CorteXforge: Scenario-Based RF Dataset Generation on Real Testbeds

Researchers introduced CorteXforge, a scenario-based framework that automatically translates high-level radio frequency scenarios into synchronized experiments on the SLICES-RI/CorteXlab programmable software defined radio testbed, producing fully annotated Signal Metadata Format (SIGMF) datasets that capture hardware impairments and propagation effects. The team validated CorteXforge with ModForge, an over-the-air dataset generated by the framework, in automatic modulation classification experiments; cross-domain tests revealed a synthetic-to-real performance gap while showing synthetic pretraining remains beneficial for adaptation to over-the-air data. The work, posted as arXiv:2610.26999v1, is presented as a practical foundation for reproducible RF dataset generation and future domain-generalization research.

arXiv:2610.26999v1 Announce Type: new Abstract: Deep learning for radio frequency RF signal analysis relies on large annotated datasets, yet synthetic datasets trade physical realism for controllability, while uncontrolled over-theair measurements offer realism but limited reproducibility. We present CorteXforge, a scenario-based framework for generating realistic and reproducible RF datasets on programmable software defined radio SDR testbeds. High-level RF scenarios are automatically translated into synchronized experiments executed on the SLICES-RI/CorteXlab platform, producing fully annotated Signal Metadata Format SIGMF datasets that capture hardware impairments and propagation effects. We validate CorteXforge through automatic modulation classification using ModForge, an over-the-air OTA dataset generated with the framework. Cross-domain experiments reveal a synthetic-to-real performance gap and show that synthetic pretraining remains beneficial for adaptation to OTA data. CorteXforge provides a practical foundation for reproducible RF dataset generation and future research on domain generalization.