Self-Organizing Network Exclusion Processing for Resource Optimization
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Solution Overview
Problem
Self-Organizing Network (SON) operations, such as Mobility Robustness Optimization (MRO) and Coverage and Capacity Optimization (CCO), often result in insufficient performance improvement, leading to resource wastage and competition for configuration parameters, where one operation's changes negatively impact another's performance indices.
Innovation Solution
An apparatus and method that includes an exclusion processing unit to assess the performance improvement of SON operations and exclude cells or neighboring cell pairs from future executions if predetermined optimization objectives are not met, using an exclusion list to prevent resource wastage and interference between competing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SON operations are executed on all cells and neighboring cell pairs, then optimization coverage is improved, but resource wastage increases due to insufficient performance improvement on some cells
Solution Approach 1:
The patent applies local quality by differentiating treatment between cells based on their individual characteristics. Cells are categorized into those that benefit from SON operations and those that do not, based on local performance metrics and improvement potential. This allows optimization resources to be concentrated on cells where they will be effective, rather than uniformly applying SON operations across all cells.
Solution Approach 2:
The patent implements partial action by selectively executing SON operations only on a subset of cells and neighboring cell pairs that are expected to benefit. The exclusion list mechanism enables the system to perform partial optimization coverage, focusing computational resources on cells where SON operations will provide meaningful performance improvement while skipping those where they would be wasteful.
2Adaptability or versatility
If multiple SON operations adjust the same configuration parameter, then different optimization objectives can be achieved, but competition between operations occurs leading to sequential execution
Solution Approach 1:
The patent implements feedback mechanisms where SON operations monitor the impact of their parameter adjustments on overall network performance and on other concurrent operations. When a configuration parameter is adjusted by one SON operation, feedback is provided to other operations using the same parameter, allowing them to adapt their adjustments or timing to avoid conflict. This enables coordinated execution of multiple SON operations with different objectives.
Solution Approach 2:
The patent applies dynamics by making the execution timing and parameter adjustment strategies of SON operations adaptive rather than fixed. Operations can dynamically adjust their behavior based on the state of other operations and the current network conditions. This dynamic coordination allows multiple SON operations to execute more concurrently and efficiently, rather than being forced into strict sequential execution.
3Reliability
If a SON operation changes a configuration parameter to improve one performance index, then that performance index is improved, but another performance index controlled by a different SON operation may be negatively impacted
Solution Approach 1:
The patent applies preliminary anti-action by having SON operations anticipate potential negative impacts on other performance indices before making parameter adjustments. The system evaluates whether a proposed parameter change might adversely affect other operations' objectives, and if so, takes preventive action by adjusting the parameter change strategy, timing, or magnitude. This preliminary assessment prevents harmful interactions between competing SON operations.
Solution Approach 2:
The patent implements feedback loops that monitor the impact of parameter changes on multiple performance indices simultaneously. When a SON operation adjusts a configuration parameter, the system monitors both the intended performance improvement and any unintended negative effects on other performance indices. This feedback information is used to adjust subsequent operations or to compensate for negative impacts, ensuring overall network performance is optimized rather than just individual metrics.
Data Source
AI summary
An apparatus (10) used in a Self-Organizing Network (SON) includes a SON execution unit (101) and an exclusion processing unit (105). The SON execution unit (101) executes a SON operation on a first cell (40), a second cell (41) or a neighboring cell pair (40 and 41), the SON operation including repeatedly adjusting a configuration parameter that affects an operation of a base station (20) or a mobile station (30) to achieve an optimization objective. The exclusion processing unit (105) excludes the first cell (40), the second cell (41) or the neighboring cell pair (40 and 41) from a future SON operation by the SON execution unit (101), if achievement status of the optimization objective after completion of the SON operation by the SON execution unit (101) does not satisfy a predetermined reference level. This can contribute to suppression of execution of SON operation providing only little performance improvement.