# Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission

arXiv: https://arxiv.org/abs/2404.09134 · HTML: https://mlsanity.com/paper/2404.09134/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 24 of its 54 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)
- Mixture-of-experts / [MoE in reinforcement learning and robotics](https://mlsanity.com/moe/moe-in-reinforcement-learning-and-robotics/)

## Builds on (most cited first, 24 of 24)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://mlsanity.com/paper/2112.06905/) (2021)
- [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2309.01431/) (2023)
- [Learning Factored Representations in a Deep Mixture of Experts](https://mlsanity.com/paper/1312.4314/) (2013)
- [InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews](https://mlsanity.com/paper/2310.17976/) (2023)
- [SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks](https://mlsanity.com/paper/2305.17390/) (2023)
- [Mixtures of Experts Unlock Parameter Scaling for Deep RL](https://mlsanity.com/paper/2402.08609/) (2024)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [TaskExpert: Dynamically Assembling Multi-Task Representations with Memorial Mixture-of-Experts](https://mlsanity.com/paper/2307.15324/) (2023)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [Interactive AI with Retrieval-Augmented Generation for Next Generation Networking](https://mlsanity.com/paper/2401.11391/) (2024)
- [Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning](https://mlsanity.com/paper/2104.09122/) (2021)
- [Satellite Based Computing Networks with Federated Learning](https://mlsanity.com/paper/2111.10586/) (2021)
- [Generative AI for Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2311.06523/) (2023)
- [Reinforcement Learning With LLMs Interaction For Distributed Diffusion Model Services](https://mlsanity.com/paper/2311.11094/) (2023)
- [Rate-Splitting Unifying SDMA, OMA, NOMA, and Multicasting in MISO Broadcast Channel: A Simple Two-User Rate Analysis](https://mlsanity.com/paper/1906.04474/) (2019)
- [LEO Satellite Access Network (LEO-SAN) Towards 6G: Challenges and Approaches](https://mlsanity.com/paper/2207.11896/) (2022)
- [LEO Satellite Constellations for 5G and Beyond: How Will They Reshape Vertical Domains?](https://mlsanity.com/paper/2106.09897/) (2021)
- [Low Earth Orbit Satellite Security and Reliability: Issues, Solutions, and the Road Ahead](https://mlsanity.com/paper/2201.03063/) (2022)
- [Channel Gain Cartography via Mixture of Experts](https://mlsanity.com/paper/2012.04290/) (2020)
- [Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks](https://mlsanity.com/paper/2208.03705/) (2022)

## Built on by (most cited first, 30 of 35)

- [From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Users](https://mlsanity.com/paper/2508.17281/) (2025)
- [Adaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments](https://mlsanity.com/paper/2502.02287/) (2025)
- [Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking](https://mlsanity.com/paper/2502.16866/) (2025)
- [ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks](https://mlsanity.com/paper/2601.19607/) (2026)
- [Generative Semantic Communication for Joint Image Transmission and Segmentation](https://mlsanity.com/paper/2411.18005/) (2024)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Retrieval-augmented Generation for GenAI-enabled Semantic Communications](https://mlsanity.com/paper/2412.19494/) (2024)
- [LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach](https://mlsanity.com/paper/2505.12247/) (2025)
- [Bridging Physical and Digital Worlds: Embodied Large AI for Future Wireless Systems](https://mlsanity.com/paper/2506.24009/) (2025)
- [Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models](https://mlsanity.com/paper/2404.18077/) (2024)
- [Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization](https://mlsanity.com/paper/2408.03631/) (2024)
- [When Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study](https://mlsanity.com/paper/2412.07189/) (2024)
- [Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles](https://mlsanity.com/paper/2501.15544/) (2025)
- [Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations](https://mlsanity.com/paper/2503.23934/) (2025)
- [Large Language Model-enhanced Reinforcement Learning for Low-Altitude Economy Networking](https://mlsanity.com/paper/2505.21045/) (2025)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025)
- [Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization](https://mlsanity.com/paper/2510.10028/) (2025)
- [Reliable LLM-Based Edge-Cloud-Expert Cascades for Telecom Knowledge Systems](https://mlsanity.com/paper/2512.20012/) (2025)
- [Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions](https://mlsanity.com/paper/2503.10058/) (2025)
- [BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization](https://mlsanity.com/paper/2509.11056/) (2025)
- [Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework](https://mlsanity.com/paper/2501.09631/) (2025)
- [Intelligent Mobile AI-Generated Content Services via Interactive Prompt Engineering and Dynamic Service Provisioning](https://mlsanity.com/paper/2502.11386/) (2025)
- [A Trustworthy Multi-LLM Network: Challenges,Solutions, and A Use Case](https://mlsanity.com/paper/2505.03196/) (2025)
- [Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness](https://mlsanity.com/paper/2506.08532/) (2025)
- [Task-Oriented Edge-Assisted Cross-System Design for Real-Time Human-Robot Interaction in Industrial Metaverse](https://mlsanity.com/paper/2508.20664/) (2025)
- [Resource-Aware LLM Reasoning for Mobile Edge General Intelligence](https://mlsanity.com/paper/2509.23248/) (2025)
- [Asynchronous Risk-Aware Multi-Agent Packet Routing for Ultra-Dense LEO Satellite Networks](https://mlsanity.com/paper/2510.27506/) (2025)
- [Embodied AI-Enhanced IoMT Edge Computing: UAV Trajectory Optimization and Task Offloading with Mobility Prediction](https://mlsanity.com/paper/2512.20902/) (2025)

## Abstract (arXiv)

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.