Decision Transformers for Wireless Communications: A New Paradigm of Resource Management
Jie Zhang, Jun Li, Long Shi, Zhe Wang, Shi Jin, Wen Chen, and H., Vincent Poor

TL;DR
This paper introduces a Decision Transformer-based architecture for wireless resource management, enabling faster adaptation and better performance in dynamic environments compared to traditional deep reinforcement learning methods.
Contribution
It proposes a novel DT-based adaptive decision architecture with cloud pre-training and edge fine-tuning for efficient wireless resource management.
Findings
Achieves 3-6 times faster convergence than DRL.
Demonstrates improved performance in wireless scenarios.
Validates effectiveness in UAV and IRS-aided communications.
Abstract
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…
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Taxonomy
TopicsDistributed and Parallel Computing Systems
MethodsAttention Is All You Need · Linear Layer · Dense Connections · Label Smoothing · Residual Connection · Dropout · Multi-Head Attention · Adam · Softmax · Layer Normalization
