  Recently, learning-based semantic communication (SemCom) has emerged as a
promising approach in the upcoming 6G network and researchers have made
remarkable efforts in this field. However, existing works have yet to fully
explore the advantages of the evolving nature of learning-based systems, where
knowledge accumulates during transmission have the potential to enhance system
performance. In this paper, we explore an evolving semantic communication
system for image transmission, referred to as ESemCom, with the capability to
continuously enhance transmission efficiency. The system features a novel
channel-aware semantic encoder that utilizes a pre-trained Semantic StyleGAN to
extract the channel-correlated latent variables consisting of serval semantic
vectors from the input images, which can be directly transmitted over a noisy
channel without further channel coding. Moreover, we introduce a semantic
caching mechanism that dynamically stores the transmitted semantic vectors in
the local caching memory of both the transmitter and receiver. The cached
semantic vectors are then exploited to eliminate the need to transmit similar
codes in subsequent transmission, thus further reducing 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
testing images compared to DeepJSCC and Inverse JSCC with the same BCR. Code of
this paper is available at \url{https://github.com/recusant7/GAN_SeCom}.
