Enhancing Communication Efficiency of Semantic Transmission via Joint Processing Technique
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913182922047488 |
|---|---|
| author | Pu, Xumin Lei, Tiantian Wen, Wanli Chen, Qianbin |
| author_facet | Pu, Xumin Lei, Tiantian Wen, Wanli Chen, Qianbin |
| contents | This work presents a novel semantic transmission framework in wireless networks, leveraging the joint processing technique. Our framework enables multiple cooperating base stations to efficiently transmit semantic information to multiple users simultaneously. To enhance the semantic communication efficiency of the transmission framework, we formulate an optimization problem with the objective of maximizing the semantic spectral efficiency of the framework and propose a lowcomplexity dynamic semantic mapping and resource allocation algorithm. This algorithm, based on deep reinforcement learning and alternative optimization, achieves near-optimal performance while reducing computational complexity. Simulation results validate the effectiveness of the proposed algorithm, bridging the research gap and facilitating the practical implementation of semantic communication systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01143 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Communication Efficiency of Semantic Transmission via Joint Processing Technique Pu, Xumin Lei, Tiantian Wen, Wanli Chen, Qianbin Information Theory This work presents a novel semantic transmission framework in wireless networks, leveraging the joint processing technique. Our framework enables multiple cooperating base stations to efficiently transmit semantic information to multiple users simultaneously. To enhance the semantic communication efficiency of the transmission framework, we formulate an optimization problem with the objective of maximizing the semantic spectral efficiency of the framework and propose a lowcomplexity dynamic semantic mapping and resource allocation algorithm. This algorithm, based on deep reinforcement learning and alternative optimization, achieves near-optimal performance while reducing computational complexity. Simulation results validate the effectiveness of the proposed algorithm, bridging the research gap and facilitating the practical implementation of semantic communication systems. |
| title | Enhancing Communication Efficiency of Semantic Transmission via Joint Processing Technique |
| topic | Information Theory |
| url | https://arxiv.org/abs/2401.01143 |