Neighbor Cell Ranking for Reliable Handover Decisions
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Solution Overview
Problem
Existing systems for identifying high-ranking neighbor cells in telecommunications networks often fail to consider crucial factors like interference, signal quality, and network load, leading to suboptimal handover decisions that result in call drops and degraded service quality.
Innovation Solution
A system and method that collect and analyze multiple parameters such as signal strength, interference levels, data throughput, call drop rates, and capacity over predefined periods, calculating a percentage of handover share to identify high-ranking neighbor cells using a combination of algorithms and real-time network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing systems use simple signal strength-based methods to identify neighbor cells, then the device complexity is reduced, but the handover reliability deteriorates due to suboptimal handover decisions
Solution Approach 1:
The patent transforms the handover decision process by changing from a single parameter (signal strength) to multiple parameters including signal strength, interference levels, signal quality metrics, and network load conditions. This multi-parameter approach enables more accurate identification of high-ranking neighbor cells, improving handover reliability while maintaining manageable system complexity through structured evaluation algorithms.
2Reliability
If multiple parameters are collected and analyzed to identify high-ranking neighbor cells, then the handover reliability is improved, but the measurement precision requirements increase
Solution Approach 1:
The patent introduces intermediary processing layers including data collection modules, data processing modules, and evaluation algorithms that act as mediators between raw measurements and handover decisions. These intermediaries aggregate and analyze multiple parameters (signal strength, interference, quality metrics, load conditions) to produce a comprehensive handover recommendation, reducing the precision burden on individual measurements while improving overall reliability.
3Reliability
If comprehensive data collection and analysis is performed for handover decisions, then the handover reliability improves, but the processing time increases
Solution Approach 1:
The patent implements preliminary action by continuously collecting and pre-processing network data in the background before handover events occur. The system maintains up-to-date information about signal strength, interference levels, signal quality, and network load conditions, so when a handover decision is needed, the evaluation algorithms can quickly process pre-prepared data rather than gathering everything from scratch, thus reducing processing time while maintaining reliability.
4Device complexity
If simple handover algorithms are used, then the device complexity is reduced, but the productivity of network optimization is worsened
Solution Approach 1:
The patent segments the handover decision process into distinct functional modules: data collection module, data processing module, evaluation algorithm module, and decision output module. Each module handles a specific aspect of the optimization process, making the overall system more manageable despite its complexity. This segmentation enables parallel processing and independent optimization of each component, improving network optimization productivity while keeping individual module complexities low.
Data Source
AI summary
The system (100-2) and method for identification of high ranked neighbor cells in a telecommunications network provide an efficient and accurate approach to selecting neighboring cells with superior performance for handover purposes. The system (100-2) leverages performance metrics, algorithms, and dynamic adaptation techniques to determine the suitability and ranking of potential neighbor cells. By considering factors such as signal strength, signal quality, interference levels, load balancing requirements, and operator-defined policies, the system (100-2) evaluates the performance of neighbor cells and assigns them scores or rankings to identify the higher ranked neighbor cells. The system's dynamic adaptation ensures that the rankings remain up to date and responsive to changing network conditions. The present system (100-2) and method optimize handover decisions, enhance network performance, and provide users with seamless connectivity and improved quality of service.