UAV Communication Research Articles

Three-dimensional deep reinforcement learning for trajectory and resource optimization in UAV communication systems

In this paper, we focus on trajectory optimization for multiple three-dimensional unmanned aerial vehicles (UAVs) serving as aerial base stations (BS) to provide wireless coverage for Internet of Things (IoT) devices. These IoT devices are randomly distributed in a three-dimensional region with unknown locations and channel parameters. By optimizing the UAV trajectory and power, we aim to achieve the minimum communication time for all IoT devices. However, the unknown flight time of the UAVs and the IoT device locations make it difficult to formulate an optimization problem between the objective and variables by using traditional convex optimization. To deal with this intractable problem, We propose a solution based on Hierarchical Reinforcement Learning (HRL), specifically modeling it as a Markov Decision Process (MDP) and we divide the training process into two parts, each utilizing the Proximal Policy Optimization (PPO) algorithm for training. Our simulation results show that the proposed HRL offers promising performance for both training and testing phases.

IRS Auxiliary UAV Communications: Channel Modeling and Performance Analysis

IRS Auxiliary UAV Communications: Channel Modeling and Performance Analysis

Outage Performance of Multitier UAV Communication With Random Beam Misalignment

Outage Performance of Multitier UAV Communication With Random Beam Misalignment

Generalized Optimization Method of Placement and Beamforming Design for Multiuser MIMO UAV Communications

Generalized Optimization Method of Placement and Beamforming Design for Multiuser MIMO UAV Communications

Enhancing Reliability in Multimodal UAV Communication Based on Opportunistic Task Space

Enhancing Reliability in Multimodal UAV Communication Based on Opportunistic Task Space

Trajectory Design and Bandwidth Allocation Considering Power-Consumption Outage for UAV Communication: A Machine Learning Approach

Trajectory Design and Bandwidth Allocation Considering Power-Consumption Outage for UAV Communication: A Machine Learning Approach

Reinforcement Learning-Based Resource Allocation for Coverage Continuity in High Dynamic UAV Communication Networks

Reinforcement Learning-Based Resource Allocation for Coverage Continuity in High Dynamic UAV Communication Networks

Covert UAV Communication by Exploiting Imperfect Cancellation Under Channel Uncertainty

Covert UAV Communication by Exploiting Imperfect Cancellation Under Channel Uncertainty

A hyperspectral pansharpening method using retrain transformer network for remote sensing images in UAV communications system

A hyperspectral pansharpening method using retrain transformer network for remote sensing images in UAV communications system

Application of multi-source remote sensing technology in UAV communication control system

Abstract Unmanned aerial vehicles (UAVs) are crucial in various sectors but face challenges in communication reliability. This paper introduces a cutting-edge UAV communication control system, designed using time-series synthetic aperture radar interferometry (InSAR) technology and support vector machine (SVM) classification. Our findings reveal exceptional communication link performance across multiple flight scenarios, notably achieving a bit error rate (BER) of 0.001% in cruising mode. Additionally, the system demonstrated remarkable reception sensitivity, closely aligning with theoretical predictions at −81.1 dBm. This innovative approach utilizes multi-source remote sensing to significantly enhance UAV communication, offering a novel strategy for addressing UAV communication dilemmas.

3D Placement Optimization in UAV-Enabled Communications: A Systematic Mapping Study

3D Placement Optimization in UAV-Enabled Communications: A Systematic Mapping Study

A Geometry-Based Modelling Approach for the Line-of-Sight Probability in UAV Communications

A Geometry-Based Modelling Approach for the Line-of-Sight Probability in UAV Communications

UAV Communications with Dipole Antennas

UAV Communications with Dipole Antennas

UAV Communication Against Intelligent Jamming: A Stackelberg Game Approach With Federated Reinforcement Learning

UAV Communication Against Intelligent Jamming: A Stackelberg Game Approach With Federated Reinforcement Learning

An Enhanced Path Loss Prediction Approach for the A2A Channel in UAV Communications

An Enhanced Path Loss Prediction Approach for the A2A Channel in UAV Communications

Population-Invariant MADRL for AoI-Aware UAV Trajectory Design and Communication Scheduling in Wireless Sensor Networks

Population-Invariant MADRL for AoI-Aware UAV Trajectory Design and Communication Scheduling in Wireless Sensor Networks

Delay-Aware UAV Computation Offloading and Communication Assistance for Post-disaster Rescue

Delay-Aware UAV Computation Offloading and Communication Assistance for Post-disaster Rescue

Channel Modeling and Performance Analysis of UAV-Enabled Communications With UAV Wobble

Channel Modeling and Performance Analysis of UAV-Enabled Communications With UAV Wobble

Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks

Unmanned aerial vehicle (UAV) network offers a variety of applications in public safety, disaster management, advertising and broadcasting, overload situation, etc. Due to the dynamic characteristics of MU, it is challenging to provide robust transmission services to mobile users (MU). Resource allocation (RA), including sub-channel, serving user, and transmit power, is a crucial problem; also, it is critical to enhance the coverage and energy efficiency of UAV-enabled communication protocol. Furthermore, system resources are limited (for example, spectrum, and transmission power) and UAV transmission coverage and on-board energy are limited. In order to meet the QoE of any user with limited UAV energy and limited resource system, we jointly enhance UAV trajectory, user communication scheduling, and bandwidth allocation and transmit power to satisfy user QoE requirements and increase energy efficiency. Thus, the study proposes a new mud ring optimization with deep belief network-based resource allocation scheme (MRODBN-RAS) technique for UAV-enabled wireless networks. The proposed MRODBN-RAS approach focuses on the effectual accomplishment of the computational and energy-effective decision. Besides, the MRODBN-RAS technique assumed the UAV as a learning agent by forming RA decisions as actions. In addition, the MRODBN-RAS technique designed a reward function to reduce the weighted resource utilization. The MRODBN-RAS technique uses DBN model with hyperparameter tuning using MRO algorithm to allocate the resources. The design of the MRO algorithm helps in the optimal selection of the hyperparameter related to the DBN model. The simulation results of the MRODBN-RAS method are examined under various measures. The extensive comparison study highlighted the better performance of the MRODBN-RAS approach over existing techniques.

A Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communication

A Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communication