Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks
Virginia Tech（弗吉尼亚理工学院）；Princeton University（普林斯顿大学）；Future Network of Intelligence Institute, Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）未来智联网络研究院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Ye Hu, Mingzhe Chen, Walid Saad, H. Vincent Poor, Shuguang Cui
In this paper, the problem of the trajectory design for a group of energy-constrained drones operating in dynamic wireless network environments is studied. In the considered model, a team of drone base stations (DBSs) is dispatched to cooperatively serve clusters of ground users that have dynamic and unpredictable uplink access demands. In this scenario, the DBSs must cooperatively navigate in the considered area to maximize coverage of the dynamic requests of the ground users. This trajectory design problem is posed as an optimization framework whose goal is to find optimal trajectories that maximize the fraction of users served by all DBSs. To find an optimal solution for this non-convex optimization problem under unpredictable environments, a value decomposition based reinforcement learning (VDRL) solution coupled with a meta-training mechanism is proposed. This algorithm allows the DBSs to dynamically learn their trajectories while generalizing their learning to unseen environments. Analytical results show that, the proposed VD-RL algorithm is guaranteed to converge to a local optimal solution of the non-convex optimization problem. Simulation results show that, even without meta-training, the proposed VD-RL algorithm can achieve a 53.2% improvement of the service coverage and a 30.6% improvement in terms of the convergence speed, compared to baseline multi-agent algorithms. Meanwhile, the use of meta-learning improves the convergence speed of the VD-RL algorithm by up to 53.8% when the DBSs must deal with a previously unseen task.
University of Cyprus（塞浦路斯大学）；KIOS Research and Innovation Center of Excellence（KIOS卓越研究与创新中心）
As the number of unauthorized operations of Unmanned Aerial Vehicles (UAVs) is rising, the implementation of a versatile counter-drone system is becoming a necessity. In this work, we develop a drone-based counter-drone system, that employs algorithms for detecting and tracking a rogue drone, in conjunction with wireless interception capabilities to jointly jam the rogue drone while achieving self positioning for the pursuer drone. In the proposed system a software-defined-radio (SDR) is used for switching between jamming transmissions and spectrum sweeping functionalities to achieve the desir…
We present the University at Buffalo's Airborne Networking and Communications Testbed (UB-ANC Drone). UB-ANC Drone is an open software/hardware platform that aims to facilitate rapid testing and repeatable comparative evaluation of airborne networking and communications protocols at different layers of the protocol stack. It combines quadcopters capable of autonomous flight with sophisticated command and control capabilities and embedded software-defined radios (SDRs), which enable flexible deployment of novel communications and networking protocols. This is in contrast to existing airborne ne…
University of Maryland, College Park（马里兰大学帕克分校）；Institute for Systems Research（系统研究所）
We explore an end-to-end (including access and backhaul links) UAV-assisted wireless communication system, considering both uplink and downlink traffics, with the goal of supporting demand of the Ground Users (GUs) using the minimum number of UAVs. Moreover, in order to extend the operational (flight) time of UAVs, we exploit an energy-aware routing scheme. Our intention is to design and analyze the access and backhaul connectivity of a drone-assisted communication network for remote and crowded areas and disaster relief, while minimizing the resources required i.e., the number of UAVs.
Despite being a hot research topic for a decade, drones are still not part of our everyday life. In this article, we analyze the reasons for this state of affairs and look for ways of improving the situation. We rely on the achievements of the so-called Technology Assessment (TA), an interdisciplinary research field aiming at providing knowledge for better-informed and well-reflected decisions concerning new technologies. We demonstrate that the most critical area requiring further development is safety. Since Unmanned Aerial System Traffic Management (UTM) systems promise to address this prob…
Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning
Beijing Forestry University（北京林业大学）；Engineering Research Center for Forestry-oriented Intelligent Information Processing, National Forestry and Grassland Administration（国家林业和草原局林业智能信息处理工程研究中心）
FAIR^2 Drones: An AI-Ready Standard for Cross-Domain Wildlife Drone Datasets
The Ohio State University, Department of Computer Science and Engineering（俄亥俄州立大学计算机科学与工程系）；School of Civil, Aerospace and Design Engineering, University of Bristol（布里斯托尔大学土木、航空航天与设计工程学院）；D Optical Metrology (3DOM), Fondazione Bruno Kessler (FBK)（3DOM光学计量（3DOM），布鲁诺·克塞勒基金会（FBK））；Computer Vision and Machine Learning Systems Group, Institute for Geoinformatics, University of Muenster（计算机视觉与机器学习系统组，地理信息研究所，穆恩斯特大学）；Unmanned Aerial Systems Center, University of Southern Denmark（无人飞行系统中心，南部丹麦大学）；Department of Collective Behavior, Max Planck Institute of Animal Behavior（集体行为部门，动物行为马克斯·普朗克研究所）；Department of Biology, University of Konstanz（生物学系，康斯坦茨大学）；Department of Biology, University of Southern Denmark（生物学系，南部丹麦大学）
Universidad Tecnológica del Uruguay（乌拉圭技术大学）；Escuela Técnica Superior de Rivera（里韦拉高等技术学校）
Commercial drones are not yet dimensioned to perform indoor autonomous tasks, since they use GPS for their location in the environment. When it comes to a space with physical obstacles (walls, metal, etc.) between the communication of the drone and the satellites that allow the precise location of the same, there is great difficulty in finding the satellites or it generates interference for this location. This problem can cause an unexpected action of the drone, a collision and a possible accident can occur. The work to follow presents the development of a drone capable of operating in a physi…
Vision-based indoor localization of nano drones in controlled environment with its applications
Electrical Engineering Department, Indian Institute of Technology Bombay（印度理工学院班加罗尔分校电子工程系）；Embedded Real-Time Systems / e-Yantra Lab, Indian Institute of Technology Bombay（印度理工学院班加罗尔分校嵌入式实时系统/ e-Yantra实验室）；Computer Science and Engineering Department, Indian Institute of Technology Bombay（印度理工学院班加罗尔分校计算机科学与工程系）
A UAV-assisted Wireless Localization Challenge on AERPAW
School of Electrical and Computer Engineering, University of Georgia（佐治亚大学电气与计算机工程学院）；University of North Texas（北得克萨斯大学）；NYU Tandon School of Engineering（纽约大学坦登工程学院）；Delmont Systems LLC（德尔蒙特系统有限责任公司）；North Carolina State University（北卡罗来纳州立大学）
University of Bologna（博洛尼亚大学）；VTT Technical Research Centre of Finland Ltd（芬兰国家技术研究中心）
A measurement setup made of millimeter-wave and ultra wideband transceivers mounted on both a customized UAV and a ground station for full 3D wireless propagation analysis is described in this work. The developed system represents a flexible solution for the characterization of wireless channels and especially of urban propagation, as the drone might be easily located almost anywhere from ground level to the buildings rooftop and beyond. The double directional properties of the channel can be achieved by rotating directive antennas at the link ends. Other possible applications in urban context…
The growing deployment of drones in a myriad of applications relies on seamless and reliable wireless connectivity for safe control and operation of drones. Cellular technology is a key enabler for providing essential wireless services to flying drones in the sky. Existing cellular networks targeting terrestrial usage can support the initial deployment of low-altitude drone users, but there are challenges such as mobility support. In this paper, we propose a novel handover framework for providing efficient mobility support and reliable wireless connectivity to drones served by a terrestrial ce…
State Key Lab. for Integrated Service Networks, Xidian University（西安电子科技大学综合业务网理论及关键技术国家重点实验室）；Xidian University（西安电子科技大学）；The University of New South Wales Canberra（新南威尔士大学堪培拉分校）；Shanghai Jiao Tong University（上海交通大学）；Purdue University（普渡大学）
In order to support a variety of missions and deal with different flight environments, drone control programs typically provide configurable control parameters. However, such a flexibility introduces vulnerabilities. One such vulnerability, referred to as range specification bugs, has been recently identified. The vulnerability originates from the fact that even though each individual parameter receives a value in the recommended value range, certain combinations of parameter values may affect the drone physical stability. In this paper we develop a novel learning-guided search system to find …
We design a multi-purpose environment for autonomous UAVs offering different communication services in a variety of application contexts (e.g., wireless mobile connectivity services, edge computing, data gathering). We develop the environment, based on OpenAI Gym framework, in order to simulate different characteristics of real operational environments and we adopt the Reinforcement Learning to generate policies that maximize some desired performance.The quality of the resulting policies are compared with a simple baseline to evaluate the system and derive guidelines to adopt this technique in…
Virginia Tech（弗吉尼亚理工大学）；Princeton University（普林斯顿大学）
In this paper, the design of an optimal trajectory for an energy-constrained drone operating in dynamic network environments is studied. In the considered model, a drone base station (DBS) is dispatched to provide uplink connectivity to ground users whose demand is dynamic and unpredictable. In this case, the DBS's trajectory must be adaptively adjusted to satisfy the dynamic user access requests. To this end, a meta-learning algorithm is proposed in order to adapt the DBS's trajectory when it encounters novel environments, by tuning a reinforcement learning (RL) solution. The meta-learning al…
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
Given the fast growth of intelligent devices, it is expected that a large number of high-stake artificial intelligence (AI) applications, e.g., drones, autonomous cars, tactile robots, will be deployed at the edge of wireless networks in the near future. As such, the intelligent communication networks will be designed to leverage advanced wireless techniques and edge computing technologies to support AI-enabled applications at various end devices with limited communication, computation, hardware and energy resources. In this article, we shall present the principles of efficient deployment of m…
National Chiao Tung University（国立交通大学）；National Taipei University of Technology（国立台北科技大学）
Future mobile communication networks require an Aerial Base Station (ABS) with fast mobility and long-term hovering capabilities. At present, unmanned aerial vehicles (UAV) or drones do not have long flight times and are mainly used for monitoring, surveillance, and image post-processing. On the other hand, the traditional airship is too large and not easy to take off and land. Therefore, we propose to develop an "Artificial Intelligence (AI) Drone-Cruiser" base station that can help 5G mobile communication systems and beyond quickly recover the network after a disaster and handle the instant …
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications. In the same vein, artificial intelligence (AI) is growing rapidly nowadays and has been very successful, particularly due to the massive amount of the available data. As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorith…
Smart and agile drones are fast becoming ubiquitous at the edge of the cloud. The usage of these drones are constrained by their limited power and compute capability. In this paper, we present a Transfer Learning (TL) based approach to reduce on-board computation required to train a deep neural network for autonomous navigation via Deep Reinforcement Learning for a target algorithmic performance. A library of 3D realistic meta-environments is manually designed using Unreal Gaming Engine and the network is trained end-to-end. These trained meta-weights are then used as initializers to the netwo…
Centre for Wireless Communications University of Oulu（奥卢大学无线通信中心）；School of Information Technology Deakin University（迪肯大学信息技术学院）
Remote state monitoring over wireless is envisaged to play a pivotal role in enabling beyond 5G applications ranging from remote drone control to remote surgery. One key challenge is to identify the system dynamics that is non-linear with a large dimensional state. To obviate this issue, in this article we propose to train an autoencoder whose encoder and decoder are split and stored at a state sensor and its remote observer, respectively. This autoencoder not only decreases the remote monitoring payload size by reducing the state representation dimension, but also learns the system dynamics b…
Deakin University（迪肯大学）；Yonsei University（延世大学）；University of Oulu（奥卢大学）
Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems, posing unprecedented challenges in terms of its multi-modal data transmissions with stringent latency and reliability requirements. Towards enabling this wireless metaverse, in this article we propose a novel semantic communication (SC) framework by decomposing the metaverse into human/machine agent-specific semantic multiverses (SMs). An SM stored at each agent comprises a semantic encoder and a generator, leveraging recent advances in generative artificial intelligence (AI). To improve com…
Towards Scalable Wireless Federated Learning: Challenges and Solutions
ShanghaiTech University（上海科技大学）；Nanjing University（南京大学）；Beihang University（北京航空航天大学）；Xiameng University（厦门大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
University of Oulu（奥卢大学）；CentraleSupélec（中央理工-高等电力学院）；Université Paris-Saclay（巴黎-萨克雷大学）
Fueled by the availability of more data and computing power, recent breakthroughs in cloud-based machine learning (ML) have transformed every aspect of our lives from face recognition and medical diagnosis to natural language processing. However, classical ML exerts severe demands in terms of energy, memory and computing resources, limiting their adoption for resource constrained edge devices. The new breed of intelligent devices and high-stake applications (drones, augmented/virtual reality, autonomous systems, etc.), requires a novel paradigm change calling for distributed, low-latency and r…
EURECOM（欧洲通信学院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Future Network of Intelligence Institute（未来智联网络研究院）；University of Southern California（南加州大学）
The use of unmanned aerial vehicles (UAV) as flying radio access network (RAN) nodes offers a promising complement to traditional fixed terrestrial deployments. More recently yet still in the context of wireless networks, drones have also been envisioned for use as radio frequency (RF) sensing and localization devices. In both cases, the advantage of using UAVs lies in their ability to navigate themselves freely in 3D and in a timely manner to locations of space where the obtained network throughput or sensing performance is optimal. In practice, the selection of a proper location or trajector…
