论文标题
语义沟通为时间关键的物联网应用实现强大的边缘智能
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications
论文作者
论文摘要
本文旨在使用语义通信来设计强大的边缘智能，以实现时间关键的物联网应用程序。我们系统地分析了图像DCT系数对推理准确性的影响，并首先通过传输最有意义的任务数据来提出用于卸载的通道敏捷有效性。该方案可以很好地利用所有可用的通信资源，并在传输延迟和推理准确性之间取得平衡。然后，我们通过实现卷积神经网络（CNN）训练的新图像增强过程来设计有效解码，通过该过程将原始的CNN模型转换为强大的CNN模型。我们使用拟议的培训方法生成鲁棒的Mobilenet-V2和鲁棒的Resnet-50。拟议的边缘情报框架由提议的编码和有效性解码组成。实验结果表明，使用强大的CNN模型的有效性解码在由通道误差或有限的通信资源引起的各种图像扭曲下表现更好。提出的使用语义通信的Edge Intelligence框架在延迟和数据速率约束下，尤其是在超严格的截止日期和低数据速率下，大大优于常规方法。
This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We systematically analyze the effect of image DCT coefficients on inference accuracy and propose the channel-agnostic effectiveness encoding for offloading by transmitting the most meaningful task data first. This scheme can well utilize all available communication resource and strike a balance between transmission latency and inference accuracy. Then, we design an effectiveness decoding by implementing a novel image augmentation process for convolutional neural network (CNN) training, through which an original CNN model is transformed into a Robust CNN model. We use the proposed training method to generate Robust MobileNet-v2 and Robust ResNet-50. The proposed Edge Intelligence framework consists of the proposed effectiveness encoding and effectiveness decoding. The experimental results show that the effectiveness decoding using the Robust CNN models perform consistently better under various image distortions caused by channel errors or limited communication resource. The proposed Edge Intelligence framework using semantic communication significantly outperforms the conventional approach under latency and data rate constraints, in particular, under ultra stringent deadlines and low data rate.
