PAGE CONTENTS
Objectives
The objective of this project is to create a solution for detecting RF impairments with machine learning, integrated into a passive RF receiver system, and achieve TRL6 for this scope. The project was broken down into four categories of problems
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Transmitter impairment recognition
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Event and scenario recognition
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Channel condition recognition
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Specific emitter identification.
Challenges
Key challenges included:
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The design and development of realistic datasets for the project problems,
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Selecting appropriate algorithmic solutions that would be relevant for the space environment,
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Building a testbed for dataset creation as well as classifying and quantifying impairments in real-time
Technical challenges include handling of large datasets, automated training, orchestrating multiple training targets, and implementing the model in an inference solution that is suitable for a range of commercially available software radio technologies.
System Architecture
The technology was built around low parameter count models (<1M) trained with a combination of synthetic and testbed-generated data. The data was produced with RIA tools for procedural recording generation and testbed control.
Models are loaded into an inference application that is cross platform, having been tested on a range of hardware targets, software-defined radios, and operating systems.
Testbeds included a combination of transmitters and receivers orchestrated to produce desired signal conditions for model training.
Plan
The project plan included work packages for (1) Developing project tooling such as development of signal generation and synthesis software (2) Recording capture and synthesis, then curation into datasets (3) Model training. (4) Model training and testing in software environments. (5) Model training and testing in an over-the-air capture environment, (6) Adapting the previous works to a new challenge (RF fingerprinting) and (7) integrating the findings into Qoherent’s RIA platform.
Current Status
The project was completed in 2024 and successfully created machine learning models and the software to produce them for all 4 impairment categories, including reference workflows for each that can be used in all future Qoherent products and services.
During the project Qoherent produced:
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New implementations of ML signal classifiers and regressors were developed. A new flagship model architecture was identified, suitable for space applications, which is being used for most Qoherent space-related activities.
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In total, 30 models and corresponding datasets were developed over hundreds of training runs across all 4 categories.
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Datasets included training data, challenge data, and unseen data.
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Qoherent filed 4 provisional patent applications or Patent Cooperation Treaty Applications. The project contributed heavily to these.
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