CorteXforge: Scenario-Based RF Dataset Generation on Real Testbeds

arXiv cs.LG Papers
rf-signals sdr dataset-generation deep-learning modulation-classification domain-adaptation

Summary

CorteXforge is a scenario-based framework that generates realistic, reproducible annotated RF datasets on programmable SDR testbeds (SLICES-RI/CorteXlab), validated via automatic modulation classification showing synthetic-to-real gaps and the benefits of synthetic pretraining.

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.
Original Article
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# CorteXforge: Scenario-Based RF Dataset Generation on Real Testbeds
Source: [https://arxiv.org/abs/2610.26999](https://arxiv.org/abs/2610.26999)
[View PDF](https://arxiv.org/pdf/2610.26999)

> 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\.

## Submission history

From: Andrea JOLY \[[view email](https://arxiv.org/show-email/d51d6a63/2610.26999)\] \[via CCSD proxy\] **\[v1\]**Mon, 19 Oct 2026 09:08:16 UTC \(101 KB\)

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