Deep Learning-Based Semantic Communication System for Wireless Image Transmission

As semantic communication can efficiently extract semantic information and significantly reduce the amount of data transmission, an increasing number of studies are exploring deep learning-based semantic communication methods. This paper proposes a convolutional neural network (ConvNet)-based semantic communication system for wireless image transmission called ConvSC, which utilizes deep learning techniques for semantic information extraction and reconstruction. To address the adaptation of semantic communication systems to different channel states, a semantic adaptive module is proposed, which adjusts the weights of different parts of the semantic information based on channel state information. The model was evaluated under additive white Gaussian noise (AWGN) channel, Rayleigh fading channel, and underwater wireless optical communication (UWOC) channel. Simulation results demonstrate that, compared to the baseline model, our proposed ConvSC achieves superior performance across multiple metrics in various channel environments.