Robust and Efficient Specific Emitter Identification via Broad Learning-Based Mixture of Experts

Abstract

Specific Emitter Identification (SEI) is important for enhancing physical-layer security in resource-constrained Internet of Things (IoT) networks. However, Deep Learning (DL)-based SEI methods often incur high computational complexity and suffer performance degradation under noisy conditions. To address these issues, this letter proposes Broad Learning-Based Mixture of Experts (BLMoE) with Dynamic Entropy-Residual (DER) fusion, referred to as DER-BLMoE. DER-BLMoE constructs lightweight Broad Learning System (BLS) experts over frequency sub-bands, allocates expert resources according to spectral variance, and fuses sub-band predictions through DER without additional trainable gating parameters. Experimental results over multiple Monte Carlo trials show that DER-BLMoE achieves 96.37% average accuracy with only 0.24M FLOPs, reduces parameters and FLOPs by 92.0% and 92.1% compared with SFEBLN, and maintains 95.94% accuracy at 0 dB Signal-to-Noise Ratio (SNR) under Additive White Gaussian Noise (AWGN). The code is available at https://github.com/3041749396/DER-BLMoE