Cross-Fitted Residual Utility for Primary-Preserving Cognitive Decision Correction in Automatic Modulation Classification
2026-08-03 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors address how a cognitive radio receiver can decide when to trust a default signal classification or switch based on new evidence. They use a system combining a structured default classifier with other models that provide additional information, learning how to weigh their usefulness. Their method improves accuracy on several datasets and remains reliable even under various signal distortions. The findings show that the overall decision policy, not just the learned utility, drives improvements.
automatic modulation classificationcognitive receivercross-fitted residual utilityKAN-Fourier classifierout-of-fold predictionsrisk maskMcNemar testcarrier-frequency offsetI/Q imbalanceRayleigh/Rician fading
Authors
Linzhuo Han, Zongyong Cui, Houbiao Li
Abstract
Automatic modulation classification research has largely emphasized representation accuracy, but a cognitive receiver must also decide when heterogeneous evidence justifies overriding a trusted default prediction. We study this post-inference problem through cross-fitted residual utility and a primary-preserving cognitive decision policy. A structured KAN-Fourier classifier supplies the default probability, while neural and non-neural candidates provide observable evidence. Candidate-specific residual utility is learned from train-split out-of-fold predictions, and a disjoint validation split freezes action thresholds, approved transitions, conditional routes, and a unified risk mask before held-out evaluation. On RMLA, RMLB, and HISAR, the complete system improves overall accuracy from 63.632% to 66.332%, 65.161% to 66.168%, and 77.769% to 79.867%, respectively. Controlled comparisons show that the isolated utility target does not uniformly dominate alternative out-of-fold meta-learners; the consistent gain comes from the complete evidence-and-action policy. Paired bootstrap and Holm-corrected McNemar analyses support the controlled gains. A frozen-policy stress test under carrier-frequency offset, I/Q imbalance, and synthetic Rayleigh/Rician fading yields positive gains in all 11 conditions, with every paired 95\% confidence interval above zero.