发表机构
School of Mathematical Sciences, University of Electronic Science and Technology of China; School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学数学科学学院; 电子科技大学信息与通信工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对自动调制分类的推理后决策校正问题，提出交叉拟合残差效用与保留主决策的认知策略，在多数据集上提升了分类准确率，且在多种信道干扰场景下表现稳定。
AI 中文摘要
自动调制分类研究大多侧重表示准确性，但认知接收机还必须决定何时存在异构证据足以推翻可信的默认预测。我们通过交叉拟合残差效用和保留主决策的认知决策策略研究这一推理后问题：结构化KAN-Fourier分类器提供默认概率，而神经与非神经候选者提供可观测证据；针对候选者的残差效用从训练集拆分的袋外预测中学习，不相交的验证集拆分在保留评估前冻结动作阈值、批准的转移、条件路径及统一风险掩码。在RMLA、RMLB和HISAR数据集上，完整系统的整体准确率分别从63.632%提升至66.332%、65.161%提升至66.168%、77.769%提升至79.867%；受控对比显示，孤立的效用目标并未统一优于其他袋外元学习器，一致增益来自完整的证据-动作策略；配对自助法与Holm校正的McNemar分析支持受控增益；在载波频率偏移、I/Q失衡及合成瑞利/莱斯衰落下的冻结策略压力测试中，11种场景均获正增益，所有配对95%置信区间均大于零。
英文摘要
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.
Comments13 pages, 5 figures
