Evolving Semantic Communication with Generative Modelling
Shunpu Tang, Qianqian Yang, Denız Gündüz, Zhaoyang Zhang · 2024
Learning-based semantic communication (SemCom) has emerged as a promising solution for the upcoming 6G networks. In this paper, we explore an evolving SemCom system for image transmission, which can continuously adapt and enhance its transmission efficiency by exploiting knowledge accumulated during previous transmissions. Specifically, we propose a novel channel-aware semantic encoder that utilizes a pretrained generative model to extract channel-correlated latent variables consisting of several semantic vectors from the input images, which can be directly transmitted over a noisy channel without further channel coding. Moreover, we introduce a dynamic code construction mechanism that dynamically updates the codebook with transmitted semantic vectors to eliminate the need to transmit similar codes in subsequent transmissions, thus further reducing the communication overhead. Simulation results highlight the evolving performance of the proposed system in terms of transmission efficiency, achieving superior perceptual quality with an average bandwidth compression ratio (BCR) of $1 / 192$ for a sequence of 100 test images compared to DeepJSCC and InverseJSCC. Code used in this paper is available at https://github.com/recusant7/GAN_SeCom.