Decision Transformer for UAV-Mounted RIS-Assisted Dynamic D2D Communications — Paper

Abstract

This paper studies unmanned aerial vehicle (UAV)-mouted reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication with stochastic link activation. It models UAV motion and attitude, time-varying Rician angles, and angle-dependent RIS reflection. A joint optimization of UAV trajectory, attitude, and RIS phases is formulated to maximize average sum rate under mobility, energy, and hardware constraints. The problem is addressed using deep reinforcement learning and a Decision Transformer trained on expert trajectories from multiple scenarios. Results demonstrate effective cross-scenario generalization, with zero-shot transfer outperforming direct DRL transfer and online fine-tuning achieving competitive performance with fewer interactions.

Authors 1

Yaxuan Liu

Specification

Official page: arxiv.org/abs/2609.09885
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
arXiv id: 2609.09885
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
Categories: cs.AI, cs.RO
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
PDF: arxiv.org/pdf/2609.09885
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
Primary category: cs.AI
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh

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Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

8 h ago

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None

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Authors: Yaxuan Liu

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Claim history · Abstract

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Abstractabstract1

Claim history for Abstract
Value	Valid from → to	Status	Source	Confidence	Extractor
This paper studies unmanned aerial vehicle (UAV)-mouted reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication with stochastic link activation. It models UAV motion and attitude, time-varying Rician angles, and angle-dependent RIS reflection. A joint optimization of UAV trajectory, attitude, and RIS phases is formulated to maximize average sum rate under mobility, energy, and hardware constraints. The problem is addressed using deep reinforcement learning and a Decision Transformer trained on expert trajectories from multiple scenarios. Results demonstrate effective cross-scenario generalization, with zero-shot transfer outperforming direct DRL transfer and online fine-tuning achieving competitive performance with fewer interactions.	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperDecision Transformer for UAV-Mounted RIS-Assisted Dynamic D2D Communications
New paper: Decision Transformer for UAV-Mounted RIS-Assisted Dynamic D2D Communications
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	6 h ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.