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communication coverage maximization in stadium environments using UAVs

TELEcommunication SYSTEMS 2024年第4期86卷 691-703页

作者： Jafaripour, Hamid Fathi, Mohammad Shariatpanah, Ali Univ Kurdistan Dept Elect Engn Sanandaj Iran

Maximizing the communication coverage with the minimum number of unmanned aerial vehicles (UAVs) in a telecommunication system is investigated in this paper. In particular, the problem of maximizing the coverage area in stadium environments using UAVs is modeled mathematically as a multi-objective optimization problem. While the problem is solved using state-of-the-art solvers, to address the problem complexity and achieve the results for real-time applications, we propose a heuristic algorithm. The performance evaluation done in three crowding levels demonstrates that the performance with the heuristic algorithm is comparable to the mathematical model in terms of the number of coverage users. Moreover, the running time is significantly smaller in the proposed heuristic algorithm. This shows the efficiency of the model and solution. Moreover, we compare the heuristic algorithm with the non-dominated sorting genetic algorithm (NSGAII). The results of the paper show that the use of the heuristic algorithm speeds up the processing and decision making, and at the same time maximizes the communication coverage in stadium environments.

关键词： Base station communication coverage UAV Stadium

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A Learning-Based Cooperative Navigation Approach for Multi-UAV Systems Under communication coverage

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第2期12卷 763-773页

作者： Wu, Di Cao, Zhuang Lin, Xudong Shu, Feng Feng, Zikai Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China State Key Lab Marine Resource Utilizat South China Haikou 570228 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

In multi-unmanned aerial vehicle (UAV) systems cooperative navigation under the communication coverage of ground base stations, UAVs encounter challenges in maintaining reliable communication, ensuring flight safety, and achieving efficient collaboration. To address these challenges, this study formulates the cooperative navigation problem as a Markov game and introduces a two-stream graph multi-agent proximal policy optimization (two-stream GMAPPO) algorithm based on the graph neural network (GNN). We model UAVs and other entities as graph-structured data, aggregate the node information of these graph-structured data through the GNN module, and extract potential features related to UAVs. This allows UAVs to obtain richer local information. Through the two-stream network structure, the extracted potential features related to UAVs are combined with the state space of UAVs to enhance adaptability to environmental changes and improve navigation safety. Simulation results demonstrate that the proposed method significantly outperforms baseline algorithms in both mean reward and convergence speed, confirming the superior performance of two-stream GMAPPO.

关键词： Autonomous aerial vehicles Navigation Feature extraction Graph neural networks Gain Antenna arrays Interference Heuristic algorithms Games Data mining Multi-UAV navigation communication coverage graph neural network reinforcement learning

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Multi-UAV Navigation for Partially Observable communication coverage by Graph Reinforcement Learning

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第7期22卷 4056-4069页

作者： Ye, Zhenhui Wang, Ke Chen, Yining Jiang, Xiaohong Song, Guanghua Zhejiang Univ Coll Comp Sci Hangzhou 310027 Peoples R China Zhejiang Univ Sch Aerosp & Astronaut Hangzhou 310027 Peoples R China

In this paper, we aim to design a deep reinforcement learning (DRL) based control solution to navigating a swarm of unmanned aerial vehicles (UAVs) to fly around an unexplored target area under partial observation, which serves as Mobile Base Stations (MBSs) providing optimal communication coverage for the ground mobile users. To handle the information loss caused by the partial observability, we introduce a novel network architecture named Deep Recurrent Graph Network (DRGN), which could obtain extra spatial information through graph-convolution based inter-UAV communication, and utilize historical features with a recurrent unit. Based on DRGN and maximum-entropy learning, we propose a stochastic DRL policy named Soft Deep Recurrent Graph Network (SDRGN). In SDRGN, a heuristic reward function is elaborated, which is based on the local information of each UAV instead of the global information;thus, SDRGN reduces the training cost and enables distributed online learning. We conducted extensive experiments to design the structure of DRGN and examine the performance of SDRGN. The simulation results show that the proposed model outperforms four state-of-the-art DRL-based approaches and three heuristic baselines, and demonstrate the scalability, transferability, robustness, and interpretability of SDRGN.

关键词： Navigation Task analysis Training Stochastic processes Base stations Ad hoc networks Autonomous aerial vehicles UAV control communication coverage deep reinforcement learning graph learning stochastic policy

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Distributed Deployment in UAV-Assisted Networks for a Long-Lasting communication coverage

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IEEE SYSTEMS JOURNAL 2022年第3期16卷 4130-4138页

作者： Liu, Xiaojie Wang, Xingwei Jia, Jie Lv, Jianhui Bartolini, Novella Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Peoples R China Tsinghua Univ Int Grad Sch Shenzhen Shenzhen 518057 Peoples R China Sapienza Univ Rome Dept Comp Sci & Technol I-110819 Rome Italy

Due to the advantage of flexible and quick movement, unmanned aerial vehicles (UAVs) have been widely utilized in assisting wireless communications. A challenging problem is how to deploy UAVs to provide communication services for more user devices for a long term while minimizing the number of UAVs. In this article, we propose a UAV network system with UAV deletion due to redundancy or energy exhaustion and UAV insertion to provide long-lasting communication services. Then, a distributed algorithm based on the virtual Coulomb force and Voronoi diagram is proposed to deploy UAVs improving the communication coverage and turning redundant UAVs off. On the one hand, in order to improve the communication coverage, we propose two moving schemes to move UAVs. On the other hand, in order to save energy, we propose a sleeping scheme to turn several redundant UAVs off. Particularly, the redundancy of a UAV is evaluated according to a proposed definition of the average local coverage rate. Simulation results demonstrate that the proposed algorithm can deploy a minimal number of UAVs to provide enough communication coverage and the presented network system can offer long-lasting service.

关键词： Unmanned aerial vehicles Network systems Wireless communication Redundancy Force Sensors Distributed algorithms communication coverage distributed deploy-ment long-lasting service unmanned aerial vehicle (UAV) assisted networks

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Optimized Algorithm for communication coverage in UAVs Using Virtual Force Principles 5

Optimized Algorithm for Communication Coverage in UAVs Using...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Liu, Bingchen Wuhan University of Technology School of Information Engineering Wuhan China

ISBN: (纸本)9798350309034

This study delves into the augmentation of emergency communication network coverage algorithms by employing virtual force-based methodologies tailored for unmanned aerial vehicles (UAVs). In locales frequently besieged by natural calamities, terrestrial communication infrastructure often bears the brunt of damage, creating hurdles for crucial emergency transmissions. Recognizing this gap, this investigation leverages the high mobility of multiple UAVs, swiftly addressing communication needs and concurrently curtailing costs and conserving resources. Central to this research is the formulation of UAV perceptual and coverage models, alongside a deep dive into the virtual force algorithms (VFA). In this innovative paradigm, UAVs are analogized to charged particles, and their spatial positions are fine-tuned to bolster network coverage. Experimental outcomes underscore the efficacy of this refined algorithm in amplifying coverage, catering to both uniform and diverse UAV fleets. As this field burgeons, ensuing studies might pivot towards intricate challenges like navigational barriers, intricate three-dimensional coverage scenarios, and the potential integration of hybrid optimization strategies. © 2023 IEEE.

关键词： communication coverage Mobile sensor network Monitoring area coverage optimization

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Deployment of UAV-BSs for on-demand full communication coverage

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AD HOC NETWORKS 2023年 140卷

作者： Liu, Xiaojie Wang, Xingwei Huang, Min Jia, Jie Bartolini, Novella Li, Qing Zhao, Dan Pengcheng Lab Shenzhen 518055 Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Sapienza Univ Rome Dept Comp Sci & Technol I-110819 Rome Italy

Unmanned Aerial Vehicle (UAV) mounted Base Stations (UAV-BSs) have been widely used to enhance communication coverage for ground user devices (UDs) due to their intrinsic nature of flying. In this paper, we study the deployment of UAV-BSs for on-demand full communication coverage problem. Firstly, we give the scenario model together with communication model and coverage model. Secondly, we formulate full communication coverage problem considering network robustness and QoS provided. Thirdly, to address this problem, we propose two UAV deployment algorithms to minimize the number of UAVs while achieving on -demand full coverage. Particularly, these two UAV deployment algorithms only differ in building/updating service connections. Finally, simulation results demonstrate the efficiency of our proposed algorithms.

关键词： UAV-mounted base station UAV-BS deployment communication coverage On-demand coverage Full coverage

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Distributed Energy-Efficient Multi-UAV Navigation for Long-Term communication coverage by Deep Reinforcement Learning

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2020年第6期19卷 1274-1285页

作者： Liu, Chi Harold Ma, Xiaoxin Gao, Xudong Tang, Jian Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Sejong Univ Dept Comp & Informat Secur Gwangjin Gu 209 Seoul South Korea Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

In this paper, we aim to design a fully-distributed control solution to navigate a group of unmanned aerial vehicles (UAVs), as the mobile Base Stations (BSs) to fly around a target area, to provide long-term communication coverage for the ground mobile users. Different from existing solutions that mainly solve the problem from optimization perspectives, we proposed a decentralized deep reinforcement learning (DRL) based framework to control each UAV in a distributed manner. Our goal is to maximize the temporal average coverage score achieved by all UAVs in a task, maximize the geographical fairness of all considered point-of-interests (PoIs), and minimize the total energy consumptions, while keeping them connected and not flying out of the area border. We designed the state, observation, action space, and reward in an explicit manner, and model each UAV by deep neural networks (DNNs). We conducted extensive simulations and found the appropriate set of hyperparameters, including experience replay buffer size, number of neural units for two fully-connected hidden layers of actor, critic, and their target networks, and the discount factor for remembering the future reward. The simulation results justified the superiority of the proposed model over the state-of-the-art DRL-EC approach based on deep deterministic policy gradient (DDPG), and three other baselines.

关键词： Navigation Energy consumption Reinforcement learning Path planning Drones Three-dimensional displays UAV control deep reinforcement learning energy efficiency communication coverage

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A distributed deployment algorithm for communication coverage in wireless robotic networks

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2021年 180卷 103019-103019页

作者： Liu, Xiaojie Wang, Xingwei Jia, Jie Huang, Min Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Peoples R China Univ Rome Coll Comp Sci & Technol I-00159 Rome Italy Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Wireless Robotic Networks (WRNs), composed of numerous mobile robotic agents with the ability of moving, computing, sensing, and communicating, are able to provide wireless communication services and thus implement complicated tasks for user equipments. In order to guarantee the performance of coverage rate and ensure providing the services timely and effectively, mobile robotics in WRNs are usually deployed flexibly and quickly. However, coverage overlaps and energy redundancy may be generated by excessive deployed agents. In order to provide maximum coverage area with a minimum number of agents, we study the 3-dimensional coverage deployment problem in WRNs and propose a distributed deployment algorithm. Firstly, we give the scenario model, communication model, and coverage model to define the 3-dimensional coverage problem. Secondly, we propose a distributed coverage deployment algorithm that can set redundant agents to idle mode iteratively. Herein, we decouple the coverage deployment problem in the altitude and horizontal dimensions without any loss of optimality. On the one hand, this algorithm can find the optimal altitude for agents mathematically. On the other hand, this algorithm contains a local deployment algorithm based on Particle Swarm Optimization (PSO) which is used for a particular active agent to find a better position with a larger local coverage area. In particular, the local coverage area is obtained depending on the Voronoi Diagram (VD). Our proposed algorithm is distributed which only requires local information. Finally, performance evaluation are given in three aspects, which demonstrate the effectiveness of the proposed distributed deployment algorithm.

关键词： Wireless robotic networks communication coverage Distributed deployment Particle swarm optimization Voronoi diagram

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Three-Dimensional Continuous Movement Control of Drone Cells for Energy-Efficient communication coverage

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第7期68卷 6535-6546页

作者： Yang, Peng Cao, Xianbin Xi, Xing Du, Wenbo Xiao, Zhenyu Wu, Dapeng Beihang Univ Sch Elect & Informat Engn Beijing 100083 Peoples R China Beihang Univ Minist Ind & Informat Technol China Key Lab Adv Technol Near Space Informat Syst Beijing 100083 Peoples R China Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

This paper is concerned with the efficient movement control of multiple drone cells for communication coverage. Although many works have been developed to cope with this problem, but only few of them have investigated the three-dimensional (3-D) continuous movement control of multiple drone cells. In this paper, a problem of 3-D continuous movement control of multiple drone cells is formulated with an objective of maximizing the energy-efficient communication coverage of drone-cell networks while preserving the network connectivity. To mitigate this challenging problem, an energy-efficiency and continuous-movementcontrol algorithm (E2CMC), which is based on an emerging deep reinforcement learning method, is proposed. InE2CMC, an energy-efficiency reward function considering the energy consumption, the sum of quality-of-service (QoS) requirements of users as well as the coverage fairness is first designed. Next, E2CMC learns to adjust drone cells' locations continuously by interacting with an environment in a sequence of observations, actions, and rewards. Furthermore, E2CMC will reduce the reward drastically as long as the networks are disconnected. Simulation results verify the superiority of the proposed learning algorithm on deploying multiple drone cells to provide the communication coverage.

关键词： Multiple drone-cells continuous movement control communication coverage deep reinforcement learning

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Energy-Efficient UAV Control for Effective and Fair communication coverage: A Deep Reinforcement Learning Approach

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IEEE JOURNAL ON SELECTED AREAS IN communicationS 2018年第9期36卷 2059-2070页

作者： Liu, Chi Harold Chen, Zheyu Tang, Jian Xu, Jie Piao, Chengzhe Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Sejong Univ Dept Comp Informat & Secur Seoul 13557 South Korea Syracuse Univ Dept Comp Sci & Engn Syracuse NY 13244 USA

Unmanned aerial vehicles (UAVs) can be used to serve as aerial base stations to enhance both the coverage and performance of communication networks in various scenarios, such as emergency communications and network access for remote areas. Mobile UAVs can establish communication links for ground users to deliver packets. However, UAVs have limited communication ranges and energy resources. Particularly, for a large region, they cannot cover the entire area all the time or keep flying for a long time. It is thus challenging to control a group of UAVs to achieve certain communication coverage in a long run, while preserving their connectivity and minimizing their energy consumption. Toward this end, we propose to leverage emerging deep reinforcement learning (DRL) for UAV control and present a novel and highly energy-efficient DRL-based method, which we call DRL-based energy-efficient control for coverage and connectivity (DRL-EC3). The proposed method 1) maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity;2) learns the environment and its dynamics;and 3) makes decisions under the guidance of two powerful deep neural networks. We conduct extensive simulations for performance evaluation. Simulation results have shown that DRL-EC3 significantly and consistently outperform two commonly used baseline methods in terms of coverage, fairness, and energy consumption.

关键词： UAV control deep reinforcement learning energy efficiency communication coverage

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