Base Station Controller Capacity Planning Optimization
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Solution Overview
Problem
Current capacity planning for base station controllers in wireless networks is labor-intensive and non-optimal, requiring manual manipulation of various parameters and failing to efficiently consider multiple factors such as load balancing, capital costs, and user behavior, which is particularly challenging for large networks.
Innovation Solution
A method and apparatus for planning base station controllers that determines optimal parameters based on input data and penalty factors, using an objective function that balances load balancing, handover traffic, geographical clustering, and budgetary limitations, with the aid of linear programming to minimize penalties and optimize network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual capacity planning is used, then planner expertise and control are maintained, but labor intensity increases and optimization quality deteriorates
Solution Approach 1:
The patent replaces manual mechanical planning processes with an automated computer-based optimization system. The system uses mathematical models and algorithms to automatically determine optimal base station controller configurations, substituting human expertise with computational intelligence that can process complex constraints and objectives simultaneously.
Solution Approach 2:
The system transforms discrete manual parameter adjustments into continuous optimization problems. By formulating capacity planning as a mathematical optimization problem with objective functions and constraints, the system can automatically adjust multiple parameters (deployment locations, capacity levels, timing) to achieve optimal solutions that would be impractical to find manually.
2Adaptability or versatility
If manual parameter manipulation is used, then control over network configuration is maintained, but consideration of multiple factors becomes inefficient
Solution Approach 1:
The optimization system is designed to handle multiple planning factors simultaneously through a unified mathematical framework. The system can incorporate diverse objectives (load balancing, capital costs, service quality) and constraints (network topology, deployment budgets, technical requirements) into a single multi-functional optimization problem, eliminating the need for separate analysis procedures for each factor.
Solution Approach 2:
The patent introduces mathematical models and algorithms as intermediary elements between the planner's objectives and the network configuration. These intermediaries translate complex qualitative requirements into quantitative constraints and objective functions, enabling systematic consideration of multiple factors without direct manual manipulation of each individual parameter.
3Productivity
If automated optimization is implemented, then planning efficiency improves, but system complexity increases
Solution Approach 1:
The optimization system is segmented into distinct functional modules: data collection components, mathematical modeling layers, optimization algorithm engines, and result visualization interfaces. This segmentation allows each component to be developed, tested, and maintained independently, reducing the perceived complexity while preserving overall system capability.
Solution Approach 2:
The system uses simplified mathematical representations (models) of the complex network to perform optimization. Instead of manipulating the full network complexity directly, the system creates abstracted copies of network characteristics in the form of mathematical variables and constraints, making the optimization process more manageable while capturing essential network behaviors.
Data Source
AI summary
A method and apparatus for providing planning of a plurality of base station controllers in a wireless network are disclosed. For example, the method obtains input data, and determines a limit for at least one base station controller parameter in accordance with the input data. The method determines if the limit for the at least one base station controller parameter is exceeded and determines an optimal output for an objective function, wherein the objective function is based on a plurality of penalty factors, if the limit for the at least one base station controller parameter is exceeded.