WhiteNet: Robust Identification of Overlapping IEEE 802.11 Signals Across Unseen Channels

Computer Science > Cryptography and Security [Submitted on 6 Aug 2026] WhiteNet: Robust Identification of Overlapping IEEE 802.11 Signals Across Unseen Channels Ildi Alla, Vincent Lenders Deep learning (DL) classifiers trained on I/Q samples achieve high accuracy for IEEE 802.11 protocol identification of overlapping signals, but their performance degrades sharply when channel conditions at deployment differ from those encountered during training. We present WhiteNet, a framework that addresses the problem of channel variability in I/Q samples. The central idea is spectral whitening, a physics-grounded preprocessing step that suppresses frequency-selective fading while preserving protocol-discriminative features. To reduce dependence on costly multi-transmitter over-the-air captures for training, we complement it with a synthetic overlap mixer featuring a physically accurate per-transmitter channel and shared-receiver signal chain for pre-training without extensive field data collection. On public over-the-air IEEE 802.11 data, WhiteNet closes a substantial portion of the accuracy gap caused by unseen channel conditions while using 7.7 times fewer parameters than the prior state of the art, and optionally distills to compact variants for coarse spectrum awareness on power-constrained edge devices. Subjects: Cryptography and Security (cs.CR); Signal Processing (eess.SP) Cite as: arXiv:2608.06581 [cs.CR]   (or arXiv:2608.06581v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.06581 Focus to learn more Submission history From: Ildi Alla [view email] [v1] Thu, 6 Aug 2026 20:50:50 UTC (2,956 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs eess eess.SP References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)