Cache-enabled Generative Joint Source-Channel Coding for Evolving Semantic Communications

doi:10.1109/tcomm.2026.3735786

Cache-enabled Generative Joint Source-Channel Coding for Evolving Semantic Communications

2026-09-21

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DOI：10.1109/tcomm.2026.3735786

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基于学习的语义通信（SemCom） 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 requires no additional end-to-end transceiver training 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.

Keyword:

Semantic communication

wireless cache

GAN

joint source-channel coding

Cache-enabled Generative Joint Source-Channel Coding for Evolving Semantic Communications

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