HFEMCNet：一种用于自动调制分类的紧凑型混合频率增强多通道网络
HFEMCNet: A Compact Hybrid Frequency Enriched Multi Channel Network for Automatic Modulation Classification
中文总结 AI 辅助
HFEMCNet提出一种紧凑混合频率增强多通道网络，融合FFT频域特征与LSTM时序建模，在三个基准数据集上以更少参数和更低延迟实现超越现有深度模型的调制分类精度。
中文摘要 AI 辅助
接收无线电信号的自动调制分类（AMC）对于后续信号处理任务（如通信监测、认知无线电操作和电磁频谱中的干扰缓解）至关重要。传统方法通常依赖手工特征，在复杂信道条件下表现不佳，而深度学习（DL）架构可以直接从原始信号中学习判别性表示。在本工作中，我们提出了HFEMCNet，一种新颖的紧凑型混合频率增强多通道网络，它联合利用了通过快速傅里叶变换（FFT）获得的时空依赖性和频域信息。HFEMCNet将原始IQ样本与分层频谱特征集成，以产生丰富的信号表示，该表示由卷积层进行空间特征提取，并由长短期记忆（LSTM）单元进行时间建模。在基准数据集RML2016.10a、RML2016.10b以及空中RML2018.01a上的实验评估表明，HFEMCNet在分类准确性上显著优于当代最先进的DL模型，同时实现了更少的参数数量、更小的内存占用和更低的尾部延迟，使其适用于资源受限平台上的实时部署。这些结果凸显了将混合架构与频域增强相结合以实现鲁棒且高效AMC的有效性。
英文摘要
Automatic modulation classification (AMC) of received radio signals is prudent for further signal processing tasks such as communication monitoring, cognitive radio operation, and interference mitigation in the electromagnetic spectrum. Traditional methods often rely on handcrafted features and struggle under complex channel conditions, whereas deep learning (DL) architectures can learn discriminative representations directly from raw signals. In this work, we propose HFEMCNet, a novel compact hybrid frequency-enriched multi-channel network that jointly exploits spatiotemporal dependencies and frequency domain information derived via Fast Fourier Transform (FFT). HFEMCNet integrates raw IQ samples with hierarchical spectral features to produce a rich signal representation, which is processed by convolutional layers for spatial feature extraction and Long Short-Term Memory (LSTM) units for temporal modeling. Experimental evaluation on benchmark datasets RML2016.10a, RML2016.10b, and over-the-air RML2018.01a demonstrates that HFEMCNet significantly outperform contemporary state-of-theart DL models in classification accuracy while achieving a reduced parameter count, smaller memory footprint, and lower tail-latency, making it suitable for real-time deployment on resource-constrained platforms. These results highlight the effectiveness of combining hybrid architectures with frequencydomain enrichment for robust and efficient AMC.
