Frozen Networks, Better Decisions: SNR-Conditioned Readout for Modulation Recognition

Automatic modulation classification identifies how information is encoded in a received wireless signal. Noise can hide the patterns that distinguish modulation formats, making accurate recognition difficult. We ask how much accuracy can be gained after a classifier has been trained. We study affine readouts that adjust its output probabilities while keeping the network fixed. The maps are fitted on validation data and can use signal-to-noise ratio (SNR) to adapt their decisions to signal quality. This conditional form requires SNR at prediction time. In a post-hoc study across five public benchmarks, we compare these readouts on matched hard-label models and models trained with SNR-dependent soft targets. On the two related RadioML2016 benchmarks, independently fitted bin maps improve mean accuracy by about ten percentage points without retraining the network. This gain occurs in a validation-defined transition between low and high baseline accuracy. Sharing parameters across bins adds little on average. Gains are smaller on other benchmarks, and some conditional maps perform worse than a global map that does not use SNR. Equal readout treatment also leaves some training advantages intact outside the transition band. Simulated SNR errors can erase the readout gains. These results identify substantial room to improve trained modulation classifiers, while showing why training improvements should be evaluated against fitted output rules with their information requirements made explicit.