UAV-mounted Base Stations Research Articles

3D deployment of UAV-mounted base stations for heterogeneous access requirements

Recently, unmanned aerial vehicles (UAVs) have been reported a lot as aerial base stations (BSs) to assist wireless communication in Internet of Things (IoT). However, most results for UAV deployment require uniform access requirements and obstacle-free environment. This paper considers multi-UAV-mounted BSs deployment problem in obstacle-laden environment to achieve on-demand coverage for ground user equipments (UEs), with the goal of minimizing the number of required UAVs for full coverage of all UEs by optimizing the three-dimensional (3D) positions of UAVs as well as UE clustering. On one hand, the considered problem is challenging as the constraints on the non-uniform Quality of Service (QoS) requirements, the service ability of each UAV, the beamwidth of the directional antenna equipped by each UAV and obstacle avoidance are jointly taken into account. On the other hand, these practical considerations contribute to broadening the scope of applications. We formulate the problem as a mixed-integer programming problem and develop a three-step placement algorithm to achieve the goal. First, the maximum allowed service radius of each UE is derived based on the Karush-Kuhn-Tucker (KKT) condition to satisfy required signal-to-interference-plus-ratio (SINR) threshold. Second, a greed-prior chaotic search enabled adaptive artificial bee colony (GP-CA2BC) algorithm is proposed to group the UEs such that the minimum number of required UAVs can be obtained. Third, the 3D position of each UAV is optimized to further enhance the QoS. Finally, some simulation results are presented to demonstrate the superiority of the proposed scheme in decreasing the number of required UAVs as well as improving the QoS compared with the existing works.

Multi-UAV Trajectory Control, Resource Allocation, and NOMA User Pairing for Uplink Energy Minimization

In this work, we study the joint optimization of multiple unmanned aerial vehicles (UAVs)’ trajectories, power allocation, user-UAV association, and user pairing for UAV-assisted wireless networks employing the nonorthogonal multiple access (NOMA) for uplink communications. The design aims to minimize the total energy consumption of ground users while guaranteeing to successfully transmit their required amount of data to the UAV-mounted base stations. The underlying problem is a mixed-integer nonlinear program (MINLP), which is difficult to solve optimally. To tackle this problem, we derive the optimal power allocation as a function of other variables, which is used to transform the optimization problem into an equivalent form. We then propose an iterative algorithm to solve the resulting optimization problem by using the block coordinate descent (BCD) method where three subproblems are solved in each iteration and this process is repeated until convergence. Specifically, given the UAVs’ trajectories and data rates, we solve the NOMA user pairing, and user-UAV association subproblem optimally by exploiting its special structure. Then, we describe how to optimize the users’ data rates and tackle the UAV trajectory optimization in the second and third subproblems, respectively, by using the successive convex approximation (SCA) method. Numerical results show that our proposed algorithm can provide efficient active-inactive schedules (by setting user’s transmit powers to zero), and lower energy consumption compared to an existing baseline, and an OMA-based resource allocation and UAV-trajectory optimization strategy.

PSO-VFA: A Hybrid Intelligent Algorithm for Coverage Optimization of UAV-Mounted Base Stations

<p>When the number of outdoor wireless users surges and fixed base stations (BSs) can hardly accommodate high-load communication traffic, unmanned aerial vehicles (UAVs) carrying BSs can provide wireless communication services, and the location deployment of the UAV-mounted BSs directly influences the reliability of network communications. For the target area scenario where the UAVs uniformly cover user nodes, we propose a hybrid intelligent coverage algorithm called PSO-VFA to optimize the coverage of a fixed number of UAV-BSs. The PSO-VFA algorithm consists of two phases employing different intelligent algorithms. First, we adopt a particle swarm optimization (PSO) method for a global search of the coverage areas. Then, for local search, a virtual-repulsive-force-based firefly algorithm (VFA) is proposed in this paper to maximize the user coverage. In the VFA algorithm, the users are treated as the objects attracting the UAVs, and the virtual repulsive force is used for UAV location adjustment. Simulation results show that the proposed PSO-VFA hybrid algorithm has faster convergence and significantly increases the communication coverage of UAV-mounted BSs compared with individual intelligent algorithms such as VFA, PSO, genetic algorithm (GA), and simulated annealing (SA).</p> <p> </p>

Energy efficient deployment of multiple UAV mounted base stations: a machine learning-based approach

Energy efficient deployment of multiple UAV mounted base stations: a machine learning-based approach

Energy efficient deployment of multiple UAV mounted base stations: a machine learning-based approach

This paper presents an energy efficient approach for the deployment of multiple 5G enabled unmanned aerial vehicle-mounted base stations (UmBSs) in the area where existing network infrastructure has got demolished due to a natural disaster. In our proposed approach, K-means, a machine learning-based technique, is used to position UmBSs based upon which their transmit power is optimised by solving a convex optimisation problem under the constraints of maximum transmit power of UmBS, maximum height of UmBS, and channel capacity. To ensure the QoS requirements, the association of UEs and UmBS is subjected to the constraint of minimum signal to interference plus noise ratio (SINR) threshold. The effectiveness of the proposed approach is demonstrated in terms of average SINR, average channel capacity, and the number of users served. The performance of the proposed approach is compared with the two baseline approaches through MATLAB simulations.

Intelligent Reflecting Surface Enhanced Multi-UAV NOMA Networks

Intelligent reflecting surface (IRS) enhanced multi-unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks are investigated. A new transmission framework is proposed, where multiple UAV-mounted base stations employ NOMA to serve multiple groups of ground users with the aid of an IRS. The three-dimensional (3D) placement and transmit power of UAVs, the reflection matrix of the IRS, and the NOMA decoding orders among users are jointly optimized for maximization of the sum rate of considered networks. To tackle the formulated mixed-integer non-convex optimization problem with coupled variables, a block coordinate descent (BCD)-based iterative algorithm is developed. Specifically, the original problem is decomposed into three subproblems, which are alternatingly solved by exploiting the penalty method and the successive convex approximation technique. The proposed BCD-based algorithm is demonstrated to be able to obtain a stationary point of the original problem with polynomial time complexity. Numerical results show that: 1) the proposed NOMA-IRS scheme for multi-UAV networks achieves a higher sum rate compared to the benchmark schemes, i.e., orthogonal multiple access (OMA)-IRS and NOMA without IRS; 2) the use of IRS is capable of providing performance gain for multi-UAV networks by both enhancing channel qualities of UAVs to their served users and mitigating the inter-UAV interference; and 3) optimizing the UAV placement can make the sum rate gain brought by NOMA more distinct due to the flexible decoding order design.

Unmanned aerial vehicle‐enabled layered architecture based solution for disaster management

AbstractUnmanned aerial vehicle (UAV) has become a priority in the industrial and academic research community for the last few years. With the fast development of wireless networking technologies, it is widely used in many applications such as surveillance, search and rescue (SAR) operation, disaster management, remote monitoring, and so forth. Emergency communication for victims and first responders is one of the critical requirements during natural and human‐made disasters. Airborne Base Stations (BS) or UAV‐mounted BS has been used to provide high‐speed internet services and rescue responses in natural disasters and emergencies in recent years. Most of the previous research on UAV‐mounted BS (UAVmBS) only considers outdoor users. It assumes their coordinate position in a two‐dimensional (2D) plane, taking only Euclidean pathloss‐based distance into account. Though, in man‐made or urban natural disaster situations such as floods, cyclones, building fires, and so forth, users are stuck in single or multistory high‐rise buildings situated in a three‐dimensional (3D) plane. In such cases, it is possible to interact and rescue reliably if pathloss features are known a priori, both for UAVmBS to users (downlink) and vice versa (uplink). As pathloss in the above situations is a mixture of outdoor and indoor elements, adopting a compatible model can be challenging for communications. This article proposes a layered architecture and studies the path loss models best suited for the disaster management system (DMS), which considers both indoor and outdoor parameters. Further, the main goal is to effectively implement a layered architecture employing UAVmBS in urban disasters for better capacity and coverage improvement. Simulation outputs display the progress in transmission delays, throughput, and effective packet delivery for disaster management.

3D Deployment of Multiple UAV-Mounted Base Stations for UAV Communications

Recently, unmanned aerial vehicles (UAVs) have attracted lots of attention because of their high mobility and low cost. This article investigates a communication system assisted by multiple UAV-mounted base stations (BSs), aiming to minimize the number of required UAVs and to improve the coverage rate by optimizing the three-dimensional (3D) positions of UAVs, user clustering, and frequency band allocation. Compared with the existing works, the constraints of the required quality of service (QoS) and the service ability of each UAV are considered, which makes the problem more challenging. A three-step method is developed to solve the formulated mixed-integer programming problem. First, to ensure that each UAV can serve more number of users, the maximum service radius of UAVs is derived according to the required minimum power of the received signals for the users. Second, an algorithm based on artificial bee colony (ABC) algorithm is proposed to minimize the number of required UAVs. Third, the 3D position and the frequency band of each UAV are designed to increase the power of the target signals and to reduce the interference. Finally, simulation results are presented to demonstrate the superiority of the proposed solution for UAV-assisted communication systems.

Multiple UAV-Mounted Base Station Placement and User Association With Joint Fronthaul and Backhaul Optimization

In this paper, we study a joint placement, resource allocation, and user association problem for UAV-assisted wireless networks with constrained backhaul links, where multiple UAV-mounted base stations (UBSs) are deployed to provide wireless services for ground users. We propose a novel framework to maximize the user throughput within the flight-time of UBSs and provides fairness among the users. We first obtain the optimal resource allocation schemes based on different fronthaul and backhaul conditions, and an efficient iterative algorithm is then developed to jointly optimize user association and UBS placement. The optimal UBS placement can be achieved by solving an unconstrained optimization problem which is a simplification of the initial constrained optimization problem based on the optimal resource allocation. We develop a dual-domain coordinated descent and bipartite graph matching based sub-process to identify an optimal user association that prefers the nearby UBSs, as the user association under constrained backhaul links have non-unique optimal solutions. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm, and results show that our proposed method under constrained backhaul can improve both the average throughput by 49% and the fairness among the users by 47% in comparison with the method under ideal backhaul.

Resource Allocation and 3-D Placement for UAV-Enabled Energy-Efficient IoT Communications

As the commercial launch of the fifth-generation (5G) wireless communications gets near, the trend from the Internet of Things (IoT) to the Internet of Everything (IoE) is emerging. Due to the advantages of the high mobility, high Line-of-Sight (LoS) probability and low labor cost, unmanned aerial vehicles (UAVs) may play an important role in the future IoT communication networks, e.g., data collection in remote areas. In this article, we study the 3-D placement and resource allocation of multiple UAV-mounted base stations (BSs) in an uplink IoT network, where the balanced task for the UAV-BSs, the limited channel resource, and the signal interference are taken into consideration. In the considered system, the total transmission power of IoT devices is minimized, subject to a signal-to-interference-and-noise ratio (SINR) threshold for each device. First, aiming to balance the task of each UAV, we propose a clustering algorithm based on an improved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means method to divide IoT devices into several groups so that the number of devices in each group is roughly the same. Then, based on matching theory, a modified-Hungarian-based dynamic many–many matching (HD4M) algorithm is designed for assigning subchannels to IoT devices, which can efficiently mitigate the interference. Finally, we jointly optimize the transmission power of IoT devices and the altitudes of UAVs via an alternating iterative method. The simulation results show that the total transmission power decreases significantly after applying the proposed algorithms.

Backhaul-Aware Trajectory Optimization of Fixed-Wing UAV-Mounted Base Station for Continuous Available Wireless Service

Due to high mobility and flexibility, unmanned aerial vehicles (UAVs) have attracted significant attention from both academia and industry in wireless communications. Different types of UAVs are used for different wireless applications. However, there is a dilemma that no suitable approach is available to provide continuous available long-time wireless coverage from UAV-mounted base stations (UBSs) since rotary-wing UAVs require frequent energy replenishment while fixed-wing UAVs cannot hover at fixed locations. In this paper, the fixed-wing UBS is considered for providing continuous available wireless services to ground users with a novel dynamic resource allocation framework. In particular, the optimal resource allocations for both out-band and in-band wireless backhaul schemes are derived using convex optimization theory. Moreover, the sequential convex optimization method is used to obtain the energy-efficient trajectories. Finally, extensive simulation results are provided to verify the effectiveness of our proposed method. A comparison of out-band and in-band backhaul schemes is also provided.

Joint Resource Allocation, Placement and User Association of Multiple UAV-Mounted Base Stations With In-Band Wireless Backhaul

The in-band wireless backhaul that shares spectrum resources between fronthaul and backhaul is a promising method for unmanned aerial vehicle-mounted base stations (UBSs) to provide wireless services with limited resources. However, the existing works mainly considered the out-band wireless backhaul, where the resources of fronthaul and backhaul are allocated separately. Moreover, the UBS placement methods usually focused on the optimization of fronthaul while neglecting the backhaul. In this letter, a placement algorithm that jointly optimizes the resource allocation and user association for multiple UBSs with in-band wireless backhaul is proposed. In particular, the joint optimal resource allocation for both fronthaul and backhaul is derived using convex optimization theory. Given the optimal resource allocation, an equivalent spectrum efficiency is defined to simplify the optimization problem. As a result, an effective algorithm is proposed by solving the user association problem and UBS placement problem iteratively. In addition, simulation results are provided to verify the efficiency and effectiveness of our proposed method.