计算机科学
生成语法
传输(电信)
人工智能
卫星
语言模型
自然语言处理
生成模型
通信卫星
语音识别
计算机网络
电信
工程类
航空航天工程
作者
Ruichen Zhang,Hongyang Du,Yinqiu Liu,Dusit Niyato,Jiawen Kang,Zehui Xiong,Abbas Jamalipour,Dong In Kim
标识
DOI:10.1109/jsac.2024.3459037
摘要
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 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 aggregate 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI