Related papers: Efficient Drone Mobility Support Using Reinforceme…
Recently, the unmanned aerial vehicles (UAVs) have been widely used in real-time sensing applications over cellular networks, which sense the conditions of the tasks and transmit the real-time sensory data to the base station (BS). The…
Unmanned Aerial Vehicles (UAVs) are increasingly used in automated inspection, delivery, and navigation tasks that require reliable autonomy. This project develops a reinforcement learning (RL) approach to enable a single UAV to…
Drones are becoming increasingly used in a wide variety of industries and services and are delivering profound socioeconomic benefits. Technology needs to be in place to ensure safe operation and management of the growing fleet of drones.…
Wireless powered mobile-edge computing (MEC) has recently emerged as a promising paradigm to enhance the data processing capability of low-power networks, such as wireless sensor networks and internet of things (IoT). In this paper, we…
Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique…
Unmanned Aerial Vehicles (UAVs) have attracted considerable research interest recently. Especially when it comes to the realm of Internet of Things, the UAVs with Internet connectivity are one of the main demands. Furthermore, the energy…
This paper presents a reinforcement learning (RL) based approach for path planning of cellular connected unmanned aerial vehicles (UAVs) operating beyond visual line of sight (BVLoS). The objective is to minimize travel distance while…
The consideration of mobile networks as a communication infrastructure for unmanned aerial vehicles (UAVs) creates a new plethora of emerging services and opportunities. In particular, the availability of different mobile network operators…
With users demanding seamless connectivity, handovers (HOs) have become a fundamental element of cellular networks. However, optimizing HOs is a challenging problem, further exacerbated by the growing complexity of mobile networks. This…
Communication networks able to withstand hostile environments are critically important for disaster relief operations. In this paper, we consider a challenging scenario where drones have been compromised in the supply chain, during their…
Fast and reliable wireless communication has become a critical demand in human life. In the case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing ubiquitous connectivity becomes challenging by using…
With the development of industry, drones are appearing in various field. In recent years, deep reinforcement learning has made impressive gains in games, and we are committed to applying deep reinforcement learning algorithms to the field…
In this work, we evaluate the use of aerial drone hover constraints in a multisensor fusion of ground robot and drone data to improve the localization performance of a drone. In particular, we build upon our prior work on cooperative…
The unmanned aerial vehicle (UAV) plays an vital role in various applications such as delivery, military mission, disaster rescue, communication, etc., due to its flexibility and versatility. This paper proposes a deep reinforcement…
Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical…
Unmanned aerial vehicles (UAVs) technique has been recognized as a promising solution in future wireless connectivity from the sky, and UAV navigation is one of the most significant open research problems, which has attracted wide interest…
The growing threat of low-cost kamikaze drone swarms poses a critical challenge to modern defense systems demanding rapid and strategic decision-making to prioritize interceptions across multiple effectors and high-value target zones. In…
Learning to control robots without requiring engineered models has been a long-term goal, promising diverse and novel applications. Yet, reinforcement learning has only achieved limited impact on real-time robot control due to its high…
In this paper, the problem of drone-assisted collaborative learning is considered. In this scenario, swarm of intelligent wireless devices train a shared neural network (NN) model with the help of a drone. Using its sensors, each device…
Drones are vital for urban emergency search and rescue (SAR) due to the challenges of navigating dynamic environments with obstacles like buildings and wind. This paper presents a method that combines multi-objective reinforcement learning…