  This paper studies the problem of the lightweight image semantic
communication system that is deployed on Internet of Things (IoT) devices. In
the considered system model, devices must use semantic communication techniques
to support user behavior recognition in ultimate video service with high data
transmission efficiency. However, it is computationally expensive for IoT
devices to deploy semantic codecs due to the complex calculation processes of
deep learning (DL) based codec training and inference. To make it affordable
for IoT devices to deploy semantic communication systems, we propose an
attention-based UNet enabled lightweight image semantic communication (LSSC)
system, which achieves low computational complexity and small model size. In
particular, we first let the LSSC system train the codec at the edge server to
reduce the training computation load on IoT devices. Then, we introduce the
convolutional block attention module (CBAM) to extract the image semantic
features and decrease the number of downsampling layers thus reducing the
floating-point operations (FLOPs). Finally, we experimentally adjust the
structure of the codec and find out the optimal number of downsampling layers.
Simulation results show that the proposed LSSC system can reduce the semantic
codec FLOPs by 14%, and reduce the model size by 55%, with a sacrifice of 3%
accuracy, compared to the baseline. Moreover, the proposed scheme can achieve a
higher transmission accuracy than the traditional communication scheme in the
low channel signal-to-noise (SNR) region.
