Jul 2026· IEEE International Conference on Consumer Electronics· pp. 635-640· 0 citations· 12 references
Abstract
In disaster scenarios where terrestrial communication infrastructure is compromised, Unmanned Aerial Vehicles (UAVs) provide a rapid solution for restoring connectivity. However, conventional trajectory planning methods often treat users uniformly, neglecting heterogeneous urgency requirements in emergency environments. This paper proposes an urgencyaware, Quality of Experience (QoE)-driven Deep Reinforcement Learning (DRL) framework for UAV trajectory optimization in multi-priority post-disaster networks. We introduce a configurable priority scaling factor that augments the standardized Mean Opinion Score (MOS) model to explicitly incorporate the influence of urgency on user satisfaction. Additionally, a minimum service-duration mechanism enforces sustained prioritization for critical users. By encoding urgency requirements directly into the satisfaction model, the proposed framework enables the learning-based policy to adaptively and flexibly optimize the trajectory under dynamic disaster conditions. Simulation results demonstrate significant improvements in responsiveness to critical users while maintaining overall network effectiveness.
Uncrewed aerial vehicle (UAV)-assisted networks provide a versatile paradigm for on-demand connectivity. However, in multi-operator aerial networks (MOANs), the joint optimization of cooperative resource sharing and 3D trajectory control to maintain information freshness is a complex combinatorial problem, which can be...
Atefeh Hajijamali Arani, M. Shirvanimoghaddam, A. Mehbodniya et al.· 0 citations
Traditional VANETs struggle with coverage gaps and routing instability due to high mobility and static infrastructure. This paper proposes a congestion-aware, UAV-assisted framework integrating Predictive K-Means clustering for deployment and Deep Q-Network (DQN) routing with Conservative Q-Learning (CQL). The deployme...
Prangya Priyadarshini, Arun Kumar, P. H. J. Chong· IEEE International Symposium...· 0 citations
This work proposes Priority- and Outage-Guided Safe QMIX (POGS-QMIX), a hybrid hierarchical framework in which a centralized online coordinator forms conflict-reduced UAV–user targets from fleet-wide priority and outage information, while parameter-shared QMIX agents independently choose target-conditioned low-level ac...
Hao-Yu Mei, Cheng-Tao Xu, Ruo-Zhe Li et al.· Drones· 0 citations
This paper investigates an energy efficiency maximization problem in a UAV-assisted WPCN by jointly optimizing the UAV trajectory, hovering altitude, and hybrid TDMA/NOMA resource allocation and proposes a deep reinforcement learning-driven Hierarchical Energy-Efficient Data Collection scheme, which is named DRL-HEEC.
Si-Liang Gong, Kai-Yang Qu, Qi-Sen Wang et al.· Electronics· 0 citations
An efficient framework to maximize global energy efficiency (EE) while promoting safe operation through threat-aware clustering and reward-based safety enforcement and demonstrates effective generalization to unseen user distributions, large UAV fleets, and different threat geometries, while maintaining zero safety vio...
F. Al-kamali, Hussein A. Ammar, François Chan et al.· IEEE Internet of Things Jour...· 0 citations
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