Network Node Configuration Using GAN-Based Performance Prediction
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Solution Overview
Problem
Existing methods for addressing degraded node performance in communications networks are ineffective, as they either address issues post-mortem or fail to adapt to environmental changes, leading to inefficient resource use and poor user experience.
Innovation Solution
Implement an auto-correction method using Generative Adversarial Networks (GANs) and auto-encoders to identify and adjust Configuration Management (CM) parameters, enabling nodes to adapt to changing traffic scenarios and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to address degraded node performance, then issues can be addressed after they occur, but the response time is delayed and resources are wasted
Solution Approach 1:
The system performs preliminary actions by continuously monitoring node parameters and predicting performance degradation before it occurs. The GAN-based model analyzes historical data and current trends to identify nodes that are likely to degrade, enabling proactive intervention rather than reactive response.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting performance data from nodes, comparing it against predicted values from the GAN model, and using the discrepancy to trigger corrective actions. This closed-loop feedback enables the system to adapt and respond to changing conditions in real-time.
2Adaptability or versatility
If existing methods are used to manage node performance, then configuration changes can be made, but they do not adapt to environmental changes and traffic scenarios
Solution Approach 1:
The system applies dynamics by making the configuration parameters adaptive rather than static. The GAN-based model continuously learns from changing environmental conditions and traffic patterns, dynamically adjusting node configurations to maintain optimal performance across varying scenarios.
Solution Approach 2:
The system implements parameter changes by using the GAN model to generate optimized configuration parameters based on current environmental conditions and historical performance data. These parameter adjustments enable the network to adapt to changing conditions while maintaining reliable performance.
3Measurement precision
If traditional monitoring methods are used, then node performance can be measured, but degraded nodes cannot be identified in advance for correction
Solution Approach 1:
The system performs preliminary detection by using the GAN model to predict which nodes are likely to degrade before actual performance issues manifest. This early identification allows for preventive corrective actions to be taken, reducing both detection time and the impact of performance degradation.
Data Source
AI summary
A method, performed by a first node (111), for handling parameters to configure a second node (112). The first node (111) determines (207) parameters to configure the second node (112). The determining (207) is based on an analysis by a Generative Adversarial Network comprising performing iteratively: i) generating a set of parameters estimated to be linked to a performance of a first group of nodes (121) being above a threshold, and ii) discriminating between the generated set of parameters and a first set of parameters observed to be linked to the performance of the first group of nodes (121) being above the threshold to obtain a score for every parameter. The score indicates how different the generated parameters and the first set of parameters are. The determined parameters have the score resulting from the discriminating being lower than another threshold. The first node (111) also outputs an indication comprising the determined parameters.