arXiv:2603. 18853v3 Announce Type: replace-cross Abstract: Autonomous aerial vehicles (AAVs) enable data collection for sixth-generation Internet-of-Things networks, but their trajectories couple nonlinear wireless rates with long-horizon service progress.
arXiv:2606. 16331v1 Announce Type: new Abstract: The integration of generative artificial intelligence with wireless communication and signal processing systems has opened new avenues for intelligent, data-driven decision-making in future 6G networks.
arXiv:2606. 24483v1 Announce Type: cross Abstract: The deployment of unmanned aerial vehicles (UAV) as open radio units (O-RUs) in 6G cellular systems presents a promising opportunity to achieve scalable and adaptive network coverage.
The paper proposes a hierarchical Open Radio Access Network (O‑RAN) control framework for tethered millimeter‑wave UAV‑mounted 5G base stations (gNBs). A Non‑Real‑Time RIC application jointly manages UAV placement and slice budgets, while a Near‑Real‑Time RIC application allocates per‑user resources using a permutation‑equivariant DeepSets Soft Actor‑Critic scheduler. This two‑level controller improves eMBB service‑level agreement satisfaction by up to 17 % and URLLC on‑time delivery by up to 42 % compared with conventional schedulers.
arXiv:2507. 19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing.
arXiv:2602. 01131v2 Announce Type: replace Abstract: With the rapid expansion of the low-altitude economy, Unmanned Aerial Vehicles (UAVs) serve as pivotal aerial base stations supporting diverse services from users, ranging from latency-sensitive critical missions to bandwidth-intensive data streaming.
arXiv:2606. 03664v1 Announce Type: cross Abstract: Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical edge networking, and unmanned-system control.
The paper proposes an adaptive multi‑discriminator Wasserstein GAN (MD‑WGAN) framework for Internet of Vehicles (IoV) that combines reinforcement learning and game‑theoretic coordination to manage resource allocation for distributed training. Roadside units host generators with Deep Q‑Network agents that select discriminator subsets and coordinate training epochs, while a unified objective links adversarial quality to resource efficiency, communication overhead, and latency. Experiments on real‑world NGSIM trajectory data show comparable prediction accuracy to state‑of‑the‑art GANs, with a 28% reduction in CPU usage and 6% reduction in memory usage, while maintaining competitive communication overhead and latency.
The paper introduces a hierarchical hybrid architecture combining large language models (LLMs) and multi-agent reinforcement learning (MARL) to manage heterogeneous unmanned aerial systems in low‑altitude wireless networks (LAWNs). An outer LLM‑driven loop interprets service requirements and operator intent to reconfigure objectives and resource priorities, while an inner MARL loop executes decentralized policies under the updated game. A logistics‑monitoring case study demonstrates the framework’s ability to coordinate diverse services and adapt to changing conditions without retraining the MARL policies.
arXiv:2608. 04590v1 Announce Type: new Abstract: The growing deployment of delay-tolerant networks (DTNs) has made store-carry-forward (SCF) communication indispensable under sparse connectivity.
arXiv:2610.09226v1 Announce Type: cross Abstract: Data collection using Unmanned Aerial Vehicles (UAVs) is challenging when LoRa IoT Devices (IoTDs) duty-cycle to conserve battery. Under energy const...
arXiv:2609.36845v1 Announce Type: cross Abstract: Uncrewed aerial vehicle base stations (UAV-BSs) are expected to cover traffic demand that shifts across space and time, yet most repositioning scheme...
arXiv:2609.14246v1 Announce Type: cross Abstract: In wireless federated learning (FL), data heterogeneity and multiple local updates induce client drift, degrading model convergence. It is further af...