相关论文: A Deep Reinforcement Learning Approach to Efficien…

To accomplish various tasks, safe and smooth control of unmanned aerial vehicles (UAVs) needs to be guaranteed, which cannot be met by existing ultra-reliable low latency communications (URLLC). This has attracted the attention of the…

In the field of autonomous Unmanned Aerial Vehicles (UAVs) landing, conventional approaches fall short in delivering not only the required precision but also the resilience against environmental disturbances. Yet, learning-based algorithms…

A novel framework is proposed for quality of experience (QoE)-driven deployment and dynamic movement of multiple unmanned aerial vehicles (UAVs). The problem of joint non-convex three-dimensional (3D) deployment and dynamic movement of the…

Deep reinforcement learning (DRL) has been successfully used to design forwarding strategies for multi-hop mobile wireless networks. While such strategies can be used directly for networks with varied connectivity and dynamic conditions,…

Unmanned aerial vehicles (UAVs) can be powerful Internet-of-Things components to execute sensing tasks over the next-generation cellular networks, which are generally referred to as the cellular Internet of UAVs. However, due to the high…

Swarms of drones are being more and more used in many practical scenarios, such as surveillance, environmental monitoring, search and rescue in hardly-accessible areas, etc.. While a single drone can be guided by a human operator, the…

This research proposes a new integrated framework for identifying safe landing locations and planning in-flight divert maneuvers. The state-of-the-art algorithms for landing zone selection utilize local terrain features such as slopes and…

In this paper, the deployment of drone base stations to provide higher performance in the cellular networks is analyzed. In particular, we investigate a new mobility model for drone base stations where they can move freely in the network,…

The exponential proliferation of mobile devices and data-intensive applications in future wireless networks imposes substantial computational burdens on resource-constrained devices, thereby fostering the emergence of over-the-air…

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…

The market size of Unmanned Aerial Vehicles (UAVs, a.k.a drones) can reach up to 10% of the global market value. In particular, drone delivery is one of the most attractive applications. The growing number of drones requires appropriate…

Deep Reinforcement Learning (DRL) has emerged as an efficient approach to resource allocation due to its strong capability in handling complex decision-making tasks. However, only limited research has explored the training of DRL models…

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…

The deployment of unmanned aerial vehicles (UAVs) in many different settings has provided various solutions and strategies for networking paradigms. Therefore, it reduces the complexity of the developments for the existing problems, which…

In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate. Two genie-aided heuristic search methods are employed to explore the solution space.…

In this paper, we present a deep reinforcement learning method for quadcopter bypassing the obstacle on the flying path. In the past study, the algorithm only controls the forward direction about quadcopter. In this letter, we use two…

Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia. A common solution is to replace partial or even all modules in the conventional systems, which is often lack of…

Deep reinforcement learning (DRL) has long been a promising solution for sequential resource management in wireless networks. However, conventional DRL methods are fundamentally limited by their reliance on unimodal policy distributions,…

This paper presents a deep reinforcement learning (DRL) solution for power control in wireless communications, describes its embedded implementation with WiFi transceivers for a WiFi network system, and evaluates the performance with…

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.…