WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929498814939136 |
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| author | Tong, Jingwen Shao, Jiawei Wu, Qiong Guo, Wei Li, Zijian Lin, Zehong Zhang, Jun |
| author_facet | Tong, Jingwen Shao, Jiawei Wu, Qiong Guo, Wei Li, Zijian Lin, Zehong Zhang, Jun |
| contents | Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we introduce WirelessAgent, a novel approach leveraging large language models (LLMs) to develop AI agents capable of managing complex tasks in wireless networks. It can effectively improve network performance through advanced reasoning, multimodal data processing, and autonomous decision making. Thereafter, we demonstrate the practical applicability and benefits of WirelessAgent for network slicing management. The experimental results show that WirelessAgent is capable of accurately understanding user intent, effectively allocating slice resources, and consistently maintaining optimal performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07964 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks Tong, Jingwen Shao, Jiawei Wu, Qiong Guo, Wei Li, Zijian Lin, Zehong Zhang, Jun Networking and Internet Architecture Artificial Intelligence Machine Learning Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we introduce WirelessAgent, a novel approach leveraging large language models (LLMs) to develop AI agents capable of managing complex tasks in wireless networks. It can effectively improve network performance through advanced reasoning, multimodal data processing, and autonomous decision making. Thereafter, we demonstrate the practical applicability and benefits of WirelessAgent for network slicing management. The experimental results show that WirelessAgent is capable of accurately understanding user intent, effectively allocating slice resources, and consistently maintaining optimal performance. |
| title | WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.07964 |