[Submitted on 19 Oct 2026]
Title:CorteXforge: Scenario-Based RF Dataset Generation on Real Testbeds
View a PDF of the paper titled CorteXforge: Scenario-Based RF Dataset Generation on Real Testbeds, by Andr\'ea Joly and 2 other authors
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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.
Subjects:
Machine Learning (cs.LG); Information Theory (cs.IT)
Cite as: arXiv:2610.26999 [cs.LG]
(or arXiv:2610.26999v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.26999
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Andrea JOLY [view email] [via CCSD proxy] [v1] Mon, 19 Oct 2026 09:08:16 UTC (101 KB)
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