Nearby Base Stations Research Articles

RL-based mobile edge computing scheme for high reliability low latency services in UAV-aided IIoT networks

The prevailing adoption of Internet of Things paradigm is giving rise to a wide range of use cases in various vertical industries including remote health, industrial automation, and smart agriculture. However, the realization of such use cases is mainly challenged due to their stringent service requirements of high reliability and low latency. This challenge grows further when the service entails processing collected data for informed decision making. In this work, we consider a field of industrial Internet of Things devices that generate computational tasks and are covered by a nearby base station equipped with an edge server. The edge server offers fast processing to the devices’ tasks to help in meeting their latency requirement. Due to statistical wireless variability, the task data may not be correctly delivered in time for processing. To this end, we utilize an unmanned aerial vehicle as a supplemental edge server that tailors its trajectory and flies closer to the IIoT devices to ensure a highly reliable task delivery based on the given task reliability constraints. We formulate the problem as a Markov Decision Process, and propose a deep reinforcement learning-based approach using proximal policy optimization to optimize the unmanned aerial vehicle trajectory and scheduling devices to offload their data for processing. We present simulation results for various system scenarios to illustrate the effectiveness of the proposed solution as compared to several baseline approaches.

Dynamic Neural Network-Based Resource Allocation Framework for Latency-Aware and Energy-Efficient Optimization in Wireless Communication Systems

In wireless communication, efficiency is a crucial factor due to the inherent constraints of the radio frequency spectrum and the escalating demand for connectivity. With a limited spectrum available, efficient communication protocols are vital to maximize its utilization and accommodate the increasing number of devices. This study introduces a dynamic resource allocation system for wireless communication, encompassing essential components such as Base Stations (BS), Mobile Devices (MD), and a Centralized Controller (CC). BSs, serving as access points, establish wireless communication with MD within predetermined coverage areas. The CC acts as the neural centre of the system, collecting real-time data from both BSs and MDs to facilitate an adaptive resource allocation process. The comprehensive mechanism involves a scheduling algorithm for optimized data transmission, considering priority and Quality of Service (QoS) requirements, alongside power control mechanisms that dynamically adjust power levels to optimize energy efficiency while maintaining communication quality. MDs communicate with nearby BSs, relaying data transmission information to the CC, which strategically coordinates resources based on the collected data. This integrated system enhances resource utilization, minimizes latency, and improves energy efficiency, collectively contributing to the robustness and reliability of the wireless communication infrastructure.

Feasibility and reliability of peercloud in vehicular networks: A comprehensive study

Advanced computing capabilities embedded in modern vehicles enable them to accommodate a variety of intelligent transportation systems and real-world applications that help improve driving safety and compliance with road regulations. However, some of these applications are computationally demanding, and the local processing capabilities of vehicles may not always be enough to support them. To address this issue, existing research has proposed offloading the excessive workload to other computing facilities, such as nearby base stations, roadside units, or remote cloud servers. Still, these facilities have several limitations, including frequent unavailability, congestion, and high fees. In this paper, we explore a more pervasive and cost-effective solution: offloading excessive workloads to nearby peer vehicles via peer-to-peer connections. This approach, referred to as peercloud-vehicle, is an extension of the peercloud approach, which has been proposed for mobile social networks in the literature. The objective of this work is to have a comprehensive study on the feasibility and reliability of vehicle-to-vehicle offloading. First, we analyze two real-world vehicular network datasets to study the robustness of the vehicle contacts and estimate contact durations with deep learning-based regression methods. Second, we design reliable vehicle-to-vehicle offloading approaches based on two optimization objectives: min-delay task offloading to minimize the overall execution delay, and cost-aware task offloading to minimize the cost of task offloading. Experimental results based on real-world datasets demonstrate that peercloud-vehicle significantly outperforms existing approaches.

A Review Study of the Optimized Cluster Head Placement of Transmit-Only Nodes in Wireless Sensor Networks

Wireless sensor networks (WSNs) with transmit-only (TO) nodes are gaining increased popularity, in particular, in applications requiring heavy deployment of sensor nodes in harsh and inaccessible environments. The data collected by the members of the WSN is relayed to a nearby base station (BS) via a cluster-head (CH). Since the nodes are usually battery-limited, it is important to optimize the location of the CH in the sensing environment to prolong the lifetime of the entire network. In this paper, we describe this problem using a general energy model and review the main articles that discuss the energy optimization problem in WSNs and summarize their important results, in addition to the basic working principles of the different widely used energy-optimization algorithms.

Emission-Aware Sustainable Energy Provision for 5G and B5G Mobile Networks

A massive number of small cell base stations are expected to be deployed in the 5G and beyond 5G mobile communication networks due to the exponential increase in mobile traffic. This will directly lead to not only a significant increase in energy consumption but also the overall operational cost and carbon footprint. An energy provision based on renewable energy generation to power these small cell base stations is considered a sustainable and promising solution to address this challenge. This paper exploits the cost-effective and low-carbon energy provision solution for individual small-cell mobile networks and presents two different potential frameworks, i.e. centralized and distributed energy provision, respectively. The former supplies nearby small cell base stations through a centralized renewable energy source with energy storage facilities. For the latter, small cell base stations can be supplied by utilizing local renewable energy and storage facilities. These two frameworks are assessed and compared in terms of renewable energy utilization and carbon emission reduction in the presence of time-varying traffic loads, small cell locations and renewable energy availabilities. In addition, we devise energy management for these configurations by incorporating a resource-on-demand strategy in the proposed framework. The numerical simulation results demonstrate that the proposed centralized renewable energy generation strategy for nearby small cells maximizes the cost and energy efficiencies of the network.

PPP-RTK with Rapid Convergence Based on SSR Corrections and Its Application in Transportation

Real-time Kinematic (RTK) positioning provides centimeter-level positioning accuracy within several seconds, but it has to rely on a nearby base station. Although Precise Point Positioning (PPP) supplies precise positions with one receiver, its convergence time takes several tens of minutes, which makes PPP not well suited for real-time kinematic applications where a rapid convergence is required. PPP-RTK integrates the benefits of PPP and RTK, which actually is PPP augmented by a ground GNSS network. The satellite orbit, clock offsets, signal biases, ionospheric and tropospheric corrections are determined based on this GNSS network, modeled as state space information and transmitted to PPP users. By applying these State Space Representation (SSR) corrections, a real-time kinematic PPP-RTK approach is developed and implemented, which can instantly resolve the ambiguities to integers and realize rapid convergence. In a static scenario, it realized an instant ambiguity resolution and a rapid convergence within 2 s in more than 90% of 120 hourly sessions. The PPP-RTK has been applied and evaluated in a kinematic scenario on the highway. The horizontal positioning errors are almost lower than 0.1 m except for the time of passing through bridges. After passing bridges, the PPP-RTK successfully resolved ambiguities within 2 s in 90.6% of the cases and achieved convergence in horizontal within 5 s in more than 90% of the cases. The PPP-RTK with a precision of 0.1 m and rapid convergence of several seconds benefits the precise navigation of automobile on the highway, which will support the development of autonomous driving in future.

Digital twin‐based framework for wireless multimodal interactions over long distance

SummaryWireless multimodal interactions over long distance (WMILD) would give rise to numerous thrilling applications, such as remote touching and immersive teleoperations. However, long distances can induce large propagation delays, which makes it difficult to meet the ultra‐low latency requirements in haptic‐visual interactions. Considering existing works mainly focused on the wireless access part, this paper designs an end‐to‐end framework for general WMILD applications based on the digital twin (DT) technology and proposes an intelligent resource allocation and parameter compression scheme to guarantee WMILD performance under constraint network resources. In the framework, user device can acquire real‐time remote interactions by performing local interactions with nearby base station (BS), where a DT of the remote side is deployed to predict the remote haptic‐visual feedbacks. A reliable DT updating process is carefully designed to guarantee the DT accurately model its dynamic physical counterpart. To optimize the updating reliability, we formulate the resource allocation and parameter compression to be a constraint‐Markov decision problem, under the constraints on energy consumption, multimodal interactions and updating latencies. Then, a safe deep reinforcement learning algorithm is proposed to adapt resources and compression according to the dynamic DT updating workload, multimodal data‐streams and remote transmission capacities. Simulation shows the framework can achieve high updating reliability compared with baselines.

$\mathcal {R}$-Tracing: Consortium Blockchain-Based Vehicle Reputation Management for Resistance to Malicious Attacks and Selfish Behaviors

Empowered by 5G communication technology, high-speed information exchange can be realized on the Internet of Vehicles (IoV) for future intelligent transportation applications. Since most applications require vehicles to report perceived information to nearby base stations, how to defend against malicious attacks and selfish behaviors of vehicles in reporting activities has become an essential security issue. Some researchers have attempted to encourage vehicles to actively report truthful information by establishing reputations for them, but these efforts have some shortcomings. In model design, they neither verify the basis and process of reputation evaluation nor pay attention to the diversity and intelligence of vehicle behaviors. In system construction, they not only fail to meet the management needs of multi-party participation but also fail to provide verifiable reputation management services. In this paper, we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{R}$</tex-math></inline-formula> -tracing, a consortium blockchain-based vehicle reputation management scheme. The main contribution of our work lies in three points. First, a vehicle reputation model is designed with a reward and punishment mechanism and a regular tax mechanism. Second, a vehicle reputation management system is constructed by using Hyperledger Fabric, in which all tasks of reputation value update are abstracted into three types of transactions. Finally, the effectiveness of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{R}$</tex-math></inline-formula> -tracing is verified by extensive simulations running on a large-scale traffic scenario and performance evaluation on a prototype system. Compared with the typical linear reputation model, our model not only resists the usual malicious attack and selfish behavior but also effectively deals with the on-off attack and rational selfish behavior improved by intelligent vehicles. In the test of throughput, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{R}$</tex-math></inline-formula> -tracing achieves 600/textittps, outperforming two state-of-the-art blockchain-based schemes.

Cooperative Beamforming With Nonlinear Power Amplifiers: A Deep Learning Approach for Distributed Networks

Network densification is essential to enhance the areal capacity of future wireless networks, for which the user-centric network is a promising solution. In the perspective of network scalability, user association and cooperative beamforming (BF) among the base stations (BSs) should be designed elaborately to manage strong interference from nearby BSs. However, practical systems with hardware impairments including nonlinearity of power amplifiers may disturb these techniques and make the network difficult to be scalable. The nonlinearity of power amplifiers affects not only radio frequency (RF)-level signal but also performance according to the number of user equipments (UEs), resulting in necessity of re-designing network-level operations including BS-UE association. This paper proposes a deep learning-based cooperative BF framework for distributed networks with nonlinear power amplifiers. To optimize the cooperative BF for complicated nonlinear systems, we adopt unsupervised learning approach with constraints. To reduce the communication overhead of the network, we propose a novel neural network structure, from which the BSs can perform the cooperative BF in distributed manner. In particular, the information exchange between the central unit and the local BSs is reduced by designing neural network so that the local channel state information are utilized only in the local BSs while the central unit utilizes only covariance matrices. We show that the proposed scheme achieves a higher effective sum rate compared to the baseline schemes by adjusting the user association, BF, and power allocation to control interference and nonlinear distortion with reduced communication overhead.

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.

Unmanned-Aircraft-System-Assisted Early Wildfire Detection with Air Quality Sensors

Numerous hectares of land are destroyed by wildfires every year, causing harm to the environment, the economy, and the ecology. More than fifty million acres have burned in several states as a result of recent forest fires in the Western United States and Australia. According to scientific predictions, as the climate warms and dries, wildfires will become more intense and frequent, as well as more dangerous. These unavoidable catastrophes emphasize how important early wildfire detection and prevention are. The energy management system described in this paper uses an unmanned aircraft system (UAS) with air quality sensors (AQSs) to monitor spot fires before they spread. The goal was to develop an efficient autonomous patrolling system that detects early wildfires while maximizing the battery life of the UAS to cover broad areas. The UAS will send real-time data (sensor readings, thermal imaging, etc.) to a nearby base station (BS) when a wildfire is discovered. An optimization model was developed to minimize the total amount of energy used by the UAS while maintaining the required levels of data quality. Finally, the simulations showed the performance of the proposed solution under different stability conditions and for different minimum data rate types.

SPHA-VC: Secure passengers health assessment via vehicular communications

Passenger health evaluation is a vital aspect of smart city applications. Remotely monitoring driver and passenger health save lives. Vehicular Ad Hoc Network (VANET)-assisted Internet of Things (IoT) will be an excellent option in that periodic vehicle health information will be sent to a nearby medical Base Station (BS). VANET communications are in an open wireless channel, making them subject to attacks against patient privacy. VANET-based healthcare monitoring systems must provide timely supervision with end-to-end security. Providing cost-effective healthcare utilizing dynamic and vulnerable VANET connections is a smart city research challenge. We propose novel Secure Passengers Health Assessment via Vehicular Communications (SPHA-VC) protocol to provide Quality of Service (QoS) efficient emergency medical information transmission with high security and privacy preservation. The SPHA-VC is consisting of security and privacy preservation-aware routing algorithms for IoT-based passenger health assessment connected with the VANETs. First, we design a reliable route formation algorithm using Artificial Intelligence (AI). Second, we propose a lightweight cryptography mechanism to achieve secure end-to-end medical data transmission. The reliable routing formation algorithm using Fuzzy Logic (FL) is integrated with Elliptic Curve Cryptography (ECC)-based secure data transmission mechanism. SPHA-VC's purpose is to accomplish secure communications in IoT-enabled VANETs and vehicle privacy reserving to guard against various cyber security threats using new FL-based reliable route formation and ECC-based secure data transfer. The simulation results revealed the efficiency of the SPHA-VC compared to state-of-the-art methods.

Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks

With the rapid development of science and technology and the improvement of people’s living standards, vehicles have gradually become the main means of travel. The increase in vehicles has also brought about an increasing incidence of car accidents. In order to reduce traffic accidents, many researchers have proposed the use of vehicular networks to quickly transmit information. As long as these vehicles can receive information from other vehicles or buildings nearby in a timely manner, they can avoid accidents. In vehicular networks, the traditional double connection technique, through interference coordination scheduling strategy based on graph theory, can ensure the fairness of vehicles and obtain suitable neighborhood interference resistance with limited computing resources. However, when a base station transmits data to the vehicular user, the nearby base station and the vehicular network user may be in a state of suspended communication. Thus, the resource utilization of the above double connection vehicular network is not sufficient, resulting in a waste of resources. To solve this issue, this paper presents a study based on earnings learning with a vehicular network multi-point collaborative transmission mechanism, in which the vehicular network users communicate with the surrounding collaborative transmission. We use the Q-learning algorithm in the reinforcement learning process to enable vehicular network users to learn from each other and make cooperative decisions in different environments. In reinforcement learning, the agent makes a decision and changes the state of the environment. Then, the environment feeds back the benefit to the agent through the related algorithm so that the agent gradually learns the optimal decision. Simulation results demonstrate the superiority of our proposed approach with the revenue machine learning model compared with the benchmark schemes.

Cooperative MEC With Non-Uniform User Density: Resource Planning and State Control Optimization

Mobile edge computing (MEC) allows mobile devices to offload computationally intensive tasks to nearby base stations equipped with computational resources. MEC also provides a solution for supporting low latency responses. However, MEC increases the system power consumption by adding extra computational units. It is important to carefully plan computational resources to achieve a balance between quality of service (QoS) and power consumption. Moreover, the MEC system control should shut down unused resources when traffic rates fall to further decrease the power consumption. In this letter, we investigate a workload-based core power state control for cooperative MEC systems and a heterogeneous resource planning method. We also formulate a joint resource planning and workload-threshold optimization problem for power consumption minimization. The problem is solved using a hybrid method using metaheuristic and heuristic algorithms. The problem is first solved for an equivalent uniform system using integer gaining and sharing knowledge (IGSK). The resources are then redistributed over the base stations using a heuristic that considers the effect of power state control on core distribution. The results show that the iterative algorithm can accomplish a satisfactory QoS with the same number of resources obtained by other approaches or less. Additionally, threshold control reduced the system power consumption by 19.6%.

Energy-Aware Cloud-Edge Collaborative Task Offloading with Adjustable Base Station Radii in Smart Cities

In smart cities, the computing power and battery life of terminal devices (TDs) can be effectively enhanced by offloading tasks to nearby base stations (BSs) with richer resources. With the goal of TDs being fully served and achieving low-carbon energy savings for the system, this paper investigates task offloading in cloud-edge collaborative heterogeneous scenarios with multiple BSs and TDs. According to the proportional relationship between the energy and coverage radii of BSs, a complete coverage task offloading model with adjustable BS radii is proposed. The task offloading problem is formulated as an integer linear program with multidimensional resource constraints to minimize the sum of energy consumption of BS coverage, offloading tasks to BSs and the cloud data center (CC). Since this task offloading problem is NP-hard, two approximate algorithms with polynomial time complexity are designed based on the greedy strategy of seeking the most energy-effective disk and the primal–dual method of constructing primal feasible solutions according to dual feasible solutions. Experimental results show that both the greedy and primal–dual algorithms can achieve good approximation performance, but each of them has its own advantages due to different design principles. The former is superior in execution time and energy consumption, while the latter has advantages in balancing loads among BSs and alleviating core network bandwidth pressure.

Deployment of Heterogeneous UAV Base Stations for Optimal Quality of Coverage

This article studies the quality of coverage of deploying flying base stations mounted on unmanned aerial vehicles (UAV-BSs) after disasters or during some occasional events. In particular, we focus on the problem of minimizing the average UAV-user distance, while maintaining connectivity between the UAV-BSs and some nearby stationary base stations (SBSs). The UAV-BSs can be deployed at different altitudes, and their transmission powers may also be different. We first propose a decentralized deployment algorithm for a Line-of-Sight (LoS) scenario. This algorithm allows UAV-BSs to determine their movements based on only local information. So, it is applicable in a large scale. The local optimality and the convergence of the algorithm are proved. Moreover, we discuss how to use the algorithm in Non-LoS (NLoS) scenarios. Specifically, during its movement, each UAV-BS needs to verify the connectivity requirement as well as if a future movement will lose any already covered users. This extension guarantees that the average UAV-user distance keeps reducing during the movements of UAV-BSs. Computer simulations and comparisons with a benchmark method confirm the effectiveness of the proposed algorithms in terms of the quality of coverage.

Resilience: A Pathway Towards High Service Reliability in Telecommunication Networks

We find the loss of demands caused by a severe fault or even failure of cells in the urban telecommunication network could be compensated by two types of resilience, namely intra-sector resilience and inter-sector resilience. Intra-sector resilience is stemmed from the multi-cell design of base stations. Inter-sector resilience manifests as the partial absorption of the loss of demand by the nearby base stations. In the investigated ToC telecommunication network, the two types of resilience are practically observable. It implies that resilience is an essential factor in preserving the serviceability of telecommunication networks.

Balancing Latency and Energy Efficiency in mmWave 5G NR Systems With Multiconnectivity

Multiconnectivity is a vital option in 5G New Radio (NR) systems allowing user equipment (UE) to maintain multiple links to nearby base stations (BS) improving service reliability. However, this functionality is very power-hungry prohibiting its application in practice. To alleviate this shortcoming discontinuous reception (DRX) mechanism can be utilized. The latter requires careful parameterization as it may drastically increase latency at the air interface. In this letter, we develop a mathematical model to characterize the trade-off between energy efficiency and latency in millimeter wave (mmWave) 5G NR systems under micromobility and blockage impairments. We then utilize it to determine the optimal type of DRX timers scaling. We show that micromobility has a positive impact on energy efficiency. For low micromobility speeds (<0.2°/s) proportional DRX scaling scheme with the scaling coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k &lt; 1.0$ </tex-math></inline-formula> , provides the best performance in terms of considered metrics while for higher speeds it leads to a compromise between them. The optimal value of <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> depends on design preferences.

Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

Nowadays, large numbers of smart sensors (e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services (DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning, we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer (D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

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.