The Data-Driven Radio Frequency Signal Separation Challenge

The radio-frequency (RF) signal separation challenge involves recovering a signal-of-interest (SOI) from a super-imposed co-channel interference signal. The SOI is a digital communication waveform of known modulation, pulse-shape, timing, etc. The interferer is unknown and must be learned from data. Submissions featured a blend of signal processing strategies, leveraging RF-specific domain knowledge and novel neural network architectures with careful hyperparameter selection/optimization. The resulting solutions establish new benchmarks for data-driven RF modeling and interference cancellation.