一种基于FL-TransCNN神经网络的水声智能频谱感知算法OA
An Underwater Acoustic Intelligent Spectrum Sensing Algorithm Based on FL-TransCNN Neural Network
为了提高频谱利用率,提出了一种基于联邦学习(Federated Learning,FL)、Transformer和卷积神经网络(Convolutional Neural Network,CNN)的水声智能频谱感知算法.首先,基于FL实现数据隔离状态下的信息共享,并应用Paillier加密技术进行权重加密保障;其次,本地感知数据经连续小波变换构建为时频谱图;最后,融合CNN与Transformer构建了TransCNN感知器,通过并行分支实现了高精度感知.在信噪比-18～0 dB范围内,与RepVGG、Swin-Transformer、YOLOv7、MobileNet算法相比,所提的水声智能频谱感知算法的平均检测概率提升了4%～10%,平均虚警概率降低了2%～9%.
To improve the spectrum utilization,an underwater acoustic intelligent spectrum sensing algorithm based on federated learning(FL),Transformer and convolutional neural network(CNN)is proposed.Firstly,information sharing in a data isolation state is realized based on FL,and Paillier encryption technology is applied to guarantee weight encryption.Secondly,the local sensing data is constructed into a time-frequency spectrum by continuous wavelet transform.Finally,a TransCNN perceptron is constructed by combining CNN and Transformer,and high-precision perception is achieved through parallel branches.Compared with that of RepVGG,Swin-Transformer,YOLOv7,and MobileNet algorithms,the average detection probability of the proposed algorithm based on the FL-TransCNN neural network is improved by 4%to 10%and the average false alarm probability is reduced by 2%to 9%in-18 dB to 0 dB signal-to-noise ratio.
李玉芳;王锴;张力良;徐凌伟;Thomas Aaron Gulliver
青岛科技大学 信息科学技术学院,山东 青岛 266061数字化学习技术集成与应用教育部工程研究中心,北京 100039青岛科技大学 信息科学技术学院,山东 青岛 266061数字化学习技术集成与应用教育部工程研究中心,北京 100039青岛科技大学 信息科学技术学院,山东 青岛 266061
信息技术与安全科学
海洋物联网智能频谱感知联邦学习连续小波变换深度可分离卷积
marine Internet of Thingsintelligent spectrum sensingfederated learningcontinuous wavelet transformdepthwise separable convolution
《电讯技术》 2026 (1)
11-20,10
国家自然科学基金资助项目(62201313)数字化学习技术集成与应用教育部工程研究中心创新基金项目(1321012)
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