Related papers: Reinforcement Learning in Multiple-UAV Networks: D…
In this article, we investigate the content distribution in the hotspot area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular…
As artificial intelligence continues to drive innovation in complex, decentralized environments, the need for scalable, adaptive, and privacy-preserving decision-making systems has become critical. This paper introduces a novel framework…
Unmanned Aerial Systems (UAS) are being increasingly deployed for commercial, civilian, and military applications. The current UAS state-of-the-art still depends on a remote human controller with robust wireless links to perform several of…
In upcoming 6G networks, unmanned aerial vehicles (UAVs) are expected to play a fundamental role by acting as mobile base stations, particularly for demanding vehicle-to-everything (V2X) applications. In this scenario, one of the most…
Unmanned aerial vehicle (UAV) based aerial base stations (BSs) can provide rapid communication services to ground users and are thus promising for future communication systems. In this paper, we consider a scenario where no functional…
The integrated use of non-terrestrial network (NTN) entities such as the high-altitude platform station (HAPS) and low-altitude platform station (LAPS) has become essential elements in the space-air-ground integrated networks (SAGINs).…
Decentralized learning empowers wireless network devices to collaboratively train a machine learning (ML) model relying solely on device-to-device (D2D) communication. It is known that the convergence speed of decentralized optimization…
Unmanned aerial vehicle (UAV)-based base stations offer a promising solution in emergencies where the rapid deployment of cutting-edge networks is crucial for maximizing life-saving potential. Optimizing the strategic positioning of these…
In this paper we study the deployment of multiple unmanned aerial vehicles (UAVs) to form a temporal UAV network for the provisioning of emergent communications to affected people in a disaster zone, where each UAV is equipped with a…
The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches. Despite the abundance of such advantages, it is imperative…
The deployment flexibility and maneuverability of Unmanned Aerial Vehicles (UAVs) increased their adoption in various applications, such as wildfire tracking, border monitoring, etc. In many critical applications, UAVs capture images and…
Harvesting data from distributed Internet of Things (IoT) devices with multiple autonomous unmanned aerial vehicles (UAVs) is a challenging problem requiring flexible path planning methods. We propose a multi-agent reinforcement learning…
Unmanned Aerial Vehicles (UAVs) have been emerging as an effective solution for IoT data collection networks thanks to their outstanding flexibility, mobility, and low operation costs. However, due to the limited energy and uncertainty from…
This paper presents a learning-augmented trajectory planning framework for cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) handover missions. While centralized trajectory optimization ensures dynamic feasibility…
This paper considers an unmanned aerial vehicle enabled-up link non-orthogonal multiple-access system, where multiple mobile users on the ground send independent messages to a unmanned aerial vehicle in the sky via non-orthogonal…
Unmanned Aerial Vehicles (UAVs), autonomously-guided aircraft, are widely used for tasks involving surveillance and reconnaissance. A version of the pursuit-evasion problems centered around UAVs and its variants has been extensively studied…
Unmanned Aerial Vehicles (UAVs) increasingly enhance the Quality of Service (QoS) in wireless networks due to their flexibility and cost-effectiveness. However, optimizing UAV placement in dynamic, obstacle-prone environments remains a…
This work presents a study on parallel and distributional deep reinforcement learning applied to the mapless navigation of UAVs. For this, we developed an approach based on the Soft Actor-Critic method, producing a distributed and…
We present a resilient deep neural network (DNN) framework for decentralized transport and coverage using uncrewed aerial systems (UAS) operating in $\mathbb{R}^n$. The proposed DNN-based mass-transport architecture constructs a layered…
In this paper, an unmanned aerial vehicle (UAV)-assisted wireless network is considered in which a battery-constrained UAV is assumed to move towards energy-constrained ground nodes to receive status updates about their observed processes.…