Collaborative Automatic Modulation Classification via Deep Edge Inference for Hierarchical Cognitive Radio Networks

Chaowei He, Peihao Dong, Fuhui Zhou, Qihui Wu
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Abstract

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 deter?mine 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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通过深度边缘推理为分层认知无线电网络协同自动调制分类
在分层认知无线电网络中,边缘或云服务器利用边缘设备收集的数据进行调制分类,但这面临着传输开销、数据隐私和计算负荷等问题。本文提出了一种基于边缘学习(EL)的框架,将边缘设备和边缘服务器联合起来进行智能协同推理,以实现它们之间的协同自动调制分类(C-AMC)。为边缘设备设计了轻量级结构的频谱语义压缩神经网络(SSCNet),将采集到的原始数据压缩成紧凑的语义信息,然后通过无线信道发送到边缘服务器。在边缘服务器端,结合双向长短时记忆(Bi?LSTM)和多头注意层的调制分类神经网络(MCNet)被精心设计,以从噪声语义信息中识别调制类型。通过充分利用边缘设备和边缘服务器的计算资源,避免了高传输开销和数据隐私泄露的风险。仿真结果验证了所提出的 C-AMC 框架的有效性,大大降低了模型大小和计算复杂度。
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