Communication Controller Using Machine Learning for Link Adaptation
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Solution Overview
Problem
Traditional communication networks are prone to interruptions due to communication errors caused by disturbances such as jamming or atmospheric conditions, leading to unreliable communication.
Innovation Solution
A communication controller that utilizes a node agent with a trained machine learning model to monitor performance characteristics and local observables, controlling network connections based on a local policy matrix and machine learning models to adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional dedicated communication devices and links are used for specific communication types, then communication function is provided, but communication reliability deteriorates due to interruptions from disturbances such as jamming or atmospheric conditions
Solution Approach 1:
The patent implements a universal communication device that can handle multiple communication types (voice, video, telemetry, sensor data) through a single integrated system rather than dedicated devices for each communication type. This multi-functional approach allows the system to adapt to different communication needs and maintain reliability across various application scenarios.
Solution Approach 2:
The system dynamically adjusts communication parameters, routing decisions, and resource allocation based on real-time monitoring of performance characteristics and local observables. This dynamic behavior enables the communication system to adapt to changing conditions and disturbances, maintaining reliability through continuous optimization rather than static dedicated configurations.
2Reliability
If machine learning models and performance monitoring are integrated into the communication controller, then communication reliability improves through adaptive control, but device complexity increases
Solution Approach 1:
The communication controller incorporates machine learning models that enable self-service functionality, automatically monitoring performance characteristics, detecting anomalies, and adjusting communication parameters without external intervention. This self-service capability improves reliability while the automation actually reduces operational complexity despite the sophisticated algorithms involved.
Solution Approach 2:
The system implements continuous feedback loops where performance characteristics are monitored, analyzed by machine learning models, and used to adjust communication decisions in real-time. This feedback mechanism creates an adaptive system that improves reliability through learning from past performance while managing complexity through structured feedback processing rather than uncontrolled system growth.
3Reliability
If network resources are dynamically adjusted based on monitored performance characteristics, then communication robustness improves under disturbances, but loss of information increases due to routing decisions
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models with extensive performance data and establishing policy matrices that encode optimal routing decisions for various disturbance scenarios. This preliminary preparation enables the system to make informed routing decisions during actual disturbances, improving robustness while minimizing information loss through pre-validated decision rules.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based routing systems with machine learning-based intelligent routing. This substitution enables more sophisticated analysis of performance characteristics and local observables, allowing the system to make smarter routing decisions that improve robustness under disturbances while reducing information loss through data-driven optimization rather than simple routing rules.
Data Source
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AI summary
A communication controller, a method and a node agent is disclosed, The communication controller comprising: a data transmission port configured to be connected to corresponding logical channels; an application port configured to provide data to or from corresponding applications; a node agent having node control data comprising at least one trained machine learning model, configured to: monitor performance characteristics for data inflow to a data transmission port; monitor an at least one local observable; control the connection between the data transmission port and another data transmission port causing outflow of data, or between the data transmission port and the application port, causing inflow of data if the application port receives data, and outflow of data if the application port sends data; control the connection between the transmission ports and the application ports based on the monitored performance characteristics, the at least one local observable, the at least one trained machine learning model and a local policy matrix.