereby reducing operational costs while maintaining or improving optimization efficiency.
Solution Approach 2:
The patent transitions from a single-dimensional centralized control architecture to a multi-dimensional distributed architecture where optimization occurs across multiple network elements simultaneously. This dimensional expansion allows parallel processing of optimization tasks, improving overall productivity while reducing the burden on any single infrastructure component.
2Reliability
If centralized infrastructure is used for coverage optimization, then centralized control is maintained, but operational costs increase
Solution Approach 1:
The patent implements self-service by enabling network elements to perform coverage optimization autonomously using locally deployed optimization entities. These entities automatically collect performance data, analyze coverage conditions, and adjust parameters without requiring centralized intervention, thereby eliminating ongoing operational costs while maintaining reliable control through distributed decision-making.
Solution Approach 2:
The patent extracts the optimization functionality from the centralized infrastructure and deploys it as independent optimization entities within individual network elements. This extraction removes the continuous operational burden from the centralized system, reducing costs while preserving control reliability through the autonomous operation of distributed optimization agents.
3Manufacturing precision
If manual intervention is used for optimization, then precise control is achieved, but time consumption and costs increase
Solution Approach 1:
The patent implements continuous feedback loops where optimization entities monitor network performance metrics in real-time, automatically adjust coverage parameters based on measured conditions, and verify results. This closed-loop feedback system maintains precise control comparable to manual intervention while operating autonomously and continuously, eliminating time delays associated with human response and reducing operational costs.
Solution Approach 2:
The patent employs preliminary action by pre-configuring optimization entities with algorithms and criteria for automatic decision-making. These entities are prepared in advance to immediately respond to changing network conditions without waiting for manual analysis, achieving precise control through pre-programmed optimization logic while significantly reducing the time required for optimization actions.
Data Source
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AI summary
Generating and exchanging measurement information for coverage optimization in wireless networks like Self-Optimizing Network, SON, applying coverage and capacity optimization, CCO, algorithms. Facilitating a distributed coverage optimization versus a centralized one. A communication is established with at least one external entity, and a coverage-related measurement is received as a report from the at least one external entity (User equipment or Base Station). A coverage parameter is then self-optimized as a function of the coverage-related measurement.