Large Language Model Empowered Next-Generation MIMO Networks: Fundamentals, Challenges, and Visions
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914142903861248 |
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| author | Wang, Zhe Zhang, Jiayi Du, Hongyang Zhang, Ruichen Niyato, Dusit Ai, Bo Letaief, Khaled B. |
| author_facet | Wang, Zhe Zhang, Jiayi Du, Hongyang Zhang, Ruichen Niyato, Dusit Ai, Bo Letaief, Khaled B. |
| contents | Next-generation Multiple-Input Multiple-Output (MIMO) is expected to be intelligent and scalable. In this paper, we study Large Language Model (LLM)-enabled next-generation MIMO networks. Firstly, we provide an overview of the development, fundamentals, and challenges of the next-generation MIMO. Then, we propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents with the aid of LLM and Retrieval Augmented Generation (RAG). Next, we comprehensively discuss the features and advantages of the generative AI agent framework. More importantly, to tackle existing challenges of next-generation MIMO, we discuss generative AI agent-enabled next-generation MIMO networks from the perspective of performance analysis, signal processing, and resource allocation. Furthermore, we present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis in complex configuration scenarios. These examples highlight how the integration of generative AI agents can significantly enhance the analysis and design of next-generation MIMO systems. Finally, we discuss important potential research future directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08878 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Model Empowered Next-Generation MIMO Networks: Fundamentals, Challenges, and Visions Wang, Zhe Zhang, Jiayi Du, Hongyang Zhang, Ruichen Niyato, Dusit Ai, Bo Letaief, Khaled B. Networking and Internet Architecture Information Theory Machine Learning Signal Processing Next-generation Multiple-Input Multiple-Output (MIMO) is expected to be intelligent and scalable. In this paper, we study Large Language Model (LLM)-enabled next-generation MIMO networks. Firstly, we provide an overview of the development, fundamentals, and challenges of the next-generation MIMO. Then, we propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents with the aid of LLM and Retrieval Augmented Generation (RAG). Next, we comprehensively discuss the features and advantages of the generative AI agent framework. More importantly, to tackle existing challenges of next-generation MIMO, we discuss generative AI agent-enabled next-generation MIMO networks from the perspective of performance analysis, signal processing, and resource allocation. Furthermore, we present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis in complex configuration scenarios. These examples highlight how the integration of generative AI agents can significantly enhance the analysis and design of next-generation MIMO systems. Finally, we discuss important potential research future directions. |
| title | Large Language Model Empowered Next-Generation MIMO Networks: Fundamentals, Challenges, and Visions |
| topic | Networking and Internet Architecture Information Theory Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2404.08878 |