基于联合残差网络和Bottleneck Transformer的调制格式识别方法OA北大核心
Modulation format identification method based on joint residual network and Bottleneck Transformers
针对未来光网络链路中的传输需求,提出一种基于联合残差网络(ResNet)和Bottleneck Transformer(BT)的调制格式识别(MFI)方法.该方法结合ResNet和BT对6 种不同调制格式的信号进行识别,并应用OptiSystem和TensorFlow对其进行仿真.仿真结果表明:在较宽的光信噪比(OSNR)范围内,所提方法的准确率达到了99.72%,并且能够很好地应对传输损伤的影响;与其它深度学习方法相比,该方法性能显著提升.
A modulation format recognition(MFI)method based on joint residual network(ResNet)and Bottleneck Transformer(BT)is proposed to meet the transmission requirements in future optical network links.This method combines ResNet and BT to identify signals with six different modulation formats,and applies OptiSystem and TensorFlow to simulate them.The simulation results show that within a wide range of optical signal-to-noise ratio(OSNR),the proposed method achieves an accuracy of 99.72%and can effectively cope with the impact of transmission damage.Compared with other deep learning methods,this method significantly improves its performance.
梁坤;刘战胜
江苏大学计算机科学与通信工程学院,江苏镇江 212000
电子信息工程
调制格式识别深度学习残差网络信号传输光信噪比
modulation format identificationdeep learningresidual networksignal transmissionoptical signal-to-noise ratio
《光通信技术》 2024 (003)
13-17 / 5
江苏大学高级人才科研启动基金(16JDG023)资助.
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