Neural Network Decoder for Synthetic RF Data Generation
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Solution Overview
Problem
Existing methods for testing new measurement and communication equipment are limited by the availability and confidentiality of real-world data, making it difficult to generate sufficient synthetic data for non-standard scenarios, especially in wireless communication technologies.
Innovation Solution
A system and method using trainable neural network encoders and decoders, specifically variational autoencoders, to generate synthetic digital data that resembles real-world measurements, allowing for a significant multiplication of data from a limited set, while ensuring confidentiality by not replicating real data exactly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world measurement data is used for testing, then the authenticity and reliability of test results are improved, but the availability and quantity of test data are limited due to confidentiality and scarcity of real scenarios
Solution Approach 1:
The patent creates synthetic copies of real measurement data through neural network generation. The system learns the statistical characteristics and patterns from limited real data, then generates multiple synthetic copies that preserve these characteristics while being distinct from the originals, thereby multiplying the available test data without requiring additional real measurements
Solution Approach 2:
The neural network models learn and manipulate the statistical parameters and distributions of the measurement data. By transforming and recombining these parameters, the system generates synthetic data with varied characteristics that still reflect the underlying physical phenomena, enabling diverse test scenarios from limited source data
2Adaptability or versatility
If real measurement data is used for testing, then the representativeness of test scenarios is improved, but the confidentiality and security requirements worsen due to exposure of sensitive information
Solution Approach 1:
The system creates synthetic copies that replicate the statistical and structural properties of confidential real data without containing the actual sensitive information. These synthetic copies can be freely distributed and used for testing purposes while maintaining the confidentiality of the source data, as they are generated representations rather than the originals
Solution Approach 2:
The neural network models act as intermediaries between the confidential real data and the testing processes. The models learn from the real data during training, then generate synthetic data that serves as a safe intermediary for testing, eliminating the need to directly expose or transmit the confidential source data
3Adaptability or versatility
If more test data is generated to cover non-standard scenarios, then the coverage and comprehensiveness of testing are improved, but the complexity of data generation and processing increases
Solution Approach 1:
The neural network models are self-training systems that automatically learn from the provided real measurement data without requiring manual annotation or extensive configuration. The models self-adjust their parameters and structures to capture the essential characteristics of the data, reducing the complexity of setup and maintenance while enabling generation of diverse synthetic test scenarios
Data Source
AI summary
The invention relates to a system for generating synthetic digital data, comprising a receiver configured to receive a measured signal, in particular an RF signal, a converter configured to convert the measured signal to digital data representing signal characteristics of the measured signal, a trainable neural network encoder and a trainable neural network decoder, wherein, during a training routine, the neural network encoder is configured to receive the digital data and to generate a compressed representation of the digital data, and the neural network decoder is configured to generate a reconstruction of the digital data based on the compressed representation, and wherein the trained neural network decoder is configured to receive random or pseudorandom data and to generate synthetic digital data representing measured signal characteristics based on the random or pseudorandom data.