Neural Network Wireless Throughput Prediction
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Solution Overview
Problem
Existing technologies face challenges in remotely diagnosing the causes of low wireless link throughput in computing devices, due to the influence of network and environmental factors that are difficult to detect or analyze remotely.
Innovation Solution
A neural network training platform is used to train and continuously update a machine learning model that can model relationships between network and environmental factors and wireless link throughput. This platform gathers controlled connectivity testing metrics during laboratory testing and uses them to train a neural network, which is then deployed on information handling systems to predict throughput in real-time environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional remote diagnosis methods are used for low throughput issues, then technical support personnel can provide basic assistance, but they cannot accurately detect or analyze network and environmental factors that affect wireless link throughput
Solution Approach 1:
A machine learning model acts as an intermediary between the wireless communication system and the diagnostic analysis. The model receives multiple input parameters (signal strength, interference levels, environmental conditions) and transforms them into accurate throughput predictions, enabling remote diagnosis without requiring complex analytical tools at the technical support level.
Solution Approach 2:
The patent replaces traditional manual diagnostic methods with an automated machine learning-based prediction system. Instead of technical support personnel manually analyzing various factors, the system uses trained models to automatically predict throughput and identify issues, substituting human analytical processes with computational algorithms.
2Measurement precision
If multiple network and environmental factors are monitored to accurately predict throughput, then prediction accuracy improves, but the complexity of data collection and analysis increases
Solution Approach 1:
The system segments the complex throughput prediction problem into multiple independent input factors (signal strength, interference, environmental conditions, device parameters). Each factor is measured and processed separately, then combined by the machine learning model to produce the final prediction. This segmentation makes the detection and measurement of individual factors more manageable.
Solution Approach 2:
The machine learning model automatically collects, processes, and analyzes multiple factors without requiring manual intervention. The system self-services by gathering data from various sources, training the models, and generating predictions autonomously, reducing the difficulty of detecting and measuring multiple factors.
3Ease of operation
If machine learning models are deployed on information handling systems for real-time throughput prediction, then remote diagnosis capability is enhanced, but the computational resources required increase
Solution Approach 1:
The machine learning models are trained in advance using historical data and laboratory measurements before being deployed for real-time prediction. This preliminary training action allows the models to be stored as pre-computed solutions that can be executed efficiently during operation, reducing the computational energy required during actual throughput prediction tasks.
Data Source
AI summary
An information handling system executing an intelligent throughput performance analysis and issue detection system may comprise a network interface device to establish a wireless link with a wireless network and a processor to execute a neural network trained to predict wireless link throughput values based on controlled connectivity testing metrics gathered in a controlled laboratory from tested information handling systems. The processor may gather measured throughput of the wireless link and operational connectivity metrics for the information handling system that describe antenna positional information, antenna adaptation controller parameters, signal strength measurements, and wireless link performance metrics. The neural network may output, based on the gathered operational connectivity metrics a predicted throughput value that differs from the measured throughput by a maximum tolerance. The network interface device may transmit a notification of erroneously predicted throughput, the neural network output, and the operational connectivity metrics to a remote neural network training platform for retraining.