GPRS Support Node Capacity Planning Optimization
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Solution Overview
Problem
Manual capacity planning in wireless networks is labor-intensive and non-optimal, particularly for large networks, as it involves balancing multiple factors such as load balancing, capital costs, and user behavior, without a systematic approach to optimize network performance.
Innovation Solution
A method and apparatus for planning serving General Packet Radio Service (GPRS) support nodes in wireless networks that determine optimal parameters based on input data and penalty factors, using an objective function to minimize penalties related to load balancing, handover traffic, and capital costs, while considering geographical clustering and user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual capacity planning is used to manage network elements, then flexibility in adjusting parameters is maintained, but labor intensity increases and planning optimality deteriorates
Solution Approach 1:
The system performs self-service by automatically gathering network data, evaluating multiple factors (load balancing, capital cost, user behavior), and generating capacity planning recommendations without requiring manual intervention. The processor autonomously executes the capacity planning algorithm and presents optimized results.
Solution Approach 2:
The manual mechanical process of parameter adjustment is replaced with an automated computational system. The processor substitutes human planners by executing algorithms that evaluate network data and generate planning recommendations, transforming a labor-intensive manual process into an automated electronic system.
2Manufacturing precision
If manual capacity planning is used for large networks, then detailed parameter control is possible, but the planning becomes highly non-optimal and time-consuming
Solution Approach 1:
The system enables continuous capacity planning by automatically and continuously gathering network data from multiple sources, evaluating factors without interruption, and generating updated planning recommendations. This continuous automated process eliminates the time loss associated with periodic manual planning cycles.
Solution Approach 2:
The time-consuming manual evaluation of multiple factors is replaced by an automated computational system that simultaneously processes network data, load balancing requirements, capital cost constraints, and user behavior patterns, delivering optimized planning results in minimal time.
3Reliability
If multiple factors are considered in capacity planning, then comprehensive optimization is achieved, but the complexity of the planning process increases
Solution Approach 1:
The system performs multiple functions within a single integrated process: gathering network data, evaluating load balancing requirements, assessing capital cost constraints, analyzing user behavior patterns, and generating capacity planning recommendations. This multi-functional approach achieves comprehensive optimization without increasing apparent process complexity.
Solution Approach 2:
The processor acts as an intermediary that receives multiple input factors (network data, load balancing requirements, capital cost constraints, user behavior), processes them through a unified algorithm, and produces integrated planning recommendations. This intermediary approach manages complexity by centralizing the evaluation of multiple factors through a single computational system.
Data Source
AI summary
A method and apparatus for providing planning of a plurality of serving general packet radio service support nodes in a wireless network are disclosed. For example, the method obtains input data, and determines a limit for at least one serving general packet radio service support node parameter in accordance with the input data. The method determines if the limit for the at least one serving general packet radio service support node 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 serving general packet radio service support node parameter is exceeded.