Drone Base Stations Research Articles

THz band drone communications with practical antennas: Performance under realistic mobility and misalignment scenarios

For 6G non-terrestrial communications, drones will offer uninterrupted connectivity for surveillance, sensing, and localization. They will also serve as drone base stations to support terrestrial base stations, providing large bandwidth, high-rate, and ultra-reliable low latency services. In this paper, for the first time in the literature, we depict the true performance of Terahertz (THz) band communications among drones by applying various channel selection and power allocation schemes with practical THz antennas within (0.75–4.4) THz under realistic mobility and misalignment scenarios. Through numerical simulations, we unveil the capacity of drone links under different channel selection and power allocation schemes within 10s to 100s of Gbps at distances (1–100) m, when drones are in motion and subject to (mis)alignment due to mobility and even under beam misalignment fading. However, when exposed to real drone mobility traces, the performance of all channel selection schemes drops significantly, sometimes by up to six orders of magnitude, due to the occasional reverse orientations of antennas. In addition to the capacity analysis, we report available frequency bands (transmission windows) considering all schemes and mobility patterns. We also identify a band that is commonly available under all considered mobility and misalignment settings, and we evaluate its performance in terms of spectral and energy efficiencies, which can be useful in designing THz transceivers for drone communications. Our findings emphasize the essence of active beam control solutions to achieve the desired capacity potential of THz drone communications, while also highlighting the challenges of utilizing the THz band for drone communications.

Optimizing the Deployment of an Aerial Base Station and the Phase-Shift of a Ground Reconfigurable Intelligent Surface for Wireless Communication Systems Using Deep Reinforcement Learning

In wireless networks, drone base stations (DBSs) offer significant benefits in terms of Quality of Service (QoS) improvement due to their line-of-sight (LoS) transmission capabilities and adaptability. However, LoS links can suffer degradation in complex propagation environments, especially in urban areas with dense structures like buildings. As a promising technology to enhance the wireless communication networks, reconfigurable intelligent surfaces (RIS) have emerged in various Internet of Things (IoT) applications by adjusting the amplitude and phase of reflected signals, thereby improving signal strength and network efficiency. This study aims to propose a novel approach to enhance communication coverage and throughput for mobile ground users by intelligently leveraging signal reflection from DBSs using ground-based RIS. We employ Deep Reinforcement Learning (DRL) to optimize both the DBS location and RIS phase-shifts. Numerical results demonstrate significant improvements in system performance, including communication quality and network throughput, validating the effectiveness of the proposed approach.

Maximizing profitability through cloud-enabled Reinforcement Learning for UAV coverage in real-time e-business applications

Real-time e-business applications are vital for operational efficiency, but connectivity challenges persist, particularly in remote or crowded areas. Drone Base Station (DBS) architecture, proposed for Beyond fifth Generation (B5G) and Sixth Generation (6G) multi-cell networks, offers on-demand hotspot coverage, addressing connectivity gaps in remote or crowded environments. DBSs provide a promising solution to meet the demanding requirements of high data rates, real-time responsiveness, low latency, and extended network coverage, particularly for real-time e-business applications. A critical challenge in this context involves efficiently allocating the needed number of DBSs to the different hotspot service areas, referred to as cells, to optimize the operator’s total profit under unpredictable user demands, varying area-specific service costs, and price dependence real-time e-service. The objective is to achieve the highest total revenue while minimizing the cost (cost savings) throughout the multi-cell system. This challenge is formulated as a profit-maximization discount return problem, integrating the coverage constraint, the variable cell-dependent operational cost, the e-service-based price and the uncertain demands of users across cells. Traditional optimization methods fail due to environmental uncertainty, which leads to the need to reformulate the problem as a Markov Decision Problem (MDP). We introduce a cloud-based Reinforcement Learning (RL) algorithm for DBS dispatch to address the MDP formulation. This algorithm dynamically adjusts to uncertain per-cell user distributions, considering variable operating costs and service-dependent prices across cells. Through extensive evaluation, the RL-based dispatch approach is compared with reference drone dispatch algorithms, demonstrating superior performance in maximizing operator profit through cost savings by optimizing DBS dispatch decisions based on learned user behaviors, variable operational costs, and e-service types.

A framework for the optimal deployment of police drones based on street-level crime risk

Drones are increasingly adopted for policing in many countries, as they can aid police officers to detect hazards and respond to incidents with timely and low-cost services. However, the planning and deployment of police drones are subject to several challenges, including the proper distance metric for drone flying and the risk-based location optimisation of drone base stations. This study proposes a new framework that enables the optimal deployment of police drones to address crime risk issues on urban street networks. This risk-based decision framework takes into account three potential distance metrics that regulate and shape the flying routes of drones, which in turn affects the optimal location of drone base stations. In addition, this framework takes into account the major risk constraints of flying drones in urban areas, including domestic privacy and elevation. The proposed risk-based decision framework is validated using the real case study of Liverpool with historical crime data and street network layouts. The findings contribute to the operations and management of police drones in urban areas and shift the paradigm of policing drones towards a risk-based regime.

Heterogeneous Drone Small Cells: Optimal 3D Placement for Downlink Power Efficiency and Rate Satisfaction

In this paper, we delve into the domain of heterogeneous drone-enabled aerial base stations, each equipped with varying transmit powers, serving as downlink wireless providers for ground users. A central challenge lies in strategically selecting and deploying a subset from the available drone base stations (DBSs) to meet the downlink data rate requirements while minimizing the overall power consumption. To tackle this, we formulate an optimization problem to identify the optimal subset of DBSs, ensuring wireless coverage with an acceptable transmission rate in the downlink path. Moreover, we determine their 3D positions for power consumption optimization. Assuming DBSs operate within the same frequency band, we introduce an innovative, computationally efficient beamforming method to mitigate intercell interference in the downlink. We propose a Kalai–Smorodinsky bargaining solution to establish the optimal beamforming strategy, compensating for interference-related impairments. Our simulation results underscore the efficacy of our solution and offer valuable insights into the performance intricacies of heterogeneous drone-based small-cell networks.

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.

Backhaul-Aware Drone Base Station Placement and Resource Management for FSO-Based Drone-Assisted Mobile Networks

In drone-assisted mobile networks, Drone-mounted Base Stations (DBSs) are responsively and flexibly deployed over any Places of Interest (PoI), such as sporadic hotspots and disaster-struck areas, where the existing mobile network infrastructure is unable to provide wireless coverage. In this paper, a DBS is an aerial base station to relay traffic between a nearby Macro Base Station (MBS) and the users. In addition, Free Space Optics (FSO) is applied as the backhauling solution to significantly increase the capacity of the backhaul link between an MBS and a DBS. Most of the existing DBS placement solutions assume the FSO-based backhaul link provides sufficient link capacity, which may not be true, especially when a DBS is placed far away from an MBS (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 10 km in disaster-struck areas) or in a bad weather condition. In this paper, we formulate a problem to jointly optimize bandwidth allocation and DBS placement by considering the FSO-based backhaul link capacity constraint. A <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> ackhaul awa <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> e bandwidth all <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> c <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> tion and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> BS placement (BROAD) algorithm is designed to efficiently solve the problem, and the performance of the algorithm is demonstrated via extensive simulations.

Optimization of Drone Base Station Location for the Next-Generation Internet-of-Things Using a Pre-Trained Deep Learning Algorithm and NOMA

Next-generation Internet-of-Things applications pose challenges for sixth-generation (6G) mobile networks, involving large bandwidth, increased network capabilities, and remarkably low latency. The possibility of using ultra-dense connectivity to address the existing problem was previously well-acknowledged. Therefore, placing base stations (BSs) is economically challenging. Drone-based stations can efficiently address Next-generation Internet-of-Things requirements while accelerating growth and expansion. Due to their versatility, they can also manage brief network development or offer on-demand connectivity in emergency scenarios. On the other hand, identifying a drone stations are a complex procedure due to the limited energy supply and rapid signal quality degradation in air-to-ground links. The proposed method uses a two-layer optimizer based on a pre-trained VGG-19 model to overcome these issues. The non-orthogonal multiple access protocol improves network performance. Initially, it uses a powerful two-layer optimizer that employs a population of micro-swarms. Next, it automatically develops a lightweight deep model with a few VGG-19 convolutional filters. Finally, non-orthogonal multiple access is used to schedule radio and power resources to devices, which improves network performance. We specifically examine how three scenarios execute when various Cuckoo Search, Grey Wolf Optimization, and Particle Swarm Optimization techniques are used. To measure the various methodologies, we also run non-parametric statistical tests, such as the Friedman and Wilcoxon tests. The proposed method also evaluates the accuracy level for network performance of DBSs using number of Devices. The proposed method achieves better performance of 98.44% compared with other methods.

Drone-Based Emergent Distribution of Packages to an Island from a Land Base

An island logistics system is vulnerable in emergency conditions and even isolated from land logistics. Drone-based distribution is an emerging solution investigated in this study to transport packages from a land base to the islands. Considering the drone costs, drone landing platforms in islands, and incorporation into the island ground distribution system, this study categorizes the direct, point-to-point, and cyclic bi-stage distribution modes: in the direct mode, the packages are distributed from the drone base station to the customers directly by drones; in the point-to-point mode, the packages are transported to the drone landing platform and then distributed to the customers independently; in the cyclic mode, the packages are distributed from a drone landing platform by a closed route. The modes are formulated, and evaluation metrics and solution methods are developed. In the experiments based on an island case, the models and solution methods are demonstrated, compared, and analyzed. The cyclic bi-stage distribution mode can improve drone flying distance by 50%, and an iterative heuristic algorithm can further improve drone flying distance by 27.8%, and the ground costs by 3.16%, average for the settings of twenty to sixty customers and two to four drone landing platforms. Based on the modeling and experimental studies, managerial implications and possible extensions are discussed.

Scheduling Drones for Ship Emission Detection from Multiple Stations

Various port cities and authorities have established emission control areas (ECAs) to constrain ships’ fuel usage in a specified offshore geographical range. However, these ECA policies involve high costs and have low monitoring and regulation enforcement efficiencies. In this study, a meeting model was used to investigate the drone-scheduling problem by considering the simultaneous movements of drones and ships. Set-covering integer linear programs were developed to formulate the assignments of drones to ships, and a model and solution algorithm were devised to determine the moving times and meeting positions for particular drones and ships. The proposed models and algorithms were employed and verified in experiments. The flying times for the datasets with three drone base stations were shorter than those with two. More drones resulted in shorter flying distances. The use of the meeting model enabled the acquirement of shorter flying times and distances than when it was not used. The datasets with more ships had longer flying times and distances, with almost linear relationships. The sensitivity of the effect of varying 5% of the ships’ speeds on the flying time metrics was less than 1%, affecting the flying distance by about 4–5%. Accelerating the drones was more effective towards optimizing the drones’ flying distances than times. Numerical studies showed that the consideration of simultaneous movements in the model allowed for a reduction in the drones’ flying distances and increased efficiency. Based on the modeling and experimental studies, managerial implications and possible extensions are discussed.

An Optimized LTE-Based Technique for Drone Base Station Dynamic 3D Placement and Resource Allocation in Delay-Sensitive M2M Networks

Drones are expected to facilitate extending wireless networks’ access for both human users and smart machine-type-communication devices (MTCDs) with strict and diverse quality of service (QoS) requirements. In this paper, we propose an optimal solution for the dynamic placement of an LTE drone-mounted base station to maximize the coverage of the MTCDs deployed over a large geographical area. The resulting technique jointly optimizes the drone's 3D positioning to maximize coverage and allocates the network resources in such a way that gives high priority to the delay-sensitive machine-to-machine (M2M) traffic. This optimization algorithm determines the optimal bound of the solution. Since it cannot be used in real-time operations due to its computational complexity, we also introduce a heuristic technique that offers a near-optimal solution with much reduced complexity. We conduct several simulation evaluations to assess the proposed techniques. These results are compared to those of other drone placement approaches. The comparisons show that the proposed techniques offer significantly better results in the communication coverage while fulfilling the diverse QoS requirements of the deployed M2M network. Moreover, the heuristic-based technique is shown to succeed in finding solutions close to the optimal bound with a considerable reduction of complexity over the exact algorithm.

A Continuous Actor–Critic Deep Q-Learning-Enabled Deployment of UAV Base Stations: Toward 6G Small Cells in the Skies of Smart Cities

Uncrewed aerial vehicle-mounted base stations (UAV-BSs), also know as drone base stations, are considered to have promising potential to tackle the limitations of ground base stations. They can provide cost-effective Internet connection to es that are out of infrastructure. They can also take over quickly as service providers when ground base stations fail in an unanticipated manner. UAV-BSs benefit from their mobile nature that enables them to change their 3D locations if the demand profile changes rapidly. In order to effectively leverage the mobility of UAV-BSs so as to maximize the performance of the network, 3D location of UAV-BSs requires continuous optimization. However, solving the optimization problem of UAVBSs is NP-hard with no deterministic solution in polynomial time. In this paper, we propose a continuous actor-critic deep reinforcement learning solution in order to solve the location optimization problem of UAV-BSs in the presence of mobile endpoints. The simulation results show that the proposed model significantly improves the network performance compared to Qlearning, deep Q-learning and conventional algorithms. While the Q-learning and deep Q-learning-based baselines reach the sum data rate of 35 Mbps and 42 Mbps respectively, our proposed ACDQL-based strategy maximizes the sum data rate of endpoints to 45 Mbps. Furthermore, the proposed ACDQLbased methodology reduces the convergence time of the UAV-BS placement optimization by 85 percent compared to the Q-learning and deep Q-learning baselines.

Energy Aware Drone Enabled Base Stations Deployment in Flying Adhoc Networks

The demand for Wireless Networks has increased rapidly for downloading and uploading all types of data, which is due to the inefficiency and high cost of infrastructure based cellular Networks. Unmanned Aerial Vehicle (UAVs) is the reliable solution for improving efficiency, latency redundancy, business, and troubleshooting problems on a high scale. Although, the deployment of UAVs to meet user demand in any environment or region is quite thought-provoking. This deployment not merely provides the only the coverage area, but also provides the capacity balancing and accommodation of extremely populated areas. In this research, the suggested solutions show significant achievements in terms of energy proficiency related to the fixed and dynamic drone base station system. We propose FSO (free space optical) communication system and evaluate three feasible algorithms for separate dynamic repositioning of drone base stations in terms of communicational energy. In MATLAB, the simulation result shows 20 percent of communicational energy consumption improvement can be obtained deprived of adversely affecting any other factor of the drone.

Deep-Reinforcement-Learning-Based Drone Base Station Deployment for Wireless Communication Services

Over the last few years, drone base station (DBS) technology has been recognized as a promising solution to the problem of network design for wireless communication systems, due to its highly flexible deployment and dynamic mobility features. This article focuses on the 3-D mobility control of the DBS to boost transmission coverage and network connectivity. We propose a dynamic and scalable control strategy for drone mobility using deep reinforcement learning (DRL). The design goal is to maximize communication coverage and network connectivity for multiple real-time users over a time horizon. The proposed method functions according to the received signals of mobile users, without the information of user locations. It is divided into two hierarchical stages. First, a time-series convolutional neural network (CNN)-based link quality estimation model is used to determine the link quality at each timeslot. Second, a deep <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 is applied to control the movement of the DBS in hotspot areas to meet user requirements. Simulation results show that the proposed method achieves significant network performance in terms of both communication coverage and network throughput in a dynamic environment, compared with the <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.

Optimal beamforming and deployment of millimeter-wave drone base stations

Due to their flexible deployment and line-of-sight channel conditions, using drones as base stations is a promising technology. Recent developments in Massive MIMO transmission with millimeter-wave beamforming also enables high data rates and enables simultaneous transmission to multiple ground users. In this work we consider the problem of deployment (i.e. positioning) of drone base stations for maximizing proportional fairness, along with analog MIMO beamforming that maximizes the Signal to Interference plus Noise Ratio (SINR). Simulation results reveal that careful K-means clustering of ground users and altitude adjustment performs close to a Particle Swarm Optimization (PSO)-based solution. Discrete Fourier Transform (DFT) codebook-based low-complexity beamforming also provides a promising performance, when compared to the Semi-Definite Programming (SDP)-based solution, as the number of antennas is increased.

An optimal location strategy for multiple drone base stations in massive MIMO

The concept of drone base stations (DBSs) has been applied to reduce the distance of the wireless link between a macro base station and its active users under diverse scenarios in military communications, smart industries, and high-density networks, and to provide service in topologies with damaged infrastructure. In this paper, we address the optimal positioning of multiple DBSs in a multiple-input multiple-output wireless network setting. We present a low-complexity machine learning-based algorithm to optimize the location of the DBSs by minimizing the collective wireless received signal strength experienced by the active terminals. The proposed algorithm reduces the propagation loss in the system and provides a lower bit error rate when compared with the Euclidean cost benchmark.

Hybrid beamforming with relay and dual-base stations blockage mitigation in millimetre-wave 5G communication applied in (VIOT)

Wireless millimetre-wave communication has been adopted in fifth-generation telecommunication due to its larger bandwidth, low interference, and higher resolutions. Despite these advantages, millimetre-wave communication faces many limitations due to path loss and blockages in the environment. In this paper, a spectral-efficient hybrid beamforming approach was proposed to increase network connectivity and performance, which involved several processes. A relay node was selected as a forwarder to connect a blocked user to a base station using the multi-objective Squirrel search optimization algorithm. Furthermore, the optimal base station was selected based on significant parameters for effective connectivity using the dynamic Shannon entropy-based Fuzzy VIKOR algorithm. Drone base stations were also deployed to improve the network coverage and quality of service. Furthermore, the connectivity of a drone base station was influenced by its positioning and altitude, which was controlled using the trust-region policy optimization algorithm. The selected base station executed hybrid beamforming based on an accurate channel state information prediction once the user equipment was directly or indirectly (i.e. via relay node or drone base station) connected to it. In particular, the bi-directional gated recurrent unit model was used to accurately predict the channel state information. MATLAB (R2017b) was used to simulate the proposed hybrid beamforming model and evaluate its performance metrics in terms of throughput, spectral efficiency, bit error rate, number of server users, and achievable rate. The robustness of our approach is validated under two scenarios with perfect and imperfect CSI. The obtained results were compared with the results of two previous studies, which showed that the proposed model achieved a significant performance improvement.

Intelligent Drone-Base-Station Placement for Improved Revenue in B5G/6G Systems Under Uncertain Fluctuated Demands

In this paper, the drone base-station (DBS) dispatching problem in a multi-cell B5G/6G network is investigated. The main objective is to achieve the highest system profit by serving the largest possible number of users with the least possible cost while considering the uncertain time-dependent fluctuated user’s (service) demand in the different cells, the cost of dispatched drones, and the possible profit loss due to un-served users. The problem is formulated as a profit-maximization discount return problem. Due to the uncertainty in the demand (users) in each cell, the problem cannot be solved using conventional optimization methods. Hence, the problem is reformulated as a Markov decision problem (MDP). Due to the exponential complexity of finding the solution and the unavailability of statistical knowledge about user availability (demand) in the considered regions for such optimization, we adopt a reinforcement learning (RL) approach based on the state-action-reward-state-action (SARSA) algorithm to efficiently solve the MDP. Simulation results reveal that our RL-based approach significantly increases the overall operator profit by continuously adapting its DBS dispatching strategy based on the learned users’ behavior in the network, which enables serving a larger number of users (highest revenue) with least number of DBSs (least cost).

Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches

The emergence of next-generation Internet-of-Things (NG-IoT) applications introduces several challenges for the sixth-generation (6G) mobile networks, such as massive connectivity, increased network capacity, and extremely low-latency. To countermeasure the aforementioned challenges, ultra-dense networking has been widely identified as a possible solution. However, the dense deployment of base stations (BSs) is not always possible or cost-efficient. Drone-base-stations (DBSs) can facilitate network expansion and efficiently address the requirements of NG-IoT. In addition, due to their flexibility, they can provide on-demand connectivity in emergency scenarios or address temporary increases in network traffic. Nevertheless, the optimal placement of a DBS is not a straightforward task due to the limited energy reserves and the increased signal quality degradation in air-to-ground links. To this end, swarm intelligence approaches can be attractive solutions for determining the optimal position of the DBS in the three-dimensional (3D) space. In this work, we explore well-known swarm intelligence approaches, namely the Cuckoo Search (CS), Elephant Herd Optimization (EHO), Grey Wolf Optimization (GWO), Monarch Butterfly Optimization (MBO), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) and investigate their performance and efficiency in solving the aforementioned problem. In particular, we investigate the performance of three scenarios in the presence of different swarm intelligence approaches. Additionally, we carry out non-parametric statistical tests, namely the Friedman and Wilcoxon tests, in order to compare the different approaches.

Backhaul-Aware Drone Base Station Placement and Resource Management for Fso Based Drone Assisted Mobile Networks

In drone assisted mobile networks, drones mounted small cell base stations (DBSs) are responsively and flexibly deployed over any Places of Interest (PoI), such as sporadic hotspots and disaster-struck areas, where the existing mobile network infrastructure is unable to provide wireless coverage. Here, a DBS is a relay node to relay traffic between a nearby macro base station (MBS) and the users. In addition, Free-space optics (FSO) is applied as the backhauling solution to significantly increase the capacity of the backhaul link between an MBS and a DBS in a drone assisted mobile network. Most of the existing DBS placement solutions assume the FSO based backhaul link provides sufficient link capacity, which may not be true, especially when a DBS is placed far away from an MBS (e.g., > 10 km in disaster-struck areas) or in a bad weather condition. In this paper, we formulate a problem to jointly optimize bandwidth allocation and DBS placement by considering the FSO based backhaul link capacity constraint. A Backhaul awaRe bandwidth allOcAtion and DBS placement (BROAD) algorithm is designed to efficiently solve the problem, and the performance of the algorithm is demonstrated via extensive simulations.