Large Language Models-Empowered Wireless Networks: Fundamentals, Architecture, and Challenges
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908405811118080 |
|---|---|
| author | Khan, Latif U. Guizani, Maher Muhaidat, Sami Hong, Choong Seon |
| author_facet | Khan, Latif U. Guizani, Maher Muhaidat, Sami Hong, Choong Seon |
| contents | The rapid advancement of wireless networks has resulted in numerous challenges stemming from their extensive demands for quality of service towards innovative quality of experience metrics (e.g., user-defined metrics in terms of sense of physical experience for haptics applications). In the meantime, large language models (LLMs) emerged as promising solutions for many difficult and complex applications/tasks. These lead to a notion of the integration of LLMs and wireless networks. However, this integration is challenging and needs careful attention in design. Therefore, in this article, we present a notion of rational wireless networks powered by \emph{telecom LLMs}, namely, \emph{LLM-native wireless systems}. We provide fundamentals, vision, and a case study of the distributed implementation of LLM-native wireless systems. In the case study, we propose a solution based on double deep Q-learning (DDQN) that outperforms existing DDQN solutions. Finally, we provide open challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10651 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Models-Empowered Wireless Networks: Fundamentals, Architecture, and Challenges Khan, Latif U. Guizani, Maher Muhaidat, Sami Hong, Choong Seon Networking and Internet Architecture Signal Processing The rapid advancement of wireless networks has resulted in numerous challenges stemming from their extensive demands for quality of service towards innovative quality of experience metrics (e.g., user-defined metrics in terms of sense of physical experience for haptics applications). In the meantime, large language models (LLMs) emerged as promising solutions for many difficult and complex applications/tasks. These lead to a notion of the integration of LLMs and wireless networks. However, this integration is challenging and needs careful attention in design. Therefore, in this article, we present a notion of rational wireless networks powered by \emph{telecom LLMs}, namely, \emph{LLM-native wireless systems}. We provide fundamentals, vision, and a case study of the distributed implementation of LLM-native wireless systems. In the case study, we propose a solution based on double deep Q-learning (DDQN) that outperforms existing DDQN solutions. Finally, we provide open challenges. |
| title | Large Language Models-Empowered Wireless Networks: Fundamentals, Architecture, and Challenges |
| topic | Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2506.10651 |