BitSemCom: A Bit-Level Semantic Communication Framework with Learnable Probabilistic Mapping
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912678218301440 |
|---|---|
| author | Zhang, Haoshuo Bo, Yufei Mo, Jianhua Tao, Meixia |
| author_facet | Zhang, Haoshuo Bo, Yufei Mo, Jianhua Tao, Meixia |
| contents | Most existing semantic communication systems employ analog modulation, which is incompatible with modern digital communication systems. Although several digital transmission approaches have been proposed to address this issue, an end-to-end bit-level method that is compatible with arbitrary modulation formats, robust to channel noise, and free from quantization errors remains lacking. To this end, we propose BitSemCom, a novel bit-level semantic communication framework that realizes true joint source-channel coding (JSCC) at the bit level. Specifically, we introduce a modular learnable bit mapper that establishes a probabilistic mapping between continuous semantic features and discrete bits, utilizing the Gumbel-Softmax trick to enable differentiable bit generation. Simulation results on image transmission demonstrate that BitSemCom achieves both competitive performance and superior robustness compared to traditional separate source-channel coding (SSCC) schemes, and outperforms deep learning based JSCC with uniform 1-bit quantization, validating the effectiveness of the learnable bit mapper. Despite these improvements, the bit mapper adds only 0.42% parameters and 0.09% computational complexity, making BitSemCom a lightweight and practical solution for real-world semantic communication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26225 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BitSemCom: A Bit-Level Semantic Communication Framework with Learnable Probabilistic Mapping Zhang, Haoshuo Bo, Yufei Mo, Jianhua Tao, Meixia Image and Video Processing Most existing semantic communication systems employ analog modulation, which is incompatible with modern digital communication systems. Although several digital transmission approaches have been proposed to address this issue, an end-to-end bit-level method that is compatible with arbitrary modulation formats, robust to channel noise, and free from quantization errors remains lacking. To this end, we propose BitSemCom, a novel bit-level semantic communication framework that realizes true joint source-channel coding (JSCC) at the bit level. Specifically, we introduce a modular learnable bit mapper that establishes a probabilistic mapping between continuous semantic features and discrete bits, utilizing the Gumbel-Softmax trick to enable differentiable bit generation. Simulation results on image transmission demonstrate that BitSemCom achieves both competitive performance and superior robustness compared to traditional separate source-channel coding (SSCC) schemes, and outperforms deep learning based JSCC with uniform 1-bit quantization, validating the effectiveness of the learnable bit mapper. Despite these improvements, the bit mapper adds only 0.42% parameters and 0.09% computational complexity, making BitSemCom a lightweight and practical solution for real-world semantic communication. |
| title | BitSemCom: A Bit-Level Semantic Communication Framework with Learnable Probabilistic Mapping |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2510.26225 |