ML-Based Message Detection in Communication Controllers
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Solution Overview
Problem
Existing communication systems face challenges in reliably transmitting and receiving data, especially in environments where channel properties are unknown or variable, leading to inefficiencies in data throughput and reliability.
Innovation Solution
The implementation of a communication system controller that uses machine learning classification to extract features from transmission signals, classify them, and detect messages, with the ability to retrain the classifier based on detected messages and incorrect classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional communication systems are used with known channel properties, then signal modulation and receiver configuration can be optimized, but the system cannot adapt to unknown or variable channel conditions
Solution Approach 1:
The communication system performs self-learning by automatically extracting features from received signals and training machine learning classifiers to detect messages. The system serves itself by continuously improving its own performance through feedback loops where detection results are used to retrain the classifier, eliminating the need for external intervention to adapt to channel conditions.
Solution Approach 2:
The system implements feedback mechanisms where the results of message detection are fed back into the training process. The classifier is retrained using newly detected messages and classification results, creating a continuous improvement loop that adapts the system's performance to current channel conditions without requiring manual reconfiguration.
2Measurement precision
If machine learning classification is implemented to detect messages, then message detection accuracy improves over time, but system complexity increases
Solution Approach 1:
The system performs preliminary feature extraction from received signals before classification. By pre-processing the signals to extract relevant features, the complex task of message detection is broken down into manageable stages, making the overall system more manageable while maintaining high detection accuracy.
Solution Approach 2:
The patent replaces traditional mechanical signal processing methods with machine learning-based classification systems. This substitution allows for more sophisticated pattern recognition and message detection that cannot be achieved with conventional approaches, significantly improving detection accuracy despite increased computational complexity.
3Adaptability or versatility
If the machine learning classifier is continuously retrained, then adaptation to changing channels is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs partial retraining by selectively updating the classifier with new data rather than complete retraining from scratch. This partial action approach allows the system to adapt to changing conditions while minimizing the computational time and resources required, striking a balance between adaptability and processing efficiency.
Solution Approach 2:
The training process is made dynamic and adaptive, where the frequency and extent of retraining are adjusted based on actual channel conditions and system performance. The system can switch between frequent small updates and less frequent comprehensive training, optimizing the balance between adaptability and processing time consumption.
Data Source
AI summary
Systems and methods detect messages in transmitted and received data in communication systems in accordance with embodiments of the invention. In one embodiment, a communication system controller includes a processor, a memory, and a receiver, wherein the processor obtains a transmission signal using the receiver, extracts features in the transmission signal, and detects a message in the transmission signal based on the extracted features using a machine learning classifier.