Ideal Test Signal for ML Training
White Papers
Training and testing machine learning algorithms require massive amounts of data, formatted in an orderly manner, so the algorithm can ingest it. Wideband RF streaming solutions help to increase the diversity and fidelity of machine learning (ML) training data as they record and regenerate real-world signal environments off the air. They can also assist during the system test phase to present realistic, but controlled, RF environments to evaluate the effectiveness and efficiencies of the algorithms. This makes streaming test solutions and technologies vital to the ML workflow.
Wideband RF streaming systems can provide diverse data, with varying degrees of real-time impairment, for a realistic training and testing environment with advantages over simulated data. The best data source is captured from real-world environments, stored with high fidelity, and rendered into meta-data that is directly consumable by algorithms.
The algorithm development workflow in this paper is adapted to spectrum monitoring that explores the benefits of wideband streaming solutions and associated software. The solution’s features can record, store, process, and regenerate wideband RF environments (and individual signals) to create an effective toolbox for developing ML algorithms for various monitoring applications. We will also learn about different RF data forms and their use in the process.