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October 10, 2025Open Access

Semantic Channel Equalization Strategies for Deep Joint Source-Channel Coding

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Semantic noise degrades reconstruction quality and task performance, highlighting a critical challenge in deep joint source-channel coding.
Extensive experiments on image reconstruction reveal trade-offs in complexity, data efficiency, and fidelity across different aligners.
Three classes of aligners are proposed: linear maps, lightweight neural networks, and a Parseval-frame equalizer for zero-shot operation.
Improved guidelines are provided for deploying deep joint source-channel coding in heterogeneous AI-native wireless networks.

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Implication

Systematic evaluation of semantic channel equalization methods for deep joint source-channel coding, suggesting improved performance in heterogeneous networks.

Cite This Study

Pannacci et al. (2025) studied this question.

synapsesocial.com/papers/68e997abe14057276da7f1d3 https://doi.org/10.48550/arxiv.2510.04674

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Implication

Systematic evaluation of semantic channel equalization methods for deep joint source-channel coding, suggesting improved performance in heterogeneous networks.

Semantic Channel Equalization Strategies for Deep Joint Source-Channel Coding

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Implication

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Semantic Channel Equalization Strategies for Deep Joint Source-Channel Coding

Key Points

Discussion

Implication

Cite This Study

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