Unmanned Aerial Vehicle Base Stations Research Articles

On the Performance of Laser-Powered UAV-Assisted SWIPT Enabled Multiuser Communication Network With Hybrid NOMA

Owing to the factors such as controllable mobility, ready-to-use technology, low cost, easy implementation, and so on, unmanned aerial vehicle (UAV) possesses tremendous potential to be one of the primary candidates for next-generation (6G) wireless networks. This paper presents a UAV-assisted multiuser communication network where a multiple antenna UAV base station (BS) serves multiple single antenna ground users (GUs). UAV-BS uses a laser source-based charging mechanism to fulfill its power requirement and applies simultaneous wireless information and power transfer (SWIPT) in the downlink in order to provide desired power to energy-constrained GUs. Also, a clustering-based hybrid multiple access technique is used that combines both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) signaling to transmit the information to all GUs, simultaneously. Due to the involved analytical complexity corresponding to the multiple antennas and users, we use a hybrid beamforming method for efficient communication. Next, we analyze the performance of the proposed framework in terms of user outage probabilities, their respective throughput, and average power harvested considering non-linear energy harvesting and derive expressions of these performance metrics. Moreover, we formulate an optimization problem where the throughput of one GU is maximized by optimally choosing the power allocation parameter while ensuring the desired target throughput at other GU in each cluster. We also illustrate how crucial is the optimal selection of the target rates to maximize the network performance. Simulation results are provided to validate the accuracy of derived expressions and to highlight the dominance of hybrid beamforming and hybrid NOMA compared to conventional methods on the performance of the considered network.

Multiagent Q-Learning-Based Multi-UAV Wireless Networks for Maximizing Energy Efficiency: Deployment and Power Control Strategy Design

In air-to-ground communications, the network lifetime depends on the operation time of unmanned aerial vehicle-base stations (UAV-BSs) owing to the restricted battery capacity. Therefore, the maximization of energy efficiency and the minimization of outage ground users are important metrics of network performance. To achieve these two objectives, the location and transmit power of the UAV-BSs in the network must be optimized. This optimization problem may not be tractable in the conventional optimization framework because multiple UAV-BSs interact in a complicated manner. Hence, we formulate the problem as a Markov decision process and develop an algorithm to obtain a solution in a reinforcement learning framework. To avoid a central controller and high computational complexity, we employ a multiagent distributed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -learning algorithm to obtain a solution. Specifically, we propose a multiagent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -learning-based UAV-BS deployment and power control strategy to maximize energy efficiency and minimize the number of outage users in multi-UAV wireless networks. Through intensive simulations, it is demonstrated that the proposed algorithm can outperform benchmark algorithms in terms of average energy efficiency and number of average outage users in multi-UAV wireless networks.

Joint Resource, Deployment, and Caching Optimization for AR Applications in Dynamic UAV NOMA Networks

The cache-enabling unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks for mixture of augmented reality (AR) and normal multimedia applications are investigated, which is assisted by UAV base stations. The user association, power allocation of NOMA, deployment of UAVs and caching placement of UAVs are jointly optimized to minimize the content delivery delay. A branch and bound (BaB) based algorithm is proposed to obtain the per-slot optimization. To cope with the dynamic content requests and mobility of users in practical scenarios, the original optimization problem is transformed to a Stackelberg game. Specifically, the game is decomposed into a leader level user association sub-problem and a number of power allocation, UAV deployment and caching placement follower level sub-problems. The long-term minimization was further solved by a deep reinforcement learning (DRL) based algorithm. Simulation result shows that the content delivery delay of the proposed BaB based algorithm is much lower than benchmark algorithms, as the optimal solution in each time slot is achieved. Meanwhile, the proposed DRL based algorithm achieves a relatively low long-term content delivery delay in the dynamic environment with lower computation complexity than BaB based algorithm.

3D Location Optimization for UAV-Aided Uplink/Downlink Transmissions

In this paper, we investigate the placement of an unmanned aerial vehicle (UAV) base station (BS) which serves multiple ground users (GUs). By taking into account both the uplink and downlink transmissions, we aim to optimize the 3D location of the UAV for maximizing the minimum average sum rate. The 3D location optimization problem is divided into the horizontal coordinate and the height optimization subproblems, respectively. We further apply the CVX toolbox to solve the horizontal coordinate optimization problem and transform the height optimization problem into the elevation angle optimization problem to obtain its solution. Our numerical results show that the minimum average sum rate of the proposed 3D location optimization scheme is higher than that of the numerical search (NS) based location optimization method, that of the energy-efficient maximal coverage (EEMC) based location optimization method and that of the random location method, for different distributions of GUs&#x2019; locations.

LEOPARD: Parallel Optimal Deep Echo State Network Prediction Improves Service Coverage for UAV-Assisted Outdoor Hotspots

Unmanned aerial vehicle (UAV) base stations (BSs) can help meet the dynamic traffic demand of flash mobile crowds, but user movements also pose a significant challenge on fast-tracking for avoiding service interruption. This paper presents a novel paral <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LE</b> l <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> ptimal dee <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> echo st <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> te netwo <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> k pre <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> iction (LEOPARD) approach that can fast and accurately learn the movement of a user equipment (UE) to reduce its impact on the link performance from the UE to the UAV-BS. Improving the current learning technique of deep echo state network (ESN), LEOPARD consists further three key optimization and learning techniques. First, we develop a Bayesian-Optimization Algorithm (BOA)-based hyper-parameters adjustment method for improving movement prediction accuracy. Secondly, the Message Passing Interface (MPI) technique is integrated into the design of LEOPARD to reduce the time complexity caused by BOA. Last, we design a Kuhn-Munkres (KM)-based matching algorithm to save the re-positioning energy consumption of multiple UAV-BSs. As shown in our simulation results, the prediction accuracy of the proposed LEOPARD, combining DeepESN, BOA, and MPI techniques, is 78% and 67% better than the state-of-the-art shallow ESN and the original deep ESN, respectively.

A swapping mechanism for the uninterrupted coverage of UAV base stations

A swapping mechanism for the uninterrupted coverage of UAV base stations

UAV-BS Formation Control Method Based on Loose Coupling Structure

We propose a novel formation control method considering the challenges of formation keeping, unmanned aerial vehicle (UAV) control, and position adjustment in UAV Base Station (UAV-BS) formation control scenarios. The method is based on a loose coupling structure inspired by the traditional virtual structure method. Firstly, a type of new formation is designed for UAV-BS group. When the UAV-BS group keep a stable network, the centers of all UAV-BS moving trajectory, e.g., circular, are treated as virtual points to form a rigid body. When the UAV-BS group transforms between different formations, the real positions of the UAV-BS group are used to form a rigid body. Secondly, we redesign a trajectory tracking method for UAV-BS to control the flight direction. A flight direction controller is designed for UAV-BS, based on nonlinear guidance logic. According to the empirical model of error feedback, an error corrector is designed to control the position error between the UAV-BS and the virtual point. Simulation results show that the proposed algorithm can satisfactorily complete the formation and control of UA-BS. Besides, the algorithm can reduce the difficulty of formation control of UAV-BS and the requirements of formation control for computing power and network communication.

UAV Base Station Site Selection Based on Spiral Algorithm in Complex Environment

The Wireless broadband communication system is the wireless communication hub and the main means of communication under the background of actual combat.In complex environments such as terrain impact and external interference strikes, the on-board base station of wireless broadband communication system may not work properly. As an air base station, UAV ensures the continuity and reliability of communication.Aiming at the problem of UAV base station location in complex environment, this paper proposes a UAV base station location method based on spiral algorithm.Firstly, this paper establishes the air-to-ground propagation model based on the characteristics of the actual geographical environment.Secondly, the coverage capability of the UAV base station is obtained by link budget and the optimal height analysis of the UAV.Finally, the location planning of the U AV base station is completed by the spiral algorithm. The simulation results show that the spiral algorithm needs fewer UAV base stations than the k-means algorithm, which can reduce the cost of the network. The method proposed in this paper can select the appropriate site for the base station of the UAV under the complex environment.

User Mobility-Aware UAV-BS Placement Update With Optimal Resource Allocation

In the presence of mobile ground users, it is imperative to optimize the placement of unmanned aerial vehicle-base stations (UAV-BSs) in a UAV-assisted communication network. Moreover, the placement update interval is also a crucial parameter that impacts the network performance; hence, it needs to be optimized. Our work aims to jointly optimize the mobile users’ association with the UAV-BSs, resource allocation, UAV-BS placement, and the update interval. We propose to divide the above optimization into two phases: phase 1 and phase 2. In phase 1, the user association, resource allocation, and UAV-BS placement are optimized, whereas the update interval is optimized in phase 2. Two different frameworks, namely, max sum rate and max min rate, are utilized for the joint optimization. Specifically, in phase 1 of the max sum rate framework, the objective is to maximize the sum rate of the users, whereas, in phase 1 of the max min rate framework, the worst-off user rate is maximized. In phase 2, the update interval is optimized by minimizing the total UAV-BS flight time and maximizing the user coverage probability. Further, the analytical expression for the coverage probability of the mobile users is also derived. A sequential approach is proposed to solve phase 1 and phase 2 jointly. We prove that the convergence of the sequential approach is guaranteed. It is observed that, in the max sum rate framework, the average update interval is independent of the number of UAV-BSs. However, in the max min rate framework, the average update interval is dependent on the number of UAV-BSs. The proposed work is also compared with a benchmark approach wherein the update interval is not optimized. It has been shown that, unlike the proposed work, the benchmark approach cannot adapt to the desired priority of service time and coverage probability.

Organizational Resource Allocation by Mobile Edge Computing in the Context of the Internet of Things

In recent years, the number of electronic devices and users has increased sharply with the rapid development and progress of electronic information technology. The motivation of this paper is to optimize the organization’s resource allocation strategy in the Internet of Things (IoT) environment. The optimal path planning and information processing efficiency are improved through Unmanned Aerial Vehicle (UAV) technology and migration optimization algorithm. The research method is migration optimization algorithm and UAV dynamic network. The information processing capability of traditional wireless communication systems has gradually been unable to meet the actual information processing needs. Therefore, a static network migration algorithm is constructed based on multiple-user multilateral edge computing servers. It migrates each user’s information processing task to the neighboring edge confidence processing server and uses the information processing server to perform auxiliary calculations. The simulation model adds a utility function that simulates energy consumption, delay weighting, and maximum extreme value, combined with the allocation strategy of optimizing each user’s information processing task to achieve the optimization goal. The static network migration algorithm established in this simulation has better results than other benchmark algorithms. Both scenario 1 and scenario 2 in the simulation show very close performance to the optimal solution. Meanwhile, a migration algorithm that can provide wireless charging for UAVs is built by a dynamic edge computing model based on the time associated with the UAV base station and multiple end users. Combined with completing the information processing tasks in each time slot, the energy arrival is also non-directional. The dynamic network migration algorithm can optimize the number of tasks absorbed by the end-user based on the current online status of the system without knowing the global information. The optimized target equation is related to the queue stability, and the parameter V has a linear relationship with the queue backlog length. Here, the problem of computing migration is studied in Mobile Edge Computing (MEC). The results show that the utility function of the weighted sum has an approximately linear relationship with the weights. As the value of the utility function increases, so does the weight function. The optimal data throughput of the proposed model is 70,000 bits, while the optimal data throughput of the state-of-the-art model is 68,000 bits. Therefore, the data transmission performance of the model presented here is better than that of other models. MEC can be significantly improved the efficiency of organizational resource allocation. Combining UAV and wireless charging technology, the computing and communication resource allocation issues of the UAV’s edge computing system are comprehensively discussed to improve the performance and efficiency of the network.

Path planning of UAV base station based on deep reinforcement learning

UAV base station platform has become the current research hotspot of assisting ground base station for wireless coverage.At present, the most important issue is how to make path planning to provide the stable communication guarantee for multiple mobile users. In this article, we model the air-to-ground channel to describe the path loss between the UAV platform and the user and build a simulation environment for training based on the OpenAI-GYM architecture. In addition, this paper proposes a reinforcement learning algorithm based on intrinsic rewards, which uses the mean square error of the state prediction results to quantify the novelty of the state. Algorithms enable agents to efficiently carry out strategy iterations. Experiments results showed that our algorithm has a higher score and takes less time.

Energy-Efficient UAV Trajectory Design with Information Freshness Constraint via Deep Reinforcement Learning

Unmanned aerial vehicle (UAV) technique with flexible deployment has enabled the development of Internet of Things (IoT) applications. However, it is difficult to guarantee the freshness of information delivery for the energy-limited UAV. Thus, we study the trajectory design in the multiple-UAV communication system, in which the massive ground devices send the individual information to mobile UAV base stations under the demand of information freshness. First, an energy-efficiency (EE) maximization optimization problem is formulated under the rest energy, safety distance, and age of information (AoI) constraints. However, it is difficult to solve the optimization problem due to the nonconvex objective function and unknown dynamic environment. Second, a trajectory design based on the deep Q-network method is proposed, in which the state space considering energy efficiency, rest energy, and AoI and the efficient reward function related with EE performance are constructed, respectively. Furthermore, to avoid the dependency of training data for the neural network, the experience replay and random sampling for batch are adopted. Finally, we validate the system performance of the proposed scheme. Simulation results show that the proposed scheme can achieve a better EE performance compared with the benchmark scheme.

The Outage-Free Replacement Problem in Unmanned Aerial Vehicle Base Stations

Thanks to the feature of on-demand and flexible deployment, unmanned aerial vehicle base stations (UAV-BS) can enhance coverage and capacity beyond current 5 G cellular networks. The main challenge in deploying UAV-BS lies in the fact that the limited battery in UAB-BS cannot support long-time reliable services. Therefore, a serving UAV-BS being running out its power needs to be replaced with a new fully-charged UAV-BS. This is called UAV-BS replacement process. However, most existing UAV-BS replacement methods ignore the link outage problem during the replacement. Thus the outage-free replacement problem (OFRP) for UAV-BSs arises. We provide a method to calculate the outage probability of the UAV replacement approach. We further formulate the Outage Probability Minimization Problem (OPMP) for UAV-BS replacement. To solve this problem, a 3-Dimensional Outage-Free Replacement Approach (3D-OFRA) is presented, which exploits different heights of swapping UAV-BSs during the replacement process. More importantly, we provide the guidelines of determining the distance between two UAV-BSs to minimize the link outage possibility during the UAV-BS replacement process. Our numerical results show that with the recommended distance between two UAV-BSs based on 3D-OFRA, the outage probability of the served UEs approaches to zero, compared with 40% of the traditional direct replacement approach.

Predictive UAV Base Station Deployment and Service Offloading With Distributed Edge Learning

In modern networks, edge computing will be responsible for processing and learning from the critical network- and user-generated data, such as wireless link usage, mobility information, application requests, and many others. The presence of Artificial Intelligence-based (AI) applications at the edge of the network will enable the network to predict necessary user behavior and its impact on network infrastructure, such as base station overloading. One of the main strategies for offloading users and base stations is to deploy UAV base stations, or flying base stations, which can dynamically provide service and connectivity. In this article, we introduce a framework for distributed learning over Multi-access Edge Computing (MEC), which manages data applications in a fully distributed setting across edge servers, thus reducing the cost of collecting user information in a centralized server. We couple the proposed distributed learning with a novel similarity metric for user trajectories, which can aggregate neural network models with similar costs as other model aggregation techniques. However, the aggregation technique can achieve much higher accuracy. Furthermore, we apply the proposed distributed learning scheme to manage and deploy flying base stations to areas that experience high demand or poor user connectivity, thus optimizing connectivity in terms of user satisfaction, delay, and network throughput.

Reinforcement Learning Aided UAV Base Station Location Optimization for Rate Maximization

The application of unmanned aerial vehicles (UAV) as base station (BS) is gaining popularity. In this paper, we consider maximization of the overall data rate by intelligent deployment of UAV BS in the downlink of a cellular system. We investigate a reinforcement learning (RL)-aided approach to optimize the position of flying BSs mounted on board UAVs to support a macro BS (MBS). We propose an algorithm to avoid collision between multiple UAVs undergoing exploratory movements and to restrict UAV BSs movement within a predefined area. Q-learning technique is used to optimize UAV BS position, where the reward is equal to sum of user equipment (UE) data rates. We consider a framework where the UAV BSs carry out exploratory movements in the beginning and exploitary movements in later stages to maximize the overall data rate. Our results show that a cellular system with three UAV BSs and one MBS serving 72 UE reaches 69.2% of the best possible data rate, which is identified by brute force search. Finally, the RL algorithm is compared with a K-means algorithm to study the need of accurate UE locations. Our results show that the RL algorithm outperforms the K-means clustering algorithm when the measure of imperfection is higher. The proposed algorithm can be made use of by a practical MBS–UAV BSs–UEs system to provide protection to UAV BSs while maximizing data rate.

A Cat Swarm Optimization based transmission power minimization for an aerial NOMA communication system

This article underlines the inclusion of Non-Orthogonal Multiple Access (NOMA) aerial network nodes to rapidly serve a mass deployment of devices in the next-generation networks. The analysis of an aerial NOMA deployment is conducted considering the objective of minimization of the required transmission power in comparison to an aerial deployment employing Orthogonal Multiple Access (OMA). The findings of the study highlight the inter-dependency among the considered optimization variables of user pairing, altitude, and power allocation as well as stress the implication of their joint optimization for improved performance of the aerial NOMA system. The formulated mixed-integer non-linear programming problem is solved utilizing a joint optimization technique. Meanwhile, the employment of Cat Swarm Optimization (CSO) framework for NOMA user pairing optimization marks the first work of its kind in the literature. Subsequently, the altitude of the NOMA Unmanned Aerial Vehicle Base Station (UAV-BS) is computed using tools of convex optimization. The obtained results of the proposed methodology solved iteratively substantiate the better performance of NOMA compared to an equivalent OMA UAV-BS deployment. Subsequently, the presented results demonstrate the efficacy of the proposed CSO approach in reducing the required transmission power as well as the operating altitude attributed to lower flying energy consumption of the UAV-BS compared to random and particle swarm optimization based user pairing techniques.

Two-Stage Channel Adaptive Algorithm for Unmanned Aerial Vehicles Localization with Cellular Networks

Unmanned aerial vehicle (UAV) is regarded as a powerful tool to expand the existing ground wireless network into aerial space. Since high mobility is an essential characteristic for UAV, it is important to carry out an accurate, real-time, and high-precision localization in terms of safe operation and communication link maintenance. The cellular network-based localization technology has provided UAV a solution with both high coverage and seamless connection. However, the complex channel environment between the UAV and terrestrial base station (BS) would have weakened the localization performance. To solve this problem, a two-stage channel adaptive algorithm for cellular-connected UAV has been proposed. The first stage of the algorithm is to revise the observation error introduced by the complex channel environment using the model of DDPG. The second stage is to locate the UAV position with TDOA algorithm using the revised observation values. Simulation results have demonstrated that the proposed algorithm can achieve the channel adaptive effect by revising the observation errors and improve location performance greatly, especially for UAVs at a relative lower altitude.

Intelligent Reflecting Surface and UAV Assisted Secrecy Communication in Millimeter-Wave Networks

In this paper, we consider the secure transmission problem in an unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) assisted mmWave networks in the presence of an eavesdropper. The UAV base station (UAV-BS) and IRS are deployed to overcome the blockages. Artificial noise (AN) is exploited against the eavesdropper. Aiming to maximize the secrecy rate, we jointly design the positions and beamforming of UAV-BS and IRS, where the positions here represent the deployed position of the UAV-BS and the activation position of the IRS. Meanwhile, the maximum transmits power, minimum height constraint for UAV-BS, and the legitimate receiver minimum rate constraint are required. To obtain the optimal UAV-BS position, the elevation angle-dependent probabilistic LoS air-to-ground channel is exploited in this network. To tackle the formulated non-convex problem, we divide it into two subproblems: (1) design UAV-BS and IRS positions; (2) design UAV-BS and IRS beamforming. Moreover, to reduce the complexity, we propose an ideal beamforming model to find the near-optimal positions of UAV-BS and IRS. Then, semidefinite relaxation (SDR) is utilized to cope with the highly coupled and high-dimensional variable vectors. Finally, we obtain a sub-optimal solution by an efficient and low-complexity alternating optimization algorithm. Numerical results demonstrate the improvement of secrecy rate by jointly designing the positions as well as the significant performance gains achieved by our proposed schemes over benchmark schemes.

Joint Subchannel Allocation and Power Control in Licensed and Unlicensed Spectrum for Multi-Cell UAV-Cellular Network

In this paper, we investigate the resource and interference management problem in a novel scenario where multiple unmanned aerial vehicle base stations (UAV-BSs) provide cellular services to UAV users (UAV-UEs) by reusing both licensed and unlicensed spectrum. Considering the co-existence of terrestrial cellular, WiFi and UAV-BSs, a joint optimization problem is formulated for both subchannel allocation and power control of UAV-UEs over the licensed/unlicensed spectrum in order to maximize the uplink sum-rate of the multi-cell UAV-cellular network. Since the formulated problem is NP-hard, we decompose it into three sub-problems. Specifically, we first use the convex optimization and the Hungarian algorithm to obtain the global optimal of power and subchannel allocations in the licensed spectrum, respectively. Then, we propose a matching game with externalities and coalition game algorithms to obtain the Nash stable of the subchannel allocation in the unlicensed band. Local optimal power assignment in the unlicensed spectrum is obtained using the successive convex approximation (SCA) method. An iterative algorithm is thereby developed to solve the three sub-problems sequentially till reaching convergence. Simulation results show that the proposed algorithm can improve the network capacity by nearly two times than the Long Term Evolution-Advanced (LTE-A).

UAV Assisted Wireless Network

With the increasing demand of social network service, the unmanned aerial vehicle has been used as a base station to assist terrestrial base station to improve wireless network performance. UAV base station provides high efficiency and wider data transmitting range due to the small size and flexibility of UAV. However, UAV wireless network faces few challenges. Energy efficiency is hard to achieve due to small battery capacity. The base station performance is also very important. It can be determined by aircraft’s flying stability, the performance of air to ground communication and the limitation of wireless coverage of UAV. In order to achieve optimal UAV deployment, improving deployment delay, communication coverage and UAV number limitation are important. Trajectory optimizing problems also need to be considered. This article analyzes UAV assisted wireless networks through investigating UAV energy efficiency, UAV aided network performance, optimal deployment methods and flight trajectory. It is shown that energy efficiency can be optimized by applying LoS based channel in UAV trajectory planning. And inequality iteration algorithm proposed by former researchers is used to determine optimal flight trajectory. This method is efficient because of cellular network’s interference-free ability. As for performance, channel selection methods are used to reduce overflow rate and boost data-received size. These methods are analyzed and proved to be effective for improving UAV aided wireless network performance.