论文库

原文摘要

Deep neural networks for electromagnetic signal processing are typically developed as single-task experts with fixed inputs and objectives, limiting cross-task transferability. Electromagnetic foundation models offer broader task coverage but incur substantial pretraining and inference costs. To balance accuracy and efficiency, we adopt model routing to dynamically dispatch unknown signals to matched experts within a heterogeneous pool. We construct HeteroSignal-7, a multi-task routing benchmark with 140,000 samples from seven datasets covering six task types, paired with seven classification and waveform-recovery experts and a unified evaluation protocol. We then propose GLCR, a two-stage routing method combining global dispatch with local discrimination of similar tasks. First, a lightweight one-dimensional convolutional neural network (CNN) with an additive-margin head predicts one of seven routing classes. Second, a gradient-boosted-tree specialist uses four physical statistics to re-evaluate samples whose top two candidates match a confusion pair automatically identified from validation errors. The pipeline is trained across the full signal-to-noise ratio (SNR) range and requires neither SNR labels nor noise estimation at inference time. GLCR outperforms all 19 baselines, achieving 96.16% routing accuracy and a 76.30% system score, exceeding the respective second-best baselines by 1.16% and 1.14%. It achieves the lowest routing latency among the compared methods at 1.43 ms per sample and reduces expected overall system FLOPs by 5.9 times compared with executing all experts. This work provides a public evaluation platform and an efficient, reproducible baseline for heterogeneous expert routing in electromagnetic signal processing.