Interactive Generative AI Agents for Satellite Networks through a
  Mixture of Experts Transmission
Ruichen Zhang Hongyang Du Yinqiu Liu Dusit Niyato Jiawen Kang Zehui Xiong Abbas Jamalipour Dong In Kim
In response to the needs of 6G global communications, satellite communication
networks have emerged as a key solution. However, the large-scale development
of satellite communication networks is constrained by the complex system
models, whose modeling is challenging for massive users. Moreover, transmission
interference between satellites and users seriously affects communication
performance. To solve these problems, this paper develops generative artificial
intelligence (AI) agents for model formulation and then applies a mixture of
experts (MoE) approach to design transmission strategies. Specifically, we
leverage large language models (LLMs) to build an interactive modeling paradigm
and utilize retrieval-augmented generation (RAG) to extract satellite expert
knowledge that supports mathematical modeling. Afterward, by integrating the
expertise of multiple specialized components, we propose an MoE-proximal policy
optimization (PPO) approach to solve the formulated problem. Each expert can
optimize the optimization variables at which it excels through specialized
training through its own network and then aggregates them through the gating
network to perform joint optimization. The simulation results validate the
accuracy and effectiveness of employing a generative agent for problem
formulation. Furthermore, the superiority of the proposed MoE-ppo approach over
other benchmarks is confirmed in solving the formulated problem. The
adaptability of MoE-PPO to various customized modeling problems has also been
demonstrated.