End-to-End Learning-Based Wireless Image Recognition Using the PyramidNet in Edge Intelligence

In edge intelligence, deep learning (DL) models are deployed at an edge device and an edge server for data processing with low latency in the Internet of Things (IoT). In this paper, we propose a new end-to-end learning-based wireless image recognition scheme using the PyramidNet in edge intelligence. We split the PyramidNet carefully into two parts for an IoT device and the edge server, which is to pursue low on-device computation. Also, we apply a squeeze-and-excitation block to the PyramidNet for the improvement of image recognition. In addition, we embed compression encoder and decoder at the splitting point, which reduces communication overhead by compressing the intermediate feature map. Simulation results demonstrate that the proposed scheme is superior to other DL-based schemes in image recognition, while presenting less on-device computation and fewer parameters with low communication overhead.