A method for deploying the minimal number of UAV base stations in cellular networks

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.

Ground-based communication facilities are at risk of being destroyed after natural disasters, such as earthquakes, floods, tsunamis, hurricanes, fires, or terrorist attacks. Unmanned aerial vehicles (UAV) can be used as air base stations to support user equipment (UE). UAV base station (UAV-BS) development plays a major part in rescue operations and post-disaster reconstruction. Improving network throughput in the UAV-BS signal coverage area and reducing deployment costs while maintaining effective communication are important issues that need to be addressed. In view of the problem, this paper demonstrates the problem of maximizing network throughput under the constraint of UAV-BS capacity by deploying UAV-BS. We proposed a UAV-artificial bee colony (U-ABC) algorithm to solve the problem of UAV-BS deployment. U-ABC algorithm can calculate the optimal flight position of each UAV-BS and maximum network throughput in the disaster area. In performance evaluation, we compared U-ABC algorithm with genetic algorithm, Greedy-ABC algorithm, PSO algorithm, DI-PSO algorithm and PSO-GWO algorithm. We analyzed the flight height altitude of UAV-BSs, the interference factor of UAV-BS, and the influence of the number of UE on network throughput. Results show that the proposed method improved the overall network throughput and achieved a high UE coverage rate under a given number of UAV-BSs.

The unmanned aerial vehicles base stations (UAV-BSs) have great potential in being widely used in many dynamic application scenarios. In those scenarios, the movements of served user equipments (UEs) are inevitable, so the UAV-BSs needs to be re-positioned dynamically for providing seamless services. In this paper, we propose a system framework consisting of UEs clustering, UAV-BS placement, UEs trajectories prediction, and UAV-BS reposition matching scheme, to serve the UEs seamlessly as well as minimize the energy cost of UAV-BSs' reposition trajectories. An Echo State Network (ESN) based algorithm for predicting the future trajectories of UEs and a Kuhn-Munkres-based algorithm for finding the energy-efficient reposition trajectories of UAV-BSs is designed, respectively. We conduct a simulation using a real open dataset for performance validation. The simulation results indicate that the proposed framework achieves high prediction accuracy and provides the energy-efficient matching scheme.

Unmanned aerial vehicle (UAV) base stations play a significant role in many scenarios like disaster relief and terrestrial traffic offloading. To put UAV base stations into operation, deployment is a key issue to be addressed. Existing works do not take into account different user Quality-of-Service (QoS) requirements in the deployment of multiple unmanned aerial vehicles (UAVs). Therefore, in this paper, we propose a 3D deployment scheme which minimizes the number of UAVs to cover all users with different QoS requirements. First, we derive the relationship between the altitude and coverage of a UAV at different user QoS requirements. Then, we formulate the deployment problem on the basis of the relationship. Next, we propose a 3D UAV deployment algorithm which considers both altitude and horizontal location. Finally, numerical results show that the proposed algorithm can deploy less UAVs to achieve full coverage of all users.

Unmanned aerial vehicles (UAVs) are being integrated as an active element in 5G and beyond networks. Because of their flexibility and mobility, UAV base stations (UAV-BSs) can be deployed according to the ground user distributions and their quality-of-service (QoS) requirement. Although there has been quite some prior research on the UAV deployment, no work has studied this problem in a 3-D setting and taken into account the UAV-BS capacity limit and the QoS requirements of ground users. Therefore, in this article, we focus on the problem of deploying UAV-BSs to provide satisfactory wireless communication services, with the aim to maximize the total number of covered user equipment subject to user data-rate requirements and UAV-BSs' capacity limit. First, we model the relationship between the air-to-ground path loss (PL) and the location of UAV-BSs in both horizontal and vertical dimensions, which has not been considered in previous works. Unlike the conventional UAV deployment problem formulation, the 3-D deployment problem is decoupled into a 2-D horizontal placement and altitude determination connected by PL requirement and minimization. Then, we propose a novel genetic algorithm-based 2-D placement approach in which UAV-BSs are placed to have maximum coverage of the users with consideration of data rate distribution. Finally, numerical and simulation results show that the proposed approach has enabled a better coverage percentage comparing with other schemes.

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.

The unmanned aerial vehicles (UAVs) have been widely considered as one of the key applications for future wireless communication systems, where UAVs can be used as aerial base stations (BSs) for coverage extension, transmission improvement, emergency communication, and etc. Against this background, each UAV BS is expected to select the optimal codeword to form directional analog beams, and it is capable of achieving concurrent transmissions from multiple other UAV BSs simultaneously. However, in such a kind of UAV networks, due to the vast number of connected mobile users (MUEs), UAV BSs cannot timely and preciously select the codeword from the pre-defined codebook. Fortunately, machine learning (ML) is suitable for decreasing complexity in codeword selection, because ML could extract features from the data samples acquired in real environments. In this paper, we propose an ML approach to achieve an efficient and low complexity codeword selection for UAV networks. Specifically, we first derive the probabilities that multiple UAV BSs serve one MUE to obtain the average sum rate (ASR) in UAV networks. On that basis, we develop an ML approach to maximize the ASR, where we design a classifier based on support vector machine (SVM), where our ML approach is used for selecting the optimal codeword and maximizing the ASR in UAV networks. Third, we proposed an iterative sequential minimal optimization (SMO) training algorithm to train the data of all links between UAV BSs and MUEs, where the algorithm convergence is also discussed. Finally, we show the comparison between our proposed algorithm and the traditional methods by the simulation results. The simulation at last demonstrate our method is a more efficient solution for obtain a higher performance, where a much lower computational complexity can achieved than the traditional algorithm based on channel estimation.

To address the ever-growing connectivity demand in communications, the adoption of ingenious solutions, such as utilization of unmanned aerial vehicles (UAVs) as mobile base stations, is imperative. In general, the location of a UAV base station (UAV-BS) is determined by optimization algorithms, which have high computationally complexities and are hard to run on UAVs due to energy consumption and time constraints. In this article, we overview the UAV-BS trajectory optimization problem for next generation wireless networks and show that a convolutional neural network (CNN) model can be trained to infer the location of a UAV-BS in real time. To this end, we create a framework to determine the UAV-BS locations considering the deployment of mobile users (MUs) to generate labels by using the data obtained from an optimization algorithm. Performance evaluations reveal that once the CNN model is trained with the given labels and locations of MUs, the proposed approach is, indeed, capable of approximating the results given by the adopted optimization algorithm with high fidelity, outperforming reinforcement learning-based approaches in resource-constrained settings. We also explore future research challenges and highlight key issues.

The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naïve and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency.

A major issue in deploying aerial base stations is the strain it places on the already scarce spectrum and the associated required re-design of the radio resource allocations. To solve this problem, we propose a Licensed Shared Access- (LSA-) based aerial-terrestrial system. The proposed scheme is an integration of unmanned aerial vehicle base station (UAV-BS), as a licensee system, utilising the radio spectrum allocated to other wireless systems- the incumbent system, with the ground base-station operating at the traditional frequency band for cellular systems. However, a major challenge in using LSA for D-BSs is the spectrum unavailability while the incumbent system is active. In conventional terrestrial networks, this leads to service unavailability in a wide geographical area. To tackle this, we define a dynamic UAV-BS positioning scheme that takes into account the presence or otherwise of the incumbent on the spectrum (incumbent Busy and, Idle mode) while taking into consideration the spatial capacity demand in the licensee network. In the busy incumbent mode, the problem of the UAV-BS positioning is tantamount to a goal programming problem with the first level priority goal of fulfilling the incumbent’s interference threshold constraint. Simulation result show that in addition to, the proposed (LSA-) based aerial-terrestrial scheme, solving the challenge posed by scarcity of spectrum in aerial base station deployment, a significant improvement in the system efficiency is obtained. It is seen that the proposed dynamic UAV-BS positioning scheme achieved the desired capacity requirement of the licensee network, while ensuring the interference threshold is not exceeded, at a relatively smaller distance of 1000 m to the boundary of the incumbent’s system transmission coverage as opposed to a larger distance of about 19 Km.

Unmanned aerial vehicle (UAV) with flexible mobility and low cost has been a promising technology for wireless communication. Thus, it can be used for wireless data collection in Internet of Things (IoT). In this article, we consider millimeter-wave (mmWave) communication on a UAV platform, where the UAV base station (UAV-BS) serves multiple ground users, which generate big sensor data. Both the deployment of the UAV-BS and the beamforming design have essential impact on the throughput of the system. Thus, we formulate a problem to maximize the achievable sum rate of all the users, subject to a minimum rate constraint for each user, a position constraint of the UAV-BS, and a constant-modulus (CM) constraint for the beamforming vector. We solve the nonconvex problem with two steps. First, by introducing the approximate beam pattern, we solve the deployment and beam gain allocation subproblem. Then, we utilize the artificial bee colony (ABC) algorithm to solve the beamforming subproblem. For the global optimization problem, we find the near-optimal position of the UAV-BS and the beamforming vector to steer toward each user, subject to an analog beamforming structure. The simulation results demonstrate that the proposed solution can achieve a more superior performance than the present random steering beamforming strategy in terms of achievable sum rate.

The problem of energy management of base stations in cellular networks is addressed following the recent 3GPP regulations towards traffic proportional energy consumption of 3G and B3G networks. The paper explores energy consumption of different cellular architectures and explores dynamic and static base station management schemes. The static approach is a centralized algorithm where the mobile operator can switch on/off a certain pattern of cells in the system, in an offline fashion. The dynamic approach is an online centralized or distributed algorithm that enables a binary change of the state of operation of the micro base stations (μ-BSs) according to traffic characteristics. A novel pseudo distributed dynamic management scheme is proposed where μ-BSs are managed under the administrative domain of critical stations used to maintain coverage during low peak hours. In this approach, named as Hybrid management, QoS is guaranteed during network transitions and communication overheads are reduced, compared to the centralized algorithm, rendering the proposed technique suitable for real implementations. Finally, the pseudo distributed algorithm is compared to the distributed one where μ-BSs decide locally on their state of operation, independently of the critical stations. It is found that the network can self optimize its own operation in terms of energy savings, without sacrificing QoS. Cost of services and carbon emissions of the mobile operators are consequently reduced.

The unmanned aerial vehicle (UAV) base station plays a significant role in enhancing the terrestrial network, when the ground base station (GBS) is destroyed in emergent cases or its load exceeds the capacity of the terrestrial network. Presently, many papers focus on optimizing the UAV position deployment and user access, while ignoring the optimization about the spectrum resource management. To solve this problem, we formulate a joint optimization problem of the spectrum resource management and the position placement for UAVs with the constraint of the limited backhaul capacity. Later, the joint optimization problem is modeled as a hierarchical game decision architecture comprised of a UAV position placement game and a spectrum resource management game. Further, we analyze the equilibrium property of the two games and propose two best response- (BR) based optimization algorithms to reach the Nash Equilibriums (NEs) of the two games, respectively. Specifically, the proposed algorithm about the UAV deployment considers the variable granularity local exploration and global random exploration. Simulation results show that the proposed UAV deployment algorithm can improve the total throughput by 7% and 20% at least in comparison with the K-means deployment algorithm and the fixed granularity exploration algorithm, respectively.

Heavy data load and wide cover range have always been crucial problems for big data processing in Internet of Things (IoT). Recently, mobile-edge computing (MEC) and unmanned aerial vehicle base stations (UAV-BSs) have emerged as promising techniques in IoT. In this article, we propose a three-layer online data processing network based on the MEC technique. On the bottom layer, raw data are generated by distributed sensors with local information. Upon them, UAV-BSs are deployed as moving MEC servers, which collect data and conduct initial steps of data processing. On top of them, a center cloud receives processed results and conducts further evaluation. For online processing requirements, the edge nodes should stabilize delay to ensure data freshness. Furthermore, limited onboard energy poses constraints to edge processing capability. In this article, we propose an online edge processing scheduling algorithm based on Lyapunov optimization. In cases of low data rate, it tends to reduce edge processor frequency for saving energy. In the presence of a high data rate, it will smartly allocate bandwidth for edge data offloading. Meanwhile, hovering UAV-BSs bring a large and flexible service coverage, which results in a path planning issue. In this article, we also consider this problem and apply deep reinforcement learning to develop an online path planning algorithm. Taking observations of around environment as an input, a CNN network is trained to predict action rewards. By simulations, we validate its effectiveness in enhancing service coverage. The result will contribute to big data processing in future IoT.

In this letter, the altitude of an unmanned aerial vehicle base station (UAV-BS) is optimized to maximize an average worst case secrecy rate (AWSR) without eavesdropper location information. The air-to-ground channels are modeled based on the derived line-of-sight probability under a general urban model with Poisson point process and Rayleigh distribution for the locations and heights of the buildings, respectively. To solve the AWSR maximization problem, which is intractable, we reformulate it by the first-order Taylor series approximation and apply the Davies-Swann-Campey algorithm to effectively find the solution of the modified AWSR maximization problem. Numerical results verify that the altitude of UAV-BS affects the secrecy rate significantly and that the proposed UAV-BS can improve the secrecy rate compared with a static terrestrial BS.