Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable. In this paper, we study generative artificial intelligence (AI) agent-enabled next-generation MIMO design. 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 large language model (LLM) and retrieval augmented generation (RAG). Next, we comprehensively discuss 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 design, from the perspective of performance analysis, signal processing, and resource allocation. Then, we provide two case studies to showcase generative AI agent-assisted performance analysis of complex configuration scenarios. It can be observed that the next-generation MIMO analysis and design can be efficiently facilitated with the assistance of generative AI agent. Finally, we discuss important potential research future directions.
In this part, we representatively provide tutorials for case studies in this paper. For XL-MIMO hardware design, we study UPA-based XL-MIMO with point antennas in case studies.
Install the necessary packages one by one using pip:
pip install langchain
pip install openai
pip install tiktoken
pip install chromadb
pip show langchain
Set up LangChain based on this repository. The database is established by merging research papers about the performance analysis of XL-MIMO:
[1] Z. Wang, J. Zhang, W. Yi, H. Du, D. Niyato, B. Ai, and D. W. K. Ng, "Analytical framework for effective degrees of freedom in near-field XL-MIMO," arXiv:2401.15280, 2024.
[2] Z. Xie, Y. Liu, J. Xu, X. Wu, and A. Nallanathan, "Performance analysis for near-field MIMO: Discrete and continuous aperture antennas," arXiv:2304.06141, 2023.
[3] S. S. A. Yuan, Z. He, X. Chen, C. Huang, and W. E. I. Sha, "Electromagnetic effective degree of freedom of an MIMO system in free space," IEEE Antennas Wireless Propagat. Lett., vol. 21, no. 3, pp. 446–450, Mar. 2022.
We formulate the capacity maximization problem for non-parallel transceiver over the dyadic Green's function based channel. The interactive assistance from the generative AI agent can be implemented (an exmaple):
With the assistance of the generative AI agent, we can study the capacity maximization problem for non-parallel transceiver. We use Matlab to implement performance evaluation. We consider one square transmitting UPA surface and one square receiving UPA surface with similar physical sizes $10\lambda \times 10\lambda$. The transmitting distances between the center point of the transmitter and the center point of the transmitter for (a) and (b) are $30\lambda$ and $4\lambda$, respectively. And the signal-to-noise ratio (SNR) $P/N_0=10$.
We then study the EDoF maximization problem for various shapes of transceiver. The interactive assistance from the generative AI agent can be implemented (an example):
We can efficiently formulate the EDoF maximization problem for various shapes of transceiver with the assistance of the generative AI agent. We apply Matlab to implement simulation evaluation. We have $L=12\lambda$ and transmitting distance $d=10\lambda$.
If our code aids your research, please cite our work:
@article{wang2024generative,
title={Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision},
author={Zhe Wang, Jiayi Zhang, Hongyang Du, Ruichen Zhang, Dusit Niyato, Bo Ai, and Khaled B. Letaief},
journal={arXiv preprint arXiv:2404.08878},
year={2024},
month = {Apr.}
}