# Decision Transformers for Wireless Communications: A New Paradigm of Resource Management

arXiv: https://arxiv.org/abs/2404.05199 · HTML: https://mlsanity.com/paper/2404.05199/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2024.

## Built on by (most cited first, 15 of 15)

- [Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition](https://mlsanity.com/paper/2501.08007/) (2025)
- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocation](https://mlsanity.com/paper/2602.16174/) (2026)
- [Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach](https://mlsanity.com/paper/2411.14052/) (2024)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Conditional Diffusion Model-Driven Generative Channels for Double RIS-Aided Wireless Systems](https://mlsanity.com/paper/2506.12682/) (2025)
- [Transformer-Driven Neural Beamforming with Imperfect CSI in Urban Macro Wireless Channels](https://mlsanity.com/paper/2504.11667/) (2025)
- [ReaCritic: Reasoning Transformer-based DRL Critic-model Scaling For Wireless Networks](https://mlsanity.com/paper/2505.10992/) (2025)
- [Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers](https://mlsanity.com/paper/2606.04328/) (2026)
- [System One Models for Wireless Decision-Making:Applications and Performance Evaluation](https://mlsanity.com/paper/2610.04345/) (2026)
- [Embedded Mean Field Reinforcement Learning for Perimeter-defense Game](https://mlsanity.com/paper/2505.14209/) (2025)
- [Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks](https://mlsanity.com/paper/2606.05208/) (2026)
- [Hybrid Offline-Online Multi-Agent Decision Transformers for Wireless Resource Management](https://mlsanity.com/paper/2608.28878/) (2026)
- [WiSDoM: Wireless Sparse Decision Transformer with Mixture-of-Experts for Multi-Task Mobile Network Optimization](https://mlsanity.com/paper/2609.00284/) (2026)
- [Decision Transformer for UAV-Mounted RIS-Assisted Dynamic D2D Communications](https://mlsanity.com/paper/2609.09885/) (2026)

## Abstract (arXiv)

As the next generation of mobile systems evolves, artificial intelligence (AI) is expected to deeply integrate with wireless communications for resource management in variable environments. In particular, deep reinforcement learning (DRL) is an important tool for addressing stochastic optimization issues of resource allocation. However, DRL has to start each new training process from the beginning once the state and action spaces change, causing low sample efficiency and poor generalization ability. Moreover, each DRL training process may take a large number of epochs to converge, which is unacceptable for time-sensitive scenarios. In this paper, we adopt an alternative AI technology, namely, Decision Transformer (DT), and propose a DT-based adaptive decision architecture for wireless resource management. This architecture innovates through constructing pre-trained models in the cloud and then fine-tuning personalized models at the edges. By leveraging the power of DT models learned over offline datasets, the proposed architecture is expected to achieve rapid convergence with many fewer training epochs and higher performance in new scenarios with different state and action spaces, compared with DRL. We then design DT frameworks for two typical communication scenarios: intelligent reflecting surfaces-aided communications and unmanned aerial vehicle-aided mobile edge computing. Simulations demonstrate that the proposed DT frameworks achieve over 3-6 times speedup in convergence and better performance relative to the classic DRL method, namely, proximal policy optimization.