Objective

2

Realizing high quality and highly reliable information transmission is an important goal in the field of semantic communication. Deep Joint Source Channel Coding (DeepJSCC) has emerged as an effective method for semantic communication and has made significant progress. However

existing DeepJSCC-based semantic communication methods still face the problem of semantic distortion caused by channel interference in low Signal-to-Noise Ratio (SNR) environments

making it difficult to achieve the desired quality of semantic transmission

thereby limiting the reliability and accuracy of communication. To address this issue

this paper aims to design a novel DeepJSCC framework that effectively suppresses the interference of channel noise on semantic information

improving the robustness of semantic communication systems.

Methods

2

The proposed Deep-JSCC framework integrates both spatial and frequency domain perspectives

enabling comprehensive and efficient representation and transmission of semantic information. Specifically

in the spatial domain

the framework efficiently extracts global and local semantic features from images

ensuring that semantic information is fully preserved during the encoding stage. In the frequency domain

it precisely identifies the frequency components

enabling accurate discrimination of the frequency components that have the most significant impact on the decoding task. Consequently

it enhances the expression of core semantic frequency components while suppressing the noise frequencies

significantly reducing the semantic distortion caused by channel noise.

Results

2

We evaluated the performance of the proposed method on public datasets and compared it with existing advanced semantic communication methods. The experimental results demonstrate that

compared to existing DeepJSCC methods

the proposed framework can significantly improve the accuracy of semantic information transmission in adverse communication environments (such as low SNR)

effectively mitigating the impact of semantic distortion on communication quality

thereby increasing the robustness of semantic communication systems.

Conclusion

2

The proposed DeepJSCC framework integrates the advantages of both spatial and frequency domains. Through an innovative coding strategy

it achieves efficient semantic feature extraction and enhancement of core semantic frequency components

greatly improving the robustness of semantic communication in adverse environments. This method complements existing DeepJSCC methods rather than replacing them

providing a new solution for the reliability and high-quality transmission of semantic communication systems. Our work provides a new solution for the reliability and high-quality transmission of semantic communication systems.