DITE-Net: A Dendritic-Inhibitory Temporal Evidence Network for Automatic Modulation Recognition

Automatic modulation recognition (AMR) from short in-phase and quadrature (I/Q) sequences remains challenging at low signal-to-noise ratios (SNRs), as informative local structures become less distinguishable and discriminative evidence may be distributed across time. We present DITE-Net, a Dendritic-Inhibitory Temporal Evidence Network for short-sequence AMR. Inspired by neural information-processing functions, the architecture combines dendritic compartmental integration (DCI) for input-dependent multiscale feature aggregation, excitation–inhibition regulation (EIR) for gated residual response control, and distributed temporal evidence integration (DTEI) for contextual processing and bidirectional recurrent aggregation. The neural-circuit-inspired terminology reflects functional design analogies rather than a biologically faithful simulation. Experiments on RADIOML 2016.10A and RML22 demonstrate that DITE-Net achieves competitive recognition accuracy. Additional ablation studies and low-SNR evaluations indicate that removing BiGRU causes the largest accuracy reduction among the evaluated components, while the independent effects of adaptive routing and EIR remain modest and dataset-dependent. Computational analyses further quantify the trade-off between recognition accuracy and inference cost for fixed-window processing.