# Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs

arXiv: https://arxiv.org/abs/2407.06227 · HTML: https://mlsanity.com/paper/2407.06227/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Eureka: Human-Level Reward Design via Coding Large Language Models](https://mlsanity.com/paper/2310.12931/) (2023)
- [Reward Design with Language Models](https://mlsanity.com/paper/2303.00001/) (2023)
- [Guiding Pretraining in Reinforcement Learning with Large Language Models](https://mlsanity.com/paper/2302.06692/) (2023)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Learning with Language-Guided State Abstractions](https://mlsanity.com/paper/2402.18759/) (2024)
- [Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks](https://mlsanity.com/paper/1803.09698/) (2018)
- [A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems](https://mlsanity.com/paper/2204.00703/) (2022)
- [Goal-Oriented Communication, Estimation, and Control over Bidirectional Wireless Links](https://mlsanity.com/paper/2312.16061/) (2023)

## Built on by (most cited first, 6 of 6)

- [ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing](https://mlsanity.com/paper/2506.00576/) (2025)
- [Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing](https://mlsanity.com/paper/2506.00574/) (2025)
- [Verifying Memoryless Sequential Decision-making of Large Language Models](https://mlsanity.com/paper/2510.06756/) (2025)
- [Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization](https://mlsanity.com/paper/2602.13210/) (2026)
- [See Where You Read with Eye Gaze Tracking and Large Language Model](https://mlsanity.com/paper/2409.19454/) (2024)
- [Semantic-Driven AI Agent Communications: Challenges and Solutions](https://mlsanity.com/paper/2510.00381/) (2025)

## Abstract (arXiv)

This article investigates a control system within the context of six-generation wireless networks. The control performance optimization confronts the technical challenges that arise from the intricate interactions between communication and control sub-systems, asking for a co-design. Accounting for the system dynamics, we formulate the sequential co-design decision-makings of communication and control over the discrete time horizon as a Markov decision process, for which a practical offline learning framework is proposed. Our proposed framework integrates large language models into the elements of reinforcement learning. We present a case study on the age of semantics-aware communication and control co-design to showcase the potentials from our proposed learning framework. Furthermore, we discuss the open issues remaining to make our proposed offline learning framework feasible for real-world implementations, and highlight the research directions for future explorations.