Wireless Node Device Neural Network Traffic Control
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Solution Overview
Problem
Current network traffic control methods rely solely on signal strength changes and preset channels, failing to adaptively improve network conditions, especially in multi-AP networks with multiple radio frequency bands.
Innovation Solution
A wireless node device equipped with a first neural network module and control circuit that processes current state data to estimate network conditions and determine traffic control policies, allowing continuous learning and adaptation through updated training parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current network traffic control methods use only signal strength change and preset channels, then the control method is simple, but the network condition cannot be improved when preset channels are not in good condition
Solution Approach 1:
The neural network model is pre-trained with initial parameters to predict network conditions before actual traffic control decisions are made. This preliminary prediction allows the system to proactively identify optimal channels based on predicted future network states rather than reactively responding to current signal strength changes alone.
Solution Approach 2:
The system implements a feedback mechanism where current state data is continuously fed into the neural network model, and the predicted network conditions are used to adjust traffic control policies. The model parameters are updated based on feedback from actual network performance, enabling continuous improvement and adaptation to changing environmental conditions.
2Measurement precision
If neural network model with continuous learning is implemented, then the network condition prediction accuracy is improved, but the device complexity increases
Solution Approach 1:
The neural network model performs self-updates by automatically adjusting its parameters based on feedback from network performance data. The system serves itself by continuously learning from operational data without requiring manual reconfiguration or external intervention, thereby improving prediction accuracy while maintaining automated operation.
Solution Approach 2:
The neural network model is pre-trained with initial parameters before deployment. This preliminary training provides a baseline level of prediction accuracy without requiring the device to perform complex real-time learning from scratch, reducing the computational burden during actual operation while still enabling continuous improvement through parameter updates.
Data Source
AI summary
A wireless node device includes a neural network module, a wireless network communication circuit, and a control circuit. The neural network module has a plurality of trained parameters. The control circuit is coupled to the neural network module and the wireless network communication circuit. The control circuit obtains, from the wireless network communication circuit, a plurality of current state data corresponding to a plurality of time points, and loads the neural network module to obtain estimated network data based on the current state data. The control circuit controls the wireless network communication circuit according to the estimated network data.