Unmanned Aerial Vehicle Base Stations Research Articles

Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle Communications.

Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate of the system by using interference technology to enhance it. To this end, the system not only deploys multiple UAV base stations (BSs) to provide services to legitimate users but also assigns dedicated UAV jammers to send interference signals to active or potential eavesdroppers to disrupt their eavesdropping effectiveness. Based on this configuration, we formulate the optimization process of parameters such as the user association variables, UAV trajectory, and output power as a sequential decision-making problem and use the single-agent soft actor-critic (SAC) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm to achieve joint optimization of the core parameters. In addition, for specific scenarios, we also use the multi-agent soft actor-critic (MASAC) algorithm to solve the joint optimization problem mentioned above. The numerical results show that the normalized average secrecy rate of the MASAC algorithm increased by more than 6.6% and 14.2% compared with that of the SAC and TD3 algorithms, respectively.

Research on path planning in UAV-assisted emergency communication

The rapid development of UAV communication technology makes it have application potential in wireless systems. However, for the optimization problem of UAV base station providing communication for ground personnel in the disaster area under special natural disasters, traditional deep reinforcement learning is used. Algorithms cannot solve such problems well. This article proposes the AD3QN algorithm combined with the attention mechanism, which can communicate with disaster victims on the ground better and more quickly, providing better information collection for rescue missions and completing the task of communication optimization. In the mission simulation environment, in order to make the environment more realistic, we innovatively designed the ground user distribution model, air-to-ground communication model, and electromagnetic interference model. Finally, the effect of the proposed algorithm is evaluated by comparing different deep reinforcement learning algorithms. The results show that the algorithm we proposed can provide communication services faster and has higher practicability in terms of algorithm.

Joint placement and communication optimization of uav base stations in GPS-denied environments

Joint placement and communication optimization of uav base stations in GPS-denied environments

Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems

Utilizing unmanned aerial vehicles (UAVs) as mobile access points or base stations has emerged as a promising solution to address the excessive traffic demands in wireless networks. This paper investigates improving the detector performance at the unmanned aerial vehicle base stations (UAV-BSs) in an uplink grant-free non-orthogonal multiple access (GF-NOMA) system by considering the activity state (AS) temporal correlation of the different user equipments (UEs) in the time domain. The Bernoulli Gaussian-Markov chain (BG-MC) probability model is used for exploiting both the sparsity and slow change characteristic of the AS of the UE. The GAMP Bernoulli Gaussian-Markov chain (GAMP-BG-MC) algorithm is proposed to improve the detector performance, which can utilize the bidirectional message passing between the neighboring time slots to fully exploit the temporally correlated AS of the UE. Furthermore, the parameters of the BG-MC model can be updated adaptively during the estimation procedure with unknown system statistics. Simulation results show that the proposed algorithm can improve the detection accuracy compared to existing methods while keeping the same order complexity.

Optimization of UAV base station placement for D2D content delivery network

Optimization of UAV base station placement for D2D content delivery network

Estimation Formula of Unmanned Aerial Vehicle Base Stations Density for Required Line-of-Sight Probability in Urban Environments

Estimation Formula of Unmanned Aerial Vehicle Base Stations Density for Required Line-of-Sight Probability in Urban Environments

Joint 3D Deployment and Beamforming for RSMA-Enabled UAV Base Station With Geographic Information

Joint 3D Deployment and Beamforming for RSMA-Enabled UAV Base Station With Geographic Information

Content Delivery Performance Analysis of a Cache-Enabled UAV Base Station Assisted Cellular Network for Metaverse Users

Content Delivery Performance Analysis of a Cache-Enabled UAV Base Station Assisted Cellular Network for Metaverse Users

A stochastic geometry-based performance analysis of a UAV corridor-assisted IoT network

The exploitation of unmanned aerial vehicles (UAVs) in enhancing network performance in the context of beyond-fifth-generation (5G) communications has shown a variety of benefits compared to terrestrial counterparts. In addition, they have been largely conceived to play a central role in data dissemination to Internet of Things (IoT) devices. In the proposed work, a novel stochastic geometry unified framework is proposed to study the downlink performance in a UAV-assisted IoT network that integrates both UAV-base stations (UAV-BSs) and terrestrial IoT receiving devices. The framework builds upon the concept of the aerial UAV corridor, which is modeled as a finite line above the IoT network, and the one-dimensional (1D) binomial point process (BPP) is employed for modeling the spatial locations of the UAV-BSs in the aerial corridor. Subsequently, a comprehensive SNR-based performance analysis in terms of coverage probability, average rate, and energy efficiency is conducted under three association strategies, namely, the nth nearest-selection scheme, the random selection scheme, and the joint transmission coordinated multi-point (JT-CoMP) scheme. The numerical results reveal valuable system-level insights and trade-offs and provide a firm foundation for the design of UAV-assisted IoT networks.

UAV Base Station Network Transmission-Based Reverse Auction Mechanism for Digital Twin Utility Maximization

UAV Base Station Network Transmission-Based Reverse Auction Mechanism for Digital Twin Utility Maximization

Semantic-AI based Trajectory Design of Multiple UAV Base Stations in Sparse and Mobile User Environments

Semantic-AI based Trajectory Design of Multiple UAV Base Stations in Sparse and Mobile User Environments

Impact of Dust and Sandstorms on 6G UAV Base Station Performance in Arid Saudi Arabian Environments

Impact of Dust and Sandstorms on 6G UAV Base Station Performance in Arid Saudi Arabian Environments

Layerwise Quantum Deep Reinforcement Learning for Joint Optimization of UAV Trajectory and Resource Allocation

This study proposes a layerwise quantum-based deep reinforcement learning (LQ-DRL) method for optimizing continuous large space and time series problems using deep layer training. The actions in LQ-DRL are optimized using a layerwise quantum embedding that leverages the advantages of quantum computing to maximize reward and reduce training loss. Moreover, this study employs a local loss to minimize the occurrence of barren plateaus phenomena and further enhance performance. As a particular case, the proposed scheme is employed to jointly optimize: (1) UAV trajectory planning, (2) user grouping, and (3) power allocation for higher energy efficiency of a UAV as the reward. The combination of these optimized factors is referred to as action space in the presented LQ-DRL. The LQ-DRL is employed to solve the optimization problem due to its non-convexity, continuous and large action space, and time-series domain. In a practical view, LQ-DRL aims to solve the issue of energy consumption related to limited-battery energy of a UAV base station while maintaining quality-of-service (QoS) for users, by gaining maximum energy efficiency as the reward. One of real applications, as an example, LQ-DRL can be employed to maximize the energy efficiency of a UAV base station in UAV empowered disaster recovery networks scenario. The quantum circuits of layerwise quantum embedding are presented to show the practical implementation in noisy intermediate-scale quantum computers. Based on the results, LQ-DRL outperformed the classical DRL by achieving higher effective dimension, rewards, and lower learning losses. In addition, better performances were achieved using more layers.

HiMAQ: Hierarchical multi-agent Q-learning-based throughput and fairness improvement for UAV-Aided IoT networks

Recently, various types of Internet of Things (IoT) services have become wide spread, and new types of IoT devices are emerging. However, the significant number of high-rise buildings in urban environments renders it difficult to provide seamless and high-speed network connectivity consistently to these IoT devices via existing terrestrial IoT networks. Therefore, we herein consider unmanned aerial vehicle (UAV)-aided IoT networks, in which a ground base station (GBS) and a UAV base station (UBS) coexist. In this paper, a hierarchical multi-agent Q-learning (HiMAQ) framework is proposed to maximize the system throughput while minimizing device outage in UAV-aided IoT networks. The proposed HiMAQ adopts distributed multi-agent inner-loop reinforcement learning (RL) for determining the optimal transmission power of UBSs and GBSs, and distributed outer-loop RL to determine the optimal UBS deployment. This hierarchical RL architecture can reduce the computational complexity of the proposed RL approach compared with the centralized RL approach. The performance of HiMAQ in various network conditions is demonstrated considering the mobility of IoT devices and even/uneven spatial traffic distributions. Simulation results show that the proposed HiMAQ can maximize the system throughput and minimize the number of outage devices compared with various benchmark algorithms.

Coverage Performance Analysis of a Cache-Enabled UAV Base Station Assisted Cellular Network

Unmanned Aerial Vehicle base stations (UBSs) can be used to assist a ground cellular network to enhance its network services for cellular users. This paper studies the coverage performance analysis of a cache-enabled UBS-assisted cellular network. Analytical models are derived for investigating the overall coverage probability of the network and the average achievable rate of a cellular user. In deriving the analytical models, an air-to-ground (A2G) channel model with both a line-of-sight (LoS) link and a non-line-of-sight (NLoS) link, a BS association strategy based on the strongest average received power, and a cache model with a probabilistic caching strategy are considered. The cache hit probability of UBSs based on the cache model is also taken into consideration. Moreover, the association probabilities with the association strategy are derived for different types of base stations. The derived analytical models are validated through simulation results and the impacts of system parameters on the coverage performance of the network are investigated through numerical results. Compared with existing relevant work, the novelty of this work is that the effects of both an access link and a backhaul link are taken into account in the coverage performance analysis. The obtained results can provide theoretical guidance for the deployment of UBSs in a cache-enabled UBS-assisted cellular network.

Dynamic redeployment of UAV base stations in large-scale and unreliable environments

The deployment of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BSs) has emerged as a promising solution to enhance communication services provided to ground users. However, deploying UAV-BSs faces challenges including the cooperation of multiple UAVs, dynamic user distribution, low-reliability issues of UAVs, and efficient redeployment in large environments. Existing literature addresses some of these challenges but lacks a comprehensive approach. In this paper, we investigate all these challenges and propose a novel deployment framework with the objective of maximizing the quality of communication service by dynamically deploying UAV-BSs. The proposed framework employs a decentralized approach, allowing UAV-BSs to locally adjust their locations and rapidly respond to changes, thus ensuring stable and efficient service supply to ground users. In addition, we implement multiple simulation experiments to evaluate the performance of the proposed framework in solving the UAV-BSs redeployment problem in large and dynamic environments. The results demonstrate its ability to effectively improve the quality of communication services.

Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications

The collaborative deployment of multiple UAVs is a crucial issue in UAV-supported disaster emergency communication networks, as utilizing these UAVs as air base stations can greatly assist in restoring communication networks within disaster-stricken areas. In this paper, the problem of rapid deployment of randomly distributed UAVs in disaster scenarios is studied, and a distributed rapid deployment method for UAVs´ emergency communication network is proposed; this method can cover all target deployment points while maintaining connectivity and provide maximum area coverage for the emergency communication network. To reduce the deployment complexity, we decoupled the three-dimensional UAV deployment problem into two dimensions: vertical and horizontal. For this small-area deployment scenario, a small area UAVs deployment improved-Broyden–Fletcher–Goldfarb–Shanno (SAIBFGS) algorithm is proposed via improving the Iterative step size and search direction to solve the high computational complexity of the traditional Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. In a large area deployment scenario, aiming at the problem of the premature convergence of the standard genetic algorithm (SGA), the large-area UAVs deployment elitist strategy genetic algorithm (LAESGA) is proposed through the improvement of selection, crossover, and mutation operations. The adaptation function of connectivity and coverage is solved by using SAIBFGS and LAESGA, respectively, in the horizontal dimension to obtain the optimal UAV two-dimensional deployment coordinates. Then, the transmitting power and height of the UAV base station are dynamically adjusted according to the channel characteristics and the discrete coefficients of the ground users to be rescued in different environments, which effectively improves the power consumption efficiency of the UAV base station and increases the usage time of the UAV base station, realizing the energy-saving deployment of the UAV base station. Finally, the effectiveness of the proposed method is verified via data transmission rate simulation results in different environments.

Adaptive Beam Alignment and Optimization for IRS-Aided High-Speed UAV Communications

In the unmanned aerial vehicle (UAV) enabled wireless communication system, the rapid channel change caused by high-speed UAV may cause the beam misalignment problem. It is a challenge to fast establish and maintain the communications link between UAV and ground base stations. To address this challenge, an IRS-aided high-speed UAV adaptive beam alignment (IRS-UAV-ABA) scheme utilizing double-layer long short-term memory (DL-LSTM) spatial angle prediction model is proposed to improve the effectiveness and reliability of the UAV communication. Firstly, DL-LSTM enabled spatial angle prediction is modeled to facilitate the calculation of the beam coverage adapted to the UAV real-time flight trajectory. Secondly, the non-convex optimization problem for maximizing the received signal-to-noise ratio of moving UAV is formulated to obtain the suboptimal solutions of the BS’ beamforming vector and the IRS’ phase shift matrix. Finally, with the aforementioned beamforming vectors, the phase shift matrix and the adaptive beam coverage, the adaptive dual-beams alignment from BS to UAV and from IRS to UAV can be achieved by the proposed DL-LSTM-enabled IRS-UAV-ABA algorithm. Simulation results verify that the proposed scheme not only can effectively avoid the beam misalignment caused by the mobility and perturbation of UAV, but also has superior spectrum efficiency and stable beamforming gain.

Energy-Efficient Ground Base Station Antenna Array System for Wireless Back-Hauling and Two State Charging of Drone Base Stations

A ground base station (GBS) antenna array system (AAS) geometry and feed technique are proposed for 360 degree azimuth beam scan, elevation beam scan, azimuth beam-width control, elevation beam-width control, and in existing AASs these four capabilities do not coexist. Firstly, the results confirm that the number of active radio-frequency (RF) switches in the proposed AAS feed network is reduced by more than 99 %, and the AAS achieves a power consumption reduction of up to 50 % without fully connected (FC) architecture and 35 % with FC architecture compared to the existing AAS. Also, an independent GBS AAS azimuth and elevation plane beam-width control can be achieved from 65 to 0.01 degrees which makes it suitable for GBS-assisted wireless back-hauling plus wireless charging of unmanned aerial vehicle base stations (UAV-BSs), dynamic GBS sectorisation order control, and millimeter wave beam tracking. Also, a parallel concurrent UAV-BS battery charging configuration with power source swap capability is proposed to charge UAV-BS using the GBS back-haul and onboard power amplifier (PA) system as the power sources. The results show that the proposed charging configuration improves hovering time by 180.7 % compared to the existing onboard PA system configuration for battery charging. Furthermore, the proposed charging configuration can recharge the battery of the inactive state UAV-BS to increase the state of charge of two 16 Ampere hour batteries to 80 % in 180.13 minutes, and UAV-BS’s inactive state charging was not proposed in the existing onboard PA system configuration for UAV-BS’s battery charging.

Optimal Trajectories of a UAV Base Station Using Hamilton-Jacobi Equations

We consider the problem of optimizing the trajectory of an Unmanned Aerial Vehicle (UAV). Assuming a traffic intensity map of users to be served, the UAV must travel from a given initial location to a final position within a given duration and serves the traffic on its way. The problem consists in finding the optimal trajectory that minimizes a certain cost depending on the velocity and on the amount of served traffic. We formulate the problem using the framework of Lagrangian mechanics. We derive closed-form formulas for the optimal trajectory when the traffic intensity is quadratic (single-phase) using Hamilton-Jacobi equations. When the traffic intensity is bi-phase, i.e. made of two quadratics, we provide necessary conditions of optimality that allow us to propose a gradient-based algorithm and a new algorithm based on the linear control properties of the quadratic model. These two solutions are of very low complexity because they rely on fast convergence numerical schemes and closed form formulas. These two approaches return a trajectory satisfying the necessary conditions of optimality. At last, we propose a data processing procedure based on a modified K-means algorithm to derive a bi-phase model and an optimal trajectory simulation from real traffic data.