Cell Area Resource Reallocation Using Network Performance Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for resource allocation in telecommunications networks rely heavily on engineer intuition and heuristics, which are inconsistent and difficult to verify, especially in complex and multivariate environments where subscriber demand varies unpredictably.
Innovation Solution
Utilizing network performance models that incorporate composite metrics and machine learning algorithms to assign scores based on user experience resources and cell site parameters, enabling data-driven resource reallocation decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resource allocation decisions are made based on engineer intuition and heuristics, then resource allocation can be performed without complex systems, but the decisions are inconsistent and difficult to verify
Solution Approach 1:
The patent replaces the mechanical system of human engineer intuition and heuristic judgment with a computational model-based system. The network performance model automatically evaluates resource allocation decisions using standardized metrics and algorithms, eliminating subjectivity and inconsistency while providing verifiable, data-driven recommendations for resource management in telecommunications networks
Solution Approach 2:
The patent transforms qualitative engineer intuition into quantitative parameters by defining specific performance metrics (user experience resources, cell site parameters, composite scores). These parameter changes enable objective comparison and verification of resource allocation decisions across different network scenarios, replacing subjective judgment with measurable criteria
2Measurement precision
If network performance models use composite metrics and machine learning algorithms to evaluate resource allocation, then decision accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the complex evaluation process into distinct components: user experience resources (total connections, average speed, average payload), cell site parameters (power, antenna tilt, beam shape), and performance scores. This segmentation allows the system to manage computational complexity by processing individual metrics separately while maintaining high measurement precision through comprehensive evaluation of all components
Solution Approach 2:
The network performance model serves multiple functions simultaneously: it evaluates current network performance, predicts outcomes of resource allocation changes, identifies underperforming and overperforming cell areas, and generates actionable recommendations. This multi-functionality justifies the computational complexity by providing comprehensive resource allocation optimization through a single integrated system
3Reliability
If resource reallocation is implemented based on performance model recommendations, then user experience in underperforming areas improves, but resource availability in donor areas may be reduced
Solution Approach 1:
The patent implements dynamic resource reallocation where resources are continuously adjusted based on real-time network performance conditions. The system identifies underperforming cell areas and reallocates resources from overperforming donor areas, creating a dynamic equilibrium that adapts to changing network demands. This dynamic approach ensures that user experience quality improves in recipient areas while donor areas maintain sufficient resources through continuous monitoring and adjustment
Solution Approach 2:
The network performance model incorporates feedback mechanisms by continuously evaluating the impact of resource reallocation on both recipient and donor cell areas. The system monitors performance metrics before and after reallocation, using this feedback to refine future resource allocation decisions. This feedback loop ensures that resource transfers improve recipient areas without critically impacting donor area service quality
Data Source
AI summary
The technology includes a system to reallocate user experience resources. The system detects network performance in a set of cell areas. The system determines low-performing cell areas and high-performing cell areas by using a network performance model. The system determines common subscribers between the low-performing cell areas and the high-performing cell areas. Based on the common subscribers, the system identifies a recipient cell area and a donor cell area. The system determines a reallocation of user experience resources by using the network performance model to determine scores based on changes to cell site parameters of the donor cell area and the recipient cell area. The system generates an indication that the reallocation will improve user experiences for subscribers of the recipient cell area, without diminishing user experiences for subscribers of the donor cell area.