Telecommunications Network Capacity Simulation Platform
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Solution Overview
Problem
Current methods for predicting telecommunications network traffic demand in urban areas are inaccurate due to reliance on historical data that lacks context, leading to inefficiencies in resource allocation and poor user experience, especially with the introduction of 5G systems which accommodate varying bandwidth and latency requirements.
Innovation Solution
A predictive simulation and optimization platform that uses machine learning techniques, combining population data, location-specific information, and external data sources to forecast telecommunications network traffic, identifying potential infrastructure failures and recommending resource adjustments to ensure minimum service levels are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical data alone is used for traffic demand prediction, then the prediction method is simple, but the prediction accuracy is low
Solution Approach 1:
The patent combines multiple data sources (historical traffic data, population data, event data, weather data) and multiple prediction models into a unified predictive simulation platform. This merging of diverse inputs resolves the contradiction by achieving high prediction accuracy through comprehensive data integration while managing complexity through a structured system architecture.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that process and synthesize data from multiple sources. These intermediary models transform raw data into predictive insights, enabling accurate predictions while abstracting the complexity of data processing from the user interface.
2Reliability
If more network infrastructure elements are deployed to meet traffic demand, then service reliability is improved, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent performs predictive simulation before actual events occur to identify potential infrastructure failures and optimize resource allocation in advance. By conducting preliminary analysis of traffic patterns and infrastructure performance, the system determines optimal resource distribution before demand spikes, ensuring reliability without wasteful over-provisioning.
Solution Approach 2:
The patent implements dynamic resource allocation that adjusts network infrastructure deployment based on predicted traffic patterns. Rather than static over-provisioning, the system dynamically optimizes resource distribution to match actual demand, maintaining service reliability while improving resource allocation efficiency through adaptive management.
3Reliability
If network resources are increased to handle traffic spikes, then user experience is improved, but energy consumption increases
Solution Approach 1:
The patent uses predictive simulation to identify upcoming traffic spikes before they occur, allowing operators to proactively allocate resources only when and where needed. This preliminary prediction capability ensures high user experience quality during traffic spikes while avoiding continuous energy consumption from permanently over-provisioned infrastructure.
Solution Approach 2:
The patent changes the operational parameters of network infrastructure based on predicted traffic conditions. By adjusting resource allocation parameters dynamically according to predictions rather than maintaining fixed high-resource states, the system maintains user experience quality during critical periods while reducing energy consumption during normal periods.
Data Source
AI summary
A method includes receiving a representation of a predefined planned event that includes the use of a first set of cellular data service infrastructure elements. A performance of the first set of cellular data service infrastructure elements is simulated, and a predicted failure of at least one cellular data service infrastructure element from the first set of cellular data service infrastructure elements is identified based on the simulation. In response to identifying the predicted failure, a modification to the at least one cellular data service infrastructure element or an additional cellular data service infrastructure element is determined and included in a second set of cellular data service infrastructure elements whose performance is subsequently simulated. The simulated performance of the first set of cellular data service infrastructure elements is compared with the simulated performance of the second set of cellular data service infrastructure elements to determine a performance improvement.