Learning-based semantic communication (SemCom) has recently emerged as a promising paradigm for improving the transmission efficiency of wireless networks. However, existing methods typically rely on extensive end-to-end training, which is both inflexible and computationally expensive in dynamic wireless environments. Moreover, they fail to exploit redundancy across multiple transmissions of semantically similar content, limiting overall efficiency. To overcome these limitations, we propose a channel-aware generative adversarial network (GAN) inversion-based joint source-channel coding (CAGI-JSCC) framework that enables training-free SemCom by leveraging a pre-trained SemanticStyleGAN model. By explicitly incorporating wireless channel characteristics into the GAN inversion process, CAGI-JSCC adapts to varying channel conditions without additional training. Furthermore, we introduce a cache-enabled dynamic codebook (CDC) that caches disentangled semantic components at both the transmitter and receiver, allowing the system to reuse previously transmitted content. This semantic-level caching can continuously reduce redundant transmissions as experience accumulates. Extensive experiments on image transmission demonstrate the effectiveness of the proposed framework. In particular, our system achieves comparable perceptual quality with an average bandwidth compression ratio (BCR) of 1/224, and as low as 1/1024 for a single image, significantly outperforming baselines with a BCR of 1/128.

翻译：基于学习的语义通信（SemCom）近期成为提升无线网络传输效率的一种有前景的范式。然而，现有方法通常依赖大量端到端训练，在动态无线环境中既缺乏灵活性又计算成本高昂。此外，它们未能利用多次传输语义相似内容时存在的冗余，限制了整体效率。为克服这些局限，我们提出一种信道感知生成对抗网络(GAN)逆变换联合信源信道编码框架（CAGI-JSCC），通过利用预训练的SemanticStyleGAN模型实现免训练的语义通信。通过将无线信道特性显式纳入GAN逆变换过程，CAGI-JSCC无需额外训练即可适应变化的信道条件。此外，我们引入一种支持缓存的动态码本（CDC），在发送端和接收端分别缓存解耦的语义成分，使系统能够重复利用先前传输的内容。这种语义级缓存可在经验积累过程中持续减少冗余传输。针对图像传输的大量实验证明了所提框架的有效性。特别地，我们的系统在平均带宽压缩比（BCR）为1/224时达到可比的感知质量，单张图像BCR低至1/1024，显著优于BCR为1/128的基线方法。