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.
Learned feature reweighting can improve automatic modulation classification (AMC) in software, but the same operation introduces additional arithmetic and latency when implemented on an FPGA. This work measures that trade-off in a compact fixed-point classifier using 24 sparse DFT-energy features, 8 phase/statistical features, and a 32-to-128-to-11 multilayer perceptron. A second architecture inserts a learned 32-element, 8-bit, input-dependent gate before the classifier. Gated and ungated models are trained using post-training quantization (PTQ) and quantization-aware training (QAT) with two matched training seeds. The resulting eight checkpoints are compiled independently for an Intel Cyclone V FPGA and evaluated over 352,000 physical-board classifications. Ungated models achieve higher test accuracy in all four matched gate comparisons, with mean gated-minus-ungated differences of -0.784 percentage points under PTQ and -0.616 percentage points under QAT. The effect of QAT changes direction between the two training seeds. In hardware, the gate adds an average of 1,318 adaptive logic modules (ALMs), 1,557 registers, 4 DSP blocks, and 3,140 processing cycles. All 352,000 board predictions agree exactly with an independent integer reference, and 3,760 captured intermediate values from one training seed also match. For this feature representation and implementation, learned gating increases FPGA cost without improving classification accuracy.
The dynamics of communication environments induce significant distribution shifts across domains, challenging the generalization of deep learning-based automatic modulation classification (AMC) models. While existing UDA methods alleviate this problem by aligning source and target features, they give limited consideration to modulation-specific structures that remain informative across domain conditions. In this paper, we consider signal prior knowledge, grounded in communication protocols and physical principles, as a potential way to enhance cross-domain representation learning. Given that different priors may vary in modulation discriminability, domain stability, and complementarity, this paper first analyzes five commonly adopted signal representations that instantiate different signal priors. From them, in-phase/quadrature (IQ), amplitude--phase (AP), and autocorrelation function (ACF) are selected as compact prior-guided inputs. Based on that, a dual knowledge and data-driven network (DKDNet) is proposed for cross-domain AMC. The multi-representation feature encoder (MRFE) and dynamic lightweight fusion unit (DLFU) are designed to achieve unified representation learning and adaptive feature fusion, and the resulting fused features are optimized with modulation classification and adversarial domain alignment objectives. Experiments on both simulated and public datasets validate the rationality of the prior selection and demonstrate the superiority of the proposed method.