Deep-EERA: DRL-based Energy-Efficient Resource Allocation in UAV-Empowered Beyond
5G Networks Page 1 TSINGHUA SCIENCE AND TECHNOLOGY DOI: 10.26599/TST.2022.90100 …

Unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) communications can be
deployed flexibly thanks to UAVs' agility. By exploiting the direct D2D interaction supported …

Cellular-connected unmanned aerial vehicles (UAVs) are expected to become integral
components of future cellular networks. To this end, one of the important problems to …

This paper presents an energy-aware task execution scheme for a network of multiple
Unmanned Aerial Vehicles (UAVs). In this work, we prioritize the forward movement of UAV …

Unmanned aerial vehicles (UAVs) serving as aerial base stations can be deployed to
provide wireless connectivity to mobile users, such as vehicles. However, the density of …

In this paper, we propose a novel joint deep reinforcement learning (DRL)-based solution to
optimize the utility of an uncrewed aerial vehicle (UAV)-assisted communication network. To …

Resource allocation is vitally important for energy harvesting-powered unmanned aerial
vehicle (UAV)-assisted device-to-device (D2D) networks. However, most of the existing …

Unmanned Aerial Vehicles (UAVs) promise to become an intrinsic part of next generation
communications, as they can be deployed to provide wireless connectivity to ground users …

This paper considers an unmanned aerial vehicle (UAV) assisted internet of things (IoT)
network. In order to prolong the lifetime of power-limited IoT devices (IDs), it is assumed that …

ABSTRACT Unmanned Aerial Vehicles (UAVs) offer a strategic solution to address the
increasing demand for cellular connectivity in rural, remote, and disaster-hit regions lacking …