Attention-Enhanced Prompt Decision Transformers for UAV-Assisted Communications with AoI
Chi Lu, Yiyang Ni, Zhe Wang, Xiaoli Shi, Jun Li, Shi Jin

TL;DR
This paper introduces an attention-enhanced prompt Decision Transformer (APDT) for UAV-assisted IoT networks, improving trajectory planning and user scheduling to reduce AoI and energy constraints more efficiently than traditional methods.
Contribution
The paper proposes an innovative APDT framework that integrates attention mechanisms and prompt adaptation for better scenario generalization and energy management in UAV communications.
Findings
APDT converges twice as fast as conventional DT.
APDT reduces average AoI by 8%.
The framework effectively manages long-term energy constraints.
Abstract
Decision Transformer (DT) has recently demonstrated strong generalizability in dynamic resource allocation within unmanned aerial vehicle (UAV) networks, compared to conventional deep reinforcement learning (DRL). However, its performance is hindered due to zero-padding for varying state dimensions, inability to manage long-term energy constraint, and challenges in acquiring expert samples for few-shot fine-tuning in new scenarios. To overcome these limitations, we propose an attention-enhanced prompt Decision Transformer (APDT) framework to optimize trajectory planning and user scheduling, aiming to minimize the average age of information (AoI) under long-term energy constraint in UAV-assisted Internet of Things (IoT) networks. Specifically, we enhance the convenional DT framework by incorporating an attention mechanism to accommodate varying numbers of terrestrial users, introducing a…
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Taxonomy
TopicsAge of Information Optimization · UAV Applications and Optimization · IoT and Edge/Fog Computing
MethodsAttention Is All You Need · Linear Layer · Layer Normalization · Byte Pair Encoding · Residual Connection · Dense Connections · Softmax · Position-Wise Feed-Forward Layer · Absolute Position Encodings · Label Smoothing
