Symbol Distributions in Semantic Communications: A Source-Channel Equilibrium Perspective
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909964898926592 |
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| author | Yoo, Hanju Choi, Dongha Kim, Songkuk Chae, Chan-Byoung Heath Jr, Robert W. |
| author_facet | Yoo, Hanju Choi, Dongha Kim, Songkuk Chae, Chan-Byoung Heath Jr, Robert W. |
| contents | Semantic communication systems often use an end-to-end neural network to map input data into continuous symbols. These symbols, which are essentially neural network features, usually have fixed dimensions and heavy-tailed distributions. However, due to the end-to-end training nature of the neural network encoder, the underlying reason for the symbol distribution remains underexplored. We propose a new explanation for the semantic symbol distribution: an inherent trade-off between source coding and communications. Specifically, the encoder balances two objectives: allocating power for minimum \emph{effective codelength} (for source coding) and maximizing mutual information (for communications). We formalize this trade-off via an information-theoretic optimization framework, which yields a Student's $t$-distribution as the resulting symbol distribution. Through extensive studies on image-based semantic systems, we find that our formulation models the learned symbols and predicts how the symbol distribution's shape parameter changes with respect to (i) the use of variable-length coding and (ii) the dataset's entropy variability. Furthermore, we demonstrate how introducing a regularizer that enforces a target symbol distribution, which guides the encoder towards a target prior (e.g., Gaussian), improves training convergence and supports our hypothesis. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_14022 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Symbol Distributions in Semantic Communications: A Source-Channel Equilibrium Perspective Yoo, Hanju Choi, Dongha Kim, Songkuk Chae, Chan-Byoung Heath Jr, Robert W. Information Theory Signal Processing Semantic communication systems often use an end-to-end neural network to map input data into continuous symbols. These symbols, which are essentially neural network features, usually have fixed dimensions and heavy-tailed distributions. However, due to the end-to-end training nature of the neural network encoder, the underlying reason for the symbol distribution remains underexplored. We propose a new explanation for the semantic symbol distribution: an inherent trade-off between source coding and communications. Specifically, the encoder balances two objectives: allocating power for minimum \emph{effective codelength} (for source coding) and maximizing mutual information (for communications). We formalize this trade-off via an information-theoretic optimization framework, which yields a Student's $t$-distribution as the resulting symbol distribution. Through extensive studies on image-based semantic systems, we find that our formulation models the learned symbols and predicts how the symbol distribution's shape parameter changes with respect to (i) the use of variable-length coding and (ii) the dataset's entropy variability. Furthermore, we demonstrate how introducing a regularizer that enforces a target symbol distribution, which guides the encoder towards a target prior (e.g., Gaussian), improves training convergence and supports our hypothesis. |
| title | Symbol Distributions in Semantic Communications: A Source-Channel Equilibrium Perspective |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2512.14022 |