基于轻量化CNN的调制识别及其在ARM Cortex-M嵌入式平台的应用  

MODULATION RECOGNITION BASED ON LIGHTWEIGHT CNN AND ITS APPLICATION ON ARM CORTEX-M EMBEDDED PLATFORM

作　　者：代滔滔 佃松宜[1] 郭斌 Dai Taotao;Dian Songyi;Guo Bin(College of Electrical Engineering,Sichuan University,Chengdu 610065,Sichuan,China)

机构地区：[1]四川大学电气工程学院,四川成都610065

出　　处：《计算机应用与软件》2025年第4期263-270,共8页Computer Applications and Software

基　　金：国家重点研发计划项目(2018YFB1307401)。

摘　　要：针对无线通信中的调制信号识别,提出一种基于卷积神经网络(Convolutional Neural Network,CNN)的轻量化CNN调制识别算法,该算法利用连续多次卷积运算提取信号的空间特征,利用全连接层对特征进行维度映射,通过Softmax层输出识别概率,实现对多类信号调制方式的识别。将训练好的轻量化CNN模型经X-CUBE-AI压缩后部署到STM32F405RGT6嵌入式微控制器中,并使用RADIOML2016.10a数据集对部署后的模型进行性能测试。实验结果表明,设计的轻量化CNN模型仅占用1474.6 KiB Flash和150 KiB RAM,与其他深度学习网络模型相比具有较少的参数量,在信噪比为0 dB及以上时识别准确率最高可达81.8%,且在ARM Cortex-M嵌入式平台上可以取得和PC平台相媲美的调制信号识别效果,验证了该算法的有效性和可行性。Aimed at the problem of modulation recognition in wireless communication,a lightweight CNN modulation recognition algorithm based on convolutional neural network(CNN)is proposed.The spatial features of the signal were extracted by continuous multiple convolution operations,and features were dimensionally mapped by fully connected layer.The recognition probability was output by using the Softmax layer to achieve the recognition of multiple modulation signal.The trained lightweight CNN model was compressed by X-CUBE-AI and deployed on the STM32F405RGT6 embedded microcontroller,and the RADIOML2016.10a data set was used to test the overall performance of the deployed model.The experimental results show that the designed lightweight CNN model only occupies 1474.6 KiB Flash and 150 KiB RAM,which has fewer parameters than other deep learning models.When the signal-to-noise ratio is 0 dB and above,the recognition accuracy is up to 81.8%.The model can achieve modulation recognition effect comparable to PC platform on ARM Cortex-M embedded platform,which verifies the effectiveness and feasibility of the algorithm.

关 键 词：调制信号识别 卷积神经网络 轻量化CNN 部署 ARM Cortex-M 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]