Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer
This work addresses robustness in semantic communication for image transmission, offering incremental improvements over existing methods.
The paper tackles the problem of codebook-based generative semantic communication systems being vulnerable to channel noise by proposing a robust codebook-assisted image semantic communication system that jointly constructs a semantic codec and codebook, using a vector-to-index transformer to mitigate noise effects and generate images. The result shows that the generated images outperform JPEG+LDPC and traditional JSCC methods in visual perception, as demonstrated by numerical results.
Codebook-based generative semantic communication attracts increasing attention, since only indices are required to be transmitted when the codebook is shared between transmitter and receiver. However, due to the fact that the semantic relations among code vectors are not necessarily related to the distance of the corresponding code indices, the performance of the codebook-enabled semantic communication system is susceptible to the channel noise. Thus, how to improve the system robustness against the noise requires careful design. This paper proposes a robust codebook-assisted image semantic communication system, where semantic codec and codebook are first jointly constructed, and then vector-to-index transformer is designed guided by the codebook to eliminate the effects of channel noise, and achieve image generation. Thanks to the assistance of the high-quality codebook to the Transformer, the generated images at the receiver outperform those of the compared methods in terms of visual perception. In the end, numerical results and generated images demonstrate the advantages of the generative semantic communication method over JPEG+LDPC and traditional joint source channel coding (JSCC) methods.