ML Model Predicting 5G Network Throughput and Latency
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Solution Overview
Problem
Existing methods struggle to accurately predict throughput and latency in wireless telecommunication networks, especially for immature networks, due to complex dependencies and limited reliable data, leading to inefficiencies in network optimization and resource management.
Innovation Solution
A system utilizing a machine learning model that combines key performance indicators and configuration parameters from mature and immature networks, with an input equalizer to adjust for performance differences, predicts throughput and latency by training on multidimensional data directly from the network, improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction methods are used for immature wireless networks, then the network can operate with limited data, but prediction accuracy deteriorates due to complex dependencies and insufficient reliable data
Solution Approach 1:
The patent introduces an intermediary processing system that combines data from multiple sources (mature networks, immature networks, simulations) and uses machine learning models to bridge the gap between limited immature network data and accurate predictions. The system acts as a mediator that transforms unreliable limited data into reliable predictions through ensemble methods and data fusion techniques.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models on abundant data from mature networks before deploying them to immature networks. This preliminary training establishes a foundation of reliable patterns that can then be adapted to immature network conditions, allowing accurate predictions even when direct measurement data is scarce or unreliable.
2Measurement precision
If more data is collected from immature networks to improve prediction accuracy, then prediction quality may improve, but the complexity of data collection and processing increases
Solution Approach 1:
The patent creates a universal prediction system that can handle multiple data sources (mature networks, immature networks, simulation data) and multiple prediction targets (throughput, latency, other KPIs) through a single machine learning framework. This multi-functional approach avoids the need for separate complex systems for each data source or prediction type, reducing overall system complexity while maintaining high accuracy.
Solution Approach 2:
The system dynamically adjusts parameters such as model architecture, data sampling rates, and feature selection based on the specific conditions of the immature network being analyzed. This adaptive parameter adjustment allows the system to optimize prediction accuracy for each specific scenario without requiring a completely different complex system for each case.
3Measurement precision
If comprehensive multidimensional data is used for prediction, then prediction accuracy improves, but the computational requirements and processing time increase
Solution Approach 1:
The patent segments the comprehensive multidimensional data into distinct feature categories (network configuration parameters, performance metrics, environmental factors) and processes them through specialized sub-models before combining results. This segmentation allows parallel processing of different data types, reducing overall computation time while maintaining the benefits of comprehensive analysis.
Solution Approach 2:
The system implements partial action by selectively processing only the most relevant features and data dimensions for each specific prediction task, rather than always processing all available data. This approach achieves sufficient prediction accuracy with reduced computational overhead, trading off minimal precision for significant time savings in many practical scenarios.
Data Source
AI summary
The system obtains multiple KPIs and multiple configuration parameters directly from a wireless telecommunication network. The multiple KPIs indicate an observed performance associated with the wireless telecommunication network. The multiple configuration parameters indicate a configuration of the wireless telecommunication network. The system predicts a value of a difficult to predict attribute of the wireless telecommunication network, where the difficult to predict attribute depends on multiple other attributes associated with the wireless telecommunication network. To make the prediction, the system provides the multiple key performance indicators and the multiple configuration parameters to a machine learning model. The machine learning model predicts the value of the difficult to predict attribute associated with the wireless telecommunication network based on multiple key performance indicators and the multiple configuration parameters.