边端协同推理实现低开销调制识别，保护隐私又省计算。
Collaborative Automatic Modulation Classification via Deep Edge Inference for Hierarchical Cognitive Radio Networks
- 边端设备与服务器分工协作，边端压缩数据，云端分类。
- 模型体积和计算量显著降低，支持实时调制识别。
- 适合资源受限的边缘计算场景，尤其无线网络智能感知。
在分层认知无线电网络中，边缘或云服务器利用边缘设备采集的数据进行调制分类，但面临传输开销大、数据隐私泄露及计算负载高的问题。本文提出一种基于边缘学习（EL）的框架，通过边缘设备与边缘服务器协同智能推断，实现边端联合自动调制分类（C-AMC）。设计轻量级谱语义压缩神经网络（SSCNet）部署于边缘设备，将原始数据压缩为紧凑的语义消息，并通过无线信道发送至边缘服务器；在边缘服务器侧，采用结合双向长短期记忆（Bi-LSTM）与多头注意力机制的调制分类神经网络（MCNet），从含噪语义消息中判定调制类型。通过合理分配边端算力，有效降低传输开销与隐私泄露风险。仿真结果验证了该框架的有效性，显著减小模型规模与计算复杂度。
原文摘要 · Abstract (English)
In hierarchical cognitive radio networks, edge or cloud servers utilize the data collected by edge devices for modulation classification, which, however, is faced with problems of the transmission overhead, data privacy, and computation load. In this article, an edge learning (EL) based framework jointly mobilizing the edge device and the edge server for intelligent co-inference is proposed to realize the collaborative automatic modulation classification (C-AMC) between them. A spectrum semantic compression neural network (SSCNet) with the lightweight structure is designed for the edge device to compress the collected raw data into a compact semantic message that is then sent to the edge server via the wireless channel. On the edge server side, a modulation classification neural network (MCNet) combining bidirectional long short-term memory (Bi-LSTM) and multi-head attention layers is elaborated to determine the modulation type from the noisy semantic message. By leveraging the computation resources of both the edge device and the edge server, high transmission overhead and risks of data privacy leakage are avoided. The simulation results verify the effectiveness of the proposed C-AMC framework, significantly reducing the model size and computational complexity.
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