RF Scene Generation for Dynamic Spectrum Testing

The RF environment is a harsh and highly dynamic environment that includes both congested and contested spectrum. Typical testing lifecycles include initial simulations, then laboratory testing in simulated noise environments with a few channel impairments, followed by outdoor testing in unpredictable RF environments under challenging unknown channel characteristics and unintended interference sources. The large leap from predictable laboratory testing to unpredictable Over-the-Air (OTA) testing makes it difficult to evaluate and quantify the actual performance. The use of emulated RF environments or RF scenes provides greater insight into system performance in challenging, and dynamic environments, while also enabling agile iterative performance improvements to fine tune the system. This paper describes the creation of realistic RF scenes to emulate RF environments that include a variety of narrowband and wideband signals, and random traffic patterns. We will also describe a RF learning approach that learns the spectral and temporal characteristics directly from actual RF environments, enabling the creation of RF scenes that mimic the actual RF environment enabling cost-effective repeatable test scenarios with the same complexity and rich expressiveness of actual operational RF environments. RF learning also enables the cost-effective generation of the massive RF data sets required to train, validate, and test new innovative RF Machine Learning (ML) algorithms.