To prolong the lifetime of the unmanned aerial vehicles (UAVs), the UAVs need to fulfill their missions in the shortest possible time. In addition to this requirement, in many applications, the UAVs require a reliable internet connection during their flights. In this paper, we minimize the travel time of the UAVs, ensuring that a probabilistic connectivity constraint is satisfied. To solve this problem, we need a global model of the outage probability in the environment. Since the UAVs have different missions and fly over different areas, their collected data carry local information on the network's connectivity. As a result, the UAVs can not rely on their own experiences to build the global model. This issue affects the path planning of the UAVs. To address this concern, we utilize a two-step approach. In the first step, by using Federated Learning (FL), the UAVs collaboratively build a global model of the outage probability in the environment. In the second step, by using the global model obtained in the first step and rapidly-exploring random trees (RRTs), we propose an algorithm to optimize UAVs' paths. Simulation results show the effectiveness of this two-step approach for UAV networks.
Qatar University（卡塔尔大学）；Qualcomm Inc（高通公司）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches. Despite the abundance of such advantages, it is imperative to investigate the performance of UAV utilization while considering their design limitations. This paper investigates the deployment of UAV swarms when each UAV carries a machine learning classification task. To avoid data exchange with ground-based processing nodes, a federated learning approach is adopted between a UAV leader and the swarm members to impro…
This paper investigates the control of a massive population of UAVs such as drones. The straightforward method of control of UAVs by considering the interactions among them to make a flock requires a huge inter-UAV communication which is impossible to implement in real-time applications. One method of control is to apply the mean-field game (MFG) framework which substantially reduces communications among the UAVs. However, to realize this framework, powerful processors are required to obtain the control laws at different UAVs. This requirement limits the usage of the MFG framework for real-tim…
Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains
Lab of Systems Engineering and Robotics (LASER), Department of Computer Systems, Universidade Federal da Paraiba（系统工程与机器人实验室（LASER），计算机系统系，巴伊帕巴联邦大学）；Department of Cybernetics, Czech Technical University in Prague（控制论系，布拉格捷克技术大学）
Imperial College London（帝国理工学院）；Sabanci University（萨班哲大学）
We study collaborative machine learning (ML) across wireless devices, each with its own local dataset. Offloading these datasets to a cloud or an edge server to implement powerful ML solutions is often not feasible due to latency, bandwidth and privacy constraints. Instead, we consider federated edge learning (FEEL), where the devices share local updates on the model parameters rather than their datasets. We consider a heterogeneous cellular network (HCN), where small cell base stations (SBSs) orchestrate FL among the mobile users (MUs) within their cells, and periodically exchange model updat…
With growth in the number of smart devices and advancements in their hardware, in recent years, data-driven machine learning techniques have drawn significant attention. However, due to privacy and communication issues, it is not possible to collect this data at a centralized location. Federated learning is a machine learning setting where the centralized location trains a learning model over remote devices. Federated learning algorithms cannot be employed in the real world scenarios unless they consider unreliable and resource-constrained nature of the wireless medium. In this paper, we propo…
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Shanghai Jiao Tong University（上海交通大学）；Nanjing University（南京大学）；The Hong Kong Polytechnic University（香港理工大学）
Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields. Empowering UAVs networks intelligence by artificial intelligence (AI) especially machine learning (ML) techniques is inevitable and appealing to enable the aforementioned applications. To solve the problems of traditional cloud-centric ML for UAV networks such as privacy concern, unacceptable latency, and resource burden, a distributed ML technique, \textit(i.e.), federated learning (FL), has been recently proposed to enable mu…
Towards Energy-Aware Federated Traffic Prediction for Cellular Networks
Democritus University of Thrace（色雷斯德谟克利特大学）；Imperial College London（伦敦帝国理工学院）；Nokia Bell Labs（诺基亚贝尔实验室）；Centre Tecnològic de Telecomunicacions de Catalunya（加泰罗尼亚电信技术中心）；IMT Atlantique（大西洋国立高等矿业电信学校）；Inria（法国国家信息与自动化研究所）
National Yang Ming Chiao Tung University（国立阳明交通大学）；Mississippi State University（密西西比州立大学）；Texas Tech University（德克萨斯理工大学）
Federated learning (FL) is a promising distributed learning technique particularly suitable for wireless learning scenarios since it can accomplish a learning task without raw data transportation so as to preserve data privacy and lower network resource consumption. However, current works on FL over wireless networks do not profoundly study the fundamental performance of FL over wireless networks that suffers from communication outage due to channel impairment and network interference. To accurately exploit the performance of FL over wireless networks, this paper proposes a novel intermittent …
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Queen’s University（女王大学）；University of Sharjah（沙迦大学）
Unmanned aerial vehicles (UAVs) mobility enables flexible and customized federated learning (FL) at the network edge. However, the underlying uncertainties in the aerial-terrestrial wireless channel may lead to a biased FL model. In particular, the distribution of the global model and the aggregation of the local updates within the FL learning rounds at the UAVs are governed by the reliability of the wireless channel. This creates an undesirable bias towards the training data of ground devices with better channel conditions, and vice versa. This paper characterizes the global bias problem of a…
Personalized Federated Learning by Energy-Efficient UAV Communications
Department of Computer Science, North Carolina State University（计算机科学系，北卡罗来纳州立大学）；Department of Electrical and Computer Engineering, University of Massachusetts（电气与计算机工程系，马萨诸塞大学）
Kyung Hee University（庆熙大学）；The University of Sydney（悉尼大学）；University of Houston（休斯顿大学）
Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users. The mobile users use their own data to train the local machine learning model, and then send the trained models to the BS, which generates the initial model, collect…
Purdue University（普渡大学）；University at Buffalo (SUNY)（纽约州立大学布法罗分校）；Duke University（杜克大学）
We investigate training machine learning (ML) models across a set of geo-distributed, resource-constrained clusters of devices through unmanned aerial vehicles (UAV) swarms. The presence of time-varying data heterogeneity and computational resource inadequacy among device clusters motivate four key parts of our methodology: (i) stratified UAV swarms of leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning (HN-PFL), a distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource p…
On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning
School of Electronics and Information, Northwestern Polytechnical University（电子工程学院，西北工业大学）；DecoreX Intelligent Technologies Co., Ltd.（DecoreX智能科技有限公司）；Samsung AI Center（三星人工智能中心）；th Research Institute of China North Industries Group Corporation（中国北方工业集团208研究所）；Department of Electrical and Computer Engineering, University of Houston（电子与计算机工程系，休斯顿大学）
Unmanned aerial vehicles (UAVs) can be powerful Internet-of-Things components to execute sensing tasks over the next-generation cellular networks, which are generally referred to as the cellular Internet of UAVs. However, due to the high mobility of UAVs and the shadowing in the air-to-ground channels, UAVs operate in an environment with dynamics and uncertainties. Therefore, UAVs need to improve the quality of service (QoS) of sensing and communication without complete information, which makes reinforcement learning suitable to be employed in the cellular Internet of UAVs. In this article, we…
Trinity College Dublin（都柏林圣三一学院）；Virginia Tech（弗吉尼亚理工大学）
Providing reliable connectivity to cellular-connected UAV can be very challenging; their performance highly depends on the nature of the surrounding environment, such as density and heights of the ground BSs. On the other hand, tall buildings might block undesired interference signals from ground BSs, thereby improving the connectivity between the UAVs and their serving BSs. To address the connectivity of UAVs in such environments, this paper proposes a RL algorithm to dynamically optimise the height of a UAV as it moves through the environment, with the goal of increasing the throughput or sp…
Cellular-connected unmanned aerial vehicles (UAVs) are expected to play a major role in various civilian and commercial applications in the future. While existing cellular networks can provide wireless coverage to UAV user equipment (UE), such legacy networks are optimized for ground users which makes it challenging to provide reliable connectivity to aerial UEs. To ensure reliable and effective mobility management for aerial UEs, estimating the velocity of cellular-connected UAVs carries critical importance. In this paper, we introduce an approximate probability mass function (PMF) of handove…
By using wireless connectivity through cellular base stations (BSs), swarms of unmanned aerial vehicles (UAVs) can provide a plethora of services ranging from delivery of goods to surveillance. In particular, UAVs in a swarm can utilize wireless communications to collect information, like velocity and heading angle, from surrounding UAVs for coordinating their operations and maintaining target speed and intra-UAV distance. However, due to the uncertainty of the wireless channel, wireless communications among UAVs will experience a transmission delay which can impair the swarm's ability to stab…
The pervasiveness of the wireless cellular network can be a key enabler for the deployment of autonomous unmanned aerial vehicles (UAVs) in beyond visual line of sight scenarios without human control. However, traditional cellular networks are optimized for ground user equipment (GUE) such as smartphones which makes providing connectivity to flying UAVs very challenging. Moreover, ensuring better connectivity to a moving cellular-connected UAV is notoriously difficult due to the complex air-to-ground path loss model. In this paper, a novel mechanism is proposed to ensure robust wireless connec…
The usage of beamforming in Unmanned Aerial Vehicles (UAVs) has the potential of significantly improving the air-to-ground link quality. This paper presents the outcome of experimental trial of such a UAV-based beamforming system over live cellular networks. A testbed with directional antennas has been built for the experiments. It is shown that beamforming can extend the signal coverage due to antenna gain, as well as spatially reduce interference leading to higher signal quality. Moreover, it has a positive impact on the mobility performance of a flying UAV by reducing handover occurrences. …
Intelligent reflective surfaces (IRSs) capable of reconfiguring their electromagnetic absorption and reflection properties in real-time are offering unprecedented opportunities to enhance wireless communication experience in challenging environments. In this paper, we analyze the potential of IRS in enhancing cellular communications for UAVs, which currently suffers from poor signal strength due to the down-tilt of base station antennas optimized to serve ground users. We consider deployment of IRS on building walls, which can be remotely configured by cellular base stations to coherently dire…
Up until now, path planning for unmanned aerial vehicles (UAVs) has mainly been focused on the optimisation towards energy efficiency. However, to operate UAVs safely, wireless coverage is of utmost importance. Currently, deployed cellular networks often exhibit an inadequate performance for aerial users due to high amounts of intercell interference. Furthermore, taking the never-ending trend of densification into account, the level of interference experienced by UAVs will only increase in the future. For the purpose of UAV trajectory planning, wireless coverage should be taken into account to…
As an emerging field of aerial robotics, Unmanned Aerial Vehicles (UAVs) have gained significant research interest within the wireless networking research community. As soon as national legislations allow UAVs to fly autonomously, we will see swarms of UAV populating the sky of our smart cities to accomplish different missions: parcel delivery, infrastructure monitoring, event filming, surveillance, tracking, etc. The UAV ecosystem can benefit from existing 5G/B5G cellular networks, which can be exploited in different ways to enhance UAV communications. Because of the inherent characteristics …
To integrate unmanned aerial vehicles (UAVs) in future large-scale deployments, a new wireless communication paradigm, namely, the cellular-connected UAV has recently attracted interest. However, the line-of-sight dominant air-to-ground channels along with the antenna pattern of the cellular ground base stations (GBSs) introduce critical interference issues in cellular-connected UAV communications. In particular, the complex antenna pattern and the ground reflection (GR) from the down-tilted antennas create both coverage holes and patchy coverage for the UAVs in the sky, which leads to unrelia…
Cellular network-connected unmanned aerial vehicles (UAVs) experience different radio propagation conditions than radio nodes on the ground. Therefore, it has become critical to investigate the performance of aerial radios, both theoretically and through field trials. In this paper, we consider low-altitude aerial nodes that are served by an experimental cellular network. We provide a detailed description of the hardware and software components needed for establishing a broadband wireless testbed for UAV communications research using software radios. Results show that a testbed for innovation …
Cellular networks are expected to be the main communication infrastructure to support the expanding applications of Unmanned Aerial Vehicles (UAVs). As these networks are deployed to serve ground User Equipment (UES), several issues need to be addressed to enhance cellular UAVs'services.In this paper, we propose a realistic communication model on the downlink,and we show that the Quality of Service (QoS)for the users is affected by the number of interfering BSs and the impact they cause. The joint problem of sub-carrier and power allocation is therefore addressed. Given its complexity, which i…
Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, artificial intelligence (AI) model training, and wireless communications. However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training. Moreover, due to the dynamic channel condition and heterogeneous computing capacity of devices in UAV-enabled networks, the reliability and efficiency of data sharing require to be further improved. In this paper, we deve…
Unmanned Aerial Vehicles (UAVs) promise to become an intrinsic part of next generation communications, as they can be deployed to provide wireless connectivity to ground users to supplement existing terrestrial networks. The majority of the existing research into the use of UAV access points for cellular coverage considers rotary-wing UAV designs (i.e. quadcopters). However, we expect fixed-wing UAVs to be more appropriate for connectivity purposes in scenarios where long flight times are necessary (such as for rural coverage), as fixed-wing UAVs rely on a more energy-efficient form of flight …
Supporting reliable and seamless mobility for aerial users, such as unmanned aerial vehicles (UAVs), is a key challenge for the next-generation cellular systems. To tackle this challenge, we propose a joint beamforming, user association, and UAV-height control framework for cellular-connected multi-UAV networks with multiple antenna base stations (BSs). With the aim of maximizing the minimum achievable rate for UAVs subject to co-existed terrestrial user equipment's rate constraints, we devise a hierarchical bi-layer iterative algorithm to optimize BSs' beamforming vectors, UAV association mat…
Providing connectivity to aerial users, such as cellular-connected unmanned aerial vehicles (UAVs) or flying taxis, is a key challenge for tomorrow's cellular systems. In this paper, the use of coordinated multi-point (CoMP) transmission along with caching for providing seamless connectivity to aerial users is investigated. In particular, a network of clustered cache-enabled small base stations (SBSs) serving aerial users is considered in which a requested content by an aerial user is cooperatively transmitted from collaborative ground SBSs. For this network, a novel upper bound expression on …
An integrated access and backhaul (IAB) network architecture can enable flexible and fast deployment of next-generation cellular networks. However, mutual interference between access and backhaul links, small inter-site distance and spatial dynamics of user distribution pose major challenges in the practical deployment of IAB networks. To tackle these problems, we leverage the flying capabilities of unmanned aerial vehicles (UAVs) as hovering IAB-nodes and propose an interference management algorithm to maximize the overall sum rate of the IAB network. In particular, we jointly optimize the us…
In this paper, we consider the cellular Internet of unmanned aerial vehicles (UAVs), where UAVs sense data for multiple tasks and transmit the data to the base station (BS). To quantify the "freshness" of the data at the BS, we bring in the concept of the age of information (AoI). The AoI is determined by the time for UAV sensing and that for UAV transmission, and gives rise to a trade-off within a given period. To minimize the AoI, we formulate a joint sensing time, transmission time, UAV trajectory, and task scheduling optimization problem. To solve this problem, we first propose an iterativ…
Unmanned Aerial Vehicles (UAVs) have attracted considerable research interest recently. Especially when it comes to the realm of Internet of Things, the UAVs with Internet connectivity are one of the main demands. Furthermore, the energy constraint i.e. battery limit is a bottle-neck of the UAVs that can limit their applications. We try to address and solve the energy problem. Therefore, a path planning method for a cellular-connected UAV is proposed that will enable the UAV to plan its path in an area much larger than its battery range by getting recharged in certain positions equipped with p…
Anomaly detection for the Internet of Things (IoT) is a major intelligent service required by many fields, including intrusion detection, device-activity analysis, and security supervision. However, the heterogeneous distribution of data and resource-constrained end nodes present challenges for existing anomaly detection models. Due to the advantages of flexible deployment and multi-dimensional resources, high altitude platform stations (HAPSs) and unmanned aerial vehicles (UAVs), which are important components of vertical heterogeneous networks (VHetNets), have significant potential for sensi…
Due to the advantages of flexible deployment and extensive coverage, unmanned aerial vehicles (UAVs) have great potential for sensing applications in the next generation of cellular networks, which will give rise to a cellular Internet of UAVs. In this paper, we consider a cellular Internet of UAVs, where the UAVs execute sensing tasks through cooperative sensing and transmission to minimize the age of information (AoI). However, the cooperative sensing and transmission is tightly coupled with the UAVs' trajectories, which makes the trajectory design challenging. To tackle this challenge, we p…
Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam-based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a reinforcement learning (RL)-based framework for UAV-BS beam alignment using deep Q-Network…
To ensure reliable and effective mobility management for aerial user equipment (UE), estimating the speed of cellular-connected unmanned aerial vehicles (UAVs) carries critical importance since this can help to improve the quality of service of the cellular network. The 3GPP LTE standard uses the number of handovers made by a UE during a predefined time period to estimate the speed and the mobility state efficiently. In this paper, we introduce an approximation to the probability mass function of handover count (HOC) as a function of a cellular-connected UAV's height and velocity, HOC measurem…
In the current unmanned aircraft systems (UASs) for sensing services, unmanned aerial vehicles (UAVs) transmit their sensory data to terrestrial mobile devices over the unlicensed spectrum. However, the interference from surrounding terminals is uncontrollable due to the opportunistic channel access. In this paper, we consider a cellular Internet of UAVs to guarantee the Quality-of-Service (QoS), where the sensory data can be transmitted to the mobile devices either by UAV-to-Device (U2D) communications over cellular networks, or directly through the base station (BS). Since UAVs' sensing and …
King’s College London（伦敦国王学院）；Ericsson Inc.（爱立信公司）
Cellular-connected unmanned aerial vehicle (UAV) swarm is a promising solution for diverse applications, including cargo delivery and traffic control. However, it is still challenging to communicate with and control the UAV swarm with high reliability, low latency, and high energy efficiency. In this paper, we propose a two-phase command and control (C&C) transmission scheme in a cellular-connected UAV swarm network, where the ground base station (GBS) broadcasts the common C&C message in Phase I. In Phase II, the UAVs that have successfully decoded the C&C message will relay the message to th…
Localization plays a key role for safe operation of UAVs enabling beyond visual line of sight applications. Compared to GPS based localization, cellular networks can reduce the positioning error and cost since cellular connectivity is becoming a prominent solution as a communication system for UAVs. As a first step towards localization, UAV needs to receive sufficient number of localization signals each having a signal to interference plus noise ratio (SINR) greater than a threshold, which is defined as localizability. On the other hand, three-dimensional mobility of UAVs, altitude dependent c…
Cellular-connected unmanned aerial vehicle (UAV) has attracted a surge of research interest in both academia and industry. To support aerial user equipment (UEs) in the existing cellular networks, one promising approach is to assign a portion of the system bandwidth exclusively to the UAV-UEs. This is especially favorable for use cases where a large number of UAV-UEs are exploited, e.g., for package delivery close to a warehouse. Although the nearly line-of-sight (LoS) channels can result in higher powers received, UAVs can in turn cause severe interference to each other in the same frequency …
Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it enables privacy-preserving machine learning that can be implemented in the IoV. However, the performance of the FL suffers from the failure of communication links and missing nodes, especially when continuous exchanges of model parameters are required. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitat…
Recently, the unmanned aerial vehicles (UAVs) have been widely used in real-time sensing applications over cellular networks, which sense the conditions of the tasks and transmit the real-time sensory data to the base station (BS). The performance of a UAV is determined by the performance of both its sensing and transmission processes, which are influenced by the trajectory of the UAV. However, it is challenging for UAVs to design their trajectories efficiently, since they work in a dynamic environment. To tackle this challenge, in this paper, we adopt the reinforcement learning framework to s…
In this paper, a novel machine learning (ML) framework is proposed for enabling a predictive, efficient deployment of unmanned aerial vehicles (UAVs), acting as aerial base stations (BSs), to provide on-demand wireless service to cellular users. In order to have a comprehensive analysis of cellular traffic, an ML framework based on a Gaussian mixture model (GMM) and a weighted expectation maximization (WEM) algorithm is introduced to predict the potential network congestion. Then, the optimal deployment of UAVs is studied to minimize the transmit power needed to satisfy the communication deman…
This letter proposes an analytical framework to evaluate the coverage performance of a cellular-connected unmanned aerial vehicle (UAV) network in which UAV user equipments (UAV-UEs) are equipped with directional antennas and move according to a three-dimensional (3D) mobility model. The ground base stations (GBSs) equipped with practical down-tilted antennas are distributed according to a Poisson point process (PPP). With tools from stochastic geometry, we derive the handover probability and coverage probability of a random UAV-UE under the strongest average received signal strength (RSS) ass…
Mobility management is the key feature of cellular networks. When integrating unmanned aerial vehicles (UAVs) into cellular networks, their cell association needs to be carefully managed for coexistence with other cellular users. UAVs move in three dimensions and may traverse several cells on their flight path, and so may be subject to several handovers. In order to enable research on mobility management with UAV users, this paper describes the design, implementation, and testing methodology for handover experiments with aerial users. We leverage software-defined radios (SDRs) and implement a …
Integrating unmanned aerial vehicles (UAVs) into existing cellular networks encounters lots of challenges, among which one of the most striking concerns is how to achieve harmonious coexistence of aerial transceivers, inter alia, UAVs, and terrestrial user equipments (UEs). In this paper, a cellular-connected UAV network is focused, where multiple UAVs receive messages from base stations (BSs) in the down-link, while BSs are serving ground UEs in their cells. For effectively managing inter-cell interferences (ICIs) among UEs due to intense reuse of time-frequency resource block (RB) resource, …
In this paper, we consider an Internet of unmanned aerial vehicles (UAVs) over cellular networks, where UAVs work as aerial users to collect various sensory data, and send the collected data to their transmission destinations over cellular links. Unlike the terrestrial users in the conventional cellular networks, different UAVs have various communication requirements due to their sensing applications, and a more flexible communication framework is in demand. To tackle this problem, we propose a UAV-to-Everything (U2X) networking, which enables the UAVs to adjust their communication modes full …
Providing connectivity to unmanned aerial vehicle-user equipments such as drones or flying taxis is a major challenge for tomorrow cellular systems. In this paper, the use of coordinated multi-point transmission for providing seamless connectivity to UAV user equipments is investigated. In particular, a network of clustered ground base stations that cooperatively serve a number of UAVUEs is considered. Two scenarios are studied: scenarios with static, hovering UAV user equipments and scenarios with mobile UAV-UEs. Under a maximum ratio transmission, a novel framework is developed and leveraged…
