Signal Classification via Latent Space Projection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current signal classification systems face challenges in accurately classifying RF signals, especially in environments with multiple wireless signals and dynamic characteristics, due to limitations in feature extraction and robustness against evolving threats.
Innovation Solution
A signal classification system utilizing a generative adversarial network architecture that projects RF data into a latent space learned by a document embedding model, enabling text-based descriptions of input signals and improving classification accuracy through a convolutional generator network, discriminator network, and classifier network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional signal classification systems are used, then the system structure is relatively simple, but the classification accuracy and robustness deteriorate in environments with multiple wireless signals and dynamic characteristics
Solution Approach 1:
The system segments the signal classification task into multiple specialized components: a convolutional generator network for feature extraction, a discriminator network for validation, and a classifier network for final classification. Each component focuses on a specific aspect of signal analysis, improving overall accuracy while managing complexity through functional division.
Solution Approach 2:
The patent introduces an intermediary latent space that transforms raw signal data into a standardized representation format. This latent space acts as a mediator between the input signals and the classification networks, enabling more accurate and robust classification by preprocessing and normalizing the data before it reaches the classifier.
2Adaptability or versatility
If traditional feature extraction methods are used, then the processing speed is faster, but the ability to detect and classify novel signals with dynamic characteristics deteriorates
Solution Approach 1:
The system performs preliminary action by pre-training the convolutional generator network on a diverse set of signal types and modulation schemes before actual classification. This pre-training establishes robust feature extraction capabilities that can generalize to novel signals, reducing the need for extensive real-time processing and adaptation.
Solution Approach 2:
The patent implements dynamics by designing the neural networks with adaptive learning capabilities that can adjust to new signal types and modulation schemes. The system continuously learns from incoming data, updating its internal representations to maintain high detection accuracy for evolving and novel signals without requiring complete retraining.
3Extent of automation
If manual signal analysis methods are used, then the system requires less computational resources, but the level of human intervention and expertise required increases
Solution Approach 1:
The system implements self-service through automated feature extraction and classification using the convolutional generator and classifier networks. The model autonomously processes signals, identifies modulation types, and detects novel waveforms without requiring manual feature engineering or expert intervention, thereby increasing automation while managing computational resources efficiently through optimized network architectures.
Data Source
AI summary
Classification of signals using machine learning and related systems, methods and computer-readable media are disclosed. A signal classification system includes a sentence embedding model network, a convolutional generator network, and a classifier network. The sentence embedding model network is trained to convert a body of sentences correlated to different signal modulation schemes into a latent space. The convolutional generator network is configured to project samples of a measured signal into the latent space. The classifier network is configured to classify the measured signal from the latent space responsive to a projection of the samples of the measured signal into the latent space. A method includes training a sentence embedding model network to convert descriptive sentences to a latent space, the descriptive sentences correlated to different signal modulation schemes. The method also includes training a convolutional generator network to project samples of a measured signal into the latent space.