Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer
CoRR(2024)
摘要
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.
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