Telecom Site Decommissioning Analysis Using Virtual Simulation
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Solution Overview
Problem
Current techniques for determining the impact of decommissioning a site in a telecommunications network on user experience are inefficient and ineffective due to the large amounts of data involved, requiring significant computer and human resources to analyze and determine necessary actions for maintaining service quality.
Innovation Solution
An analysis system processes capacity and coverage analysis data to calculate key performance indicators (KPIs) and determine actions to maintain or improve network experience by analyzing site data, handover data, and traffic data, allowing for efficient decision-making on site decommissioning and network modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual analysis methods are used to evaluate decommissioning impact, then analysis thoroughness may be maintained, but computational resources and time requirements become excessively large
Solution Approach 1:
The system creates virtual copies of network sites and user equipment to simulate decommissioning scenarios. By analyzing these virtual models rather than actual network data, the system achieves thorough analysis of potential impacts without requiring extensive manual computation or risking actual network disruption. The virtual environment allows comprehensive evaluation of KPI changes, coverage impacts, and capacity effects in parallel.
Solution Approach 2:
The system performs preliminary analysis of decommissioning impacts before actual decommissioning decisions are made. By pre-calculating KPI changes, coverage modifications, and capacity effects using virtual simulations, the system provides thorough impact assessment in advance, allowing network operators to make informed decisions without time-consuming manual analysis during the actual decommissioning process.
2Measurement precision
If comprehensive network data is collected for accurate decommissioning analysis, then analysis accuracy is improved, but data processing complexity and resource requirements increase
Solution Approach 1:
The system introduces virtual site models and virtual user equipment as intermediaries between raw network data and impact analysis. These virtual models serve as simplified representations that capture essential network characteristics while reducing data complexity. By processing virtual models rather than raw network data, the system maintains analysis accuracy while significantly reducing computational complexity and resource requirements.
Solution Approach 2:
The system transforms comprehensive network data into simplified virtual models by selecting and representing only the essential parameters needed for decommissioning analysis. This parameter transformation maintains the accuracy needed for KPI evaluation, coverage analysis, and capacity assessment while reducing the overall data complexity and making processing more efficient.
3Reliability
If manual evaluation of decommissioning impact is performed, then detailed analysis can be conducted, but human resources and operational costs increase
Solution Approach 1:
The system enables automated self-service analysis of decommissioning impacts by using virtual simulations to automatically evaluate KPI changes, coverage effects, and capacity impacts. The virtual models independently assess potential decommissioning scenarios without requiring human analysts to manually process data or interpret results, thereby maintaining reliable analysis while dramatically improving operational efficiency and reducing human resource requirements.
Solution Approach 2:
The system provides automated feedback on decommissioning impacts by analyzing virtual models and generating results on KPI changes, coverage modifications, and capacity effects. This automated feedback loop maintains analysis reliability through consistent evaluation criteria while improving operational efficiency by eliminating manual review processes and enabling faster decision-making.
Data Source
AI summary
A device may receive site data identifying a site associated with a telecommunications network. The device may determine handover data identifying handovers of user equipment associated with the site. The device may receive traffic data identifying traffic generated by the user equipment associated with the site. The device may calculate, based on the site data, the handover data, and the traffic data, a plurality of key performance indicators (KPIs) for the site, for a predefined time period, and if the site is decommissioned, wherein the plurality of KPIs include one or more of: resource block utilization data, determining KPI satisfaction data identifying whether one or more of the plurality of KPIs satisfy one or more of a plurality of KPI thresholds, and performing one or more actions based on the KPI satisfaction data.