The large-scale development of the lowaltitude economy imposes increasingly stringent requirements on the supporting information infrastructure,necessitating the establishment of a low-altitude intelligent network(LAI...The large-scale development of the lowaltitude economy imposes increasingly stringent requirements on the supporting information infrastructure,necessitating the establishment of a low-altitude intelligent network(LAIN)with wide-area communication,high-precision navigation,and efficient supervision capabilities.Benefiting from its broad coverage,high reliability,and large bandwidth,the 5G cellular network serves as a critical foundation for LAIN construction.However,conventional cellular networks are primarily designed for two-dimensional terrestrial scenarios,and thus face significant limitations in coverage and interference resistance within complex three-dimensional low-altitude environments.To address the unique demands of LAIN applications,key challenges must be tackled,including achieving seamless three-dimensional coverage,mitigating interference in multi-dimensional network deployments,and ensuring stringent requirements for service quality and security supervision.This paper proposes an integrated LAIN architecture characterized by the convergence of communication,navigation,sensing,and management,enhanced with artificial intelligence and security mechanisms to improve overall system intelligence and resilience.Furthermore,this paper conducts an in-depth analysis of the critical challenges in LAIN deployment,explores enabling technologies to address these issues,and offers insights into the future development direction of low-altitude intelligent networks.展开更多
This paper proposes a novel blended hyper-cellular architecture for low-altitude aerial intelligent networks(LAINs)to provide agile coverage tailored to active air routes and takeoff/landing spots.Traditional cellular...This paper proposes a novel blended hyper-cellular architecture for low-altitude aerial intelligent networks(LAINs)to provide agile coverage tailored to active air routes and takeoff/landing spots.Traditional cellular networks struggle to meet the dynamic demands of low-altitude UAV communications due to their rigid structures.The hyper-cellular network(HCN)architecture separates control and traffic coverage,enabling flexible and energy-efficient operations.The key components include control base stations(CBSs)for wide-area signaling coverage and traffic base stations(TBSs)that can be dynamically activated based on traffic demands.The proposed solution also integrates space information networks(SINs)to enhance the coverage efficiency.Key technologies such as all-G CBS using RISC-V architecture,AI-powered radio maps for low-altitude environments,and agile TBS coverage adaptation are introduced with some preliminary studies.These designs aim to address challenges like mobility management,interference coordination,and the need for real-time spectrum sharing in blended satellite-terrestrial networks.The proposed solution offers a scalable and agile framework to support the rapidly growing demand for reliable,low-latency,and high-capacity UAV communications in urban environments.展开更多
Communications system has a signifi-cant impact on both operational safety and logisti-cal efficiency within low-altitude drone logistics net-works.Aiming at providing a systematic investiga-tion of real-world communi...Communications system has a signifi-cant impact on both operational safety and logisti-cal efficiency within low-altitude drone logistics net-works.Aiming at providing a systematic investiga-tion of real-world communication requirements and challenges encountered in Meituan UAV’s daily oper-ations,this article first introduces the operational sce-narios within current drone logistics networks and an-alyzes the related communication requirements.Then,the current communication solution and its inherent bottlenecks are elaborated.Finally,this paper explores emerging technologies and examines their application prospects in drone logistics networks.展开更多
With the rapid growth of the low-altitude economy,the demand for typical low-altitude ap-plications has accelerated the advancement of inte-grated sensing and communications(ISAC)networks.This paper begins by analyzin...With the rapid growth of the low-altitude economy,the demand for typical low-altitude ap-plications has accelerated the advancement of inte-grated sensing and communications(ISAC)networks.This paper begins by analyzing representative ap-plication scenarios to clarify the core requirements of the low-altitude economy for modern ISAC net-works.By investigating the distinctive characteris-tics of ISAC networks in low-altitude environments,it presents a comprehensive analysis of key challenges and identifies four major issues:challenges in pre-cise target detection,interference management,in-consistent sensing and communication coverage,and the complexity of air-ground coordination and han-dover.Based on fundamental theories and principles,the paper proposes corresponding solutions,encom-passing advanced technologies for precise target de-tection and recognition,high-reliability networked de-tection,robust interference management,and seamless air-ground collaboration.These solutions aim to es-tablish a solid foundation for the future development of intelligent low-altitude networks and ensure effec-tive support for emerging applications.展开更多
The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-d...The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security.However,the timely information of surrounding situation is difficult to acquire by UAVs,which further brings security risks.As a mature technology leveraged in traditional civil aviation,the Automatic Dependent Surveillance-Broadcast(ADS-B)realizes continuous surveillance of the information of aircraft.Consequently,we leverage ADS-B for surveillance and information broadcasting,and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.In detail,we propose the secure Sub-airSpaces Planning(SSP)algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees(PSO-RRT)algorithm for the UAV trajectory planning in law-altitude airspace.The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory,and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.展开更多
Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework n...Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework named the Formation Interfered Fluid Dynamical System(FIFDS) with Moderate Evasive Maneuver Strategy(MEMS) is proposed in this study.First, the UAV formation collision avoidance problem including quantifiable performance indexes is formulated. Second, inspired by the phenomenon of fluids continuously flowing while bypassing objects, the FIFDS for multiple UAVs is presented, which contains a Parallel Streamline Tracking(PST) method for formation keeping and the traditional IFDS for collision avoidance. Third, to rationally balance flight safety and collision avoidance cost, MEMS is proposed to generate moderate evasive maneuvers that match up with collision risks. Comprehensively containing the time and distance safety information, the 3-D dynamic collision regions are modeled for collision prediction. Then, the moderate evasive maneuver principle is refined, which provides criterions of the maneuver amplitude and direction. On this basis, an analytical parameter mapping mechanism is designed to online optimize IFDS parameters. Finally, the performance of the proposed method is validated by comparative simulation results and real flight experiments using fixed-wing UAVs.展开更多
Unauthorized operations referred to as“black flights”of unmanned aerial vehicles(UAVs)pose a significant danger to public safety,and existing low-attitude object detection algorithms encounter difficulties in balanc...Unauthorized operations referred to as“black flights”of unmanned aerial vehicles(UAVs)pose a significant danger to public safety,and existing low-attitude object detection algorithms encounter difficulties in balancing detection precision and speed.Additionally,their accuracy is insufficient,particularly for small objects in complex environments.To solve these problems,we propose a lightweight feature-enhanced convolutional neural network able to perform detection with high precision detection for low-attitude flying objects in real time to provide guidance information to suppress black-flying UAVs.The proposed network consists of three modules.A lightweight and stable feature extraction module is used to reduce the computational load and stably extract more low-level feature,an enhanced feature processing module significantly improves the feature extraction ability of the model,and an accurate detection module integrates low-level and advanced features to improve the multiscale detection accuracy in complex environments,particularly for small objects.The proposed method achieves a detection speed of 147 frames per second(FPS)and a mean average precision(mAP)of 90.97%for a dataset composed of flying objects,indicating its potential for low-altitude object detection.Furthermore,evaluation results based on microsoft common objects in context(MS COCO)indicate that the proposed method is also applicable to object detection in general.展开更多
With the rapid increase of Unmanned Aircraft Vehicle(UAV) numbers,the contradiction between extensive flight demands and limited low-altitude airspace resources has become increasingly prominent.To ensure the safety a...With the rapid increase of Unmanned Aircraft Vehicle(UAV) numbers,the contradiction between extensive flight demands and limited low-altitude airspace resources has become increasingly prominent.To ensure the safety and efficiency of low-altitude UAV operations,the low-altitude UAV public air route creatively proposed by the Chinese Academy of Sciences(CAS) and supported by the Civil Aviation Administration of China(CAAC) has been gradually recognized.However,present planning research on UAV low-altitude air route is not enough to explore how to use the ground transportation infrastructure,how to closely combine the surface pattern characteristics,and how to form the mechanism of "network".Based on the solution proposed in the early stage and related researches,this paper further deepens the exploration of the low-altitude public air route network and the implementation of key technologies and steps with an actual case study in Tianjin,China.Firstly,a path-planning environment consisting of favorable spaces,obstacle spaces,and mobile communication spaces for UAV flights was pre-constructed.Subsequently,air routes were planned by using the conflict detection and path re-planning algorithm.Our study also assessed the network by computing the population exposure risk index(PERI) and found that the index value was greatly reduced after the construction of the network,indicating that the network can effectively reduce the operational risk.In this study,a low-altitude UAV air route network in an actual region was constructed using multidisciplinary approaches such as remote sensing,geographic information,aviation,and transportation;it indirectly verified the rationality of the outcomes.This can provide practical solutions to low-altitude traffic problems in urban areas.展开更多
The“14th Five-Year Plan”and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters,promote the deep integration of the internet,big data,arti...The“14th Five-Year Plan”and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters,promote the deep integration of the internet,big data,artificial intelligence,blockchain technology,etc.with the real economy,facilitate the development of advanced manufacturing,and consider unmanned aerial vehicles(UAVs)as an important breakthrough,providing significant opportunities for the development of the UAV industry.Therefore,this article takes the current status of the UAV industry development as a starting point,analyzes the exploration and practice of the UAV development model based on the low-altitude economy,and discusses strategic suggestions to promote the development of UAVs empowered by the low-altitude economy.Through analysis,this article aims to provide theoretical references and practical guidance for promoting the sustainable development of the UAV industry under the wave of the low-altitude economy.展开更多
The“14th Five-Year Plan”and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters,promote the deep integration of the internet,big data,arti...The“14th Five-Year Plan”and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters,promote the deep integration of the internet,big data,artificial intelligence,blockchain technology,etc.with the real economy,facilitate the development of advanced manufacturing,and consider UAVs as an important breakthrough,providing significant opportunities for the development of the UAV industry.Therefore,this article takes the current status of the UAV industry development as a starting point,analyzes the exploration and practice of the UAV development model based on the low-altitude economy,and discusses strategic suggestions to promote the development of UAVs empowered by the low-altitude economy.Through analysis,this article aims to provide theoretical reference and practical guidance for promoting the sustainable development of the UAV industry under the wave of the low-altitude economy.展开更多
Taking Zhejiang Province as an example,this paper explores the mechanisms and implementation pathways through which the low-altitude economy drives the transformation and upgrading of the tourism industry.It finds tha...Taking Zhejiang Province as an example,this paper explores the mechanisms and implementation pathways through which the low-altitude economy drives the transformation and upgrading of the tourism industry.It finds that the low-altitude economy can effectively promote the development of high-end and diversified tourism in Zhejiang by innovating tourism formats,optimizing resource allocation,and enhancing tourist experiences.Besides,it analyzes the current development status of the low-altitude economy in Zhejiang and its potential for integration with tourism,revealing specific enabling pathways for tourism transformation,including low-altitude sightseeing,aviation tourism,and low-altitude sports.Finally,it proposes policy recommendations such as strengthening policy support,enhancing infrastructure development,and cultivating market entities.The findings aim to provide theoretical references and practical guidance for the high-quality development of tourism in Zhejiang Province.展开更多
As low-altitude airspace becomes increasingly accessible and eVTOL(electric vertical take-off and landing)technologies advance,the low-altitude economy has emerged as a transformative frontier in urban mobility and in...As low-altitude airspace becomes increasingly accessible and eVTOL(electric vertical take-off and landing)technologies advance,the low-altitude economy has emerged as a transformative frontier in urban mobility and industrial restructuring.Although countries face comparable technological opportunities,their development paths diverge significantly.This divergence is shaped not only by policy choices and innovation capacity but also by underlying differences in institutional architectures,resource configurations,and implementation mechanisms.This paper proposes a Development Path Evolution Model grounded in four structural elements:technological capability,institutional systems,infrastructure,and application scenarios.Based on this framework,the study identifies three archetypal path types(technology-led,institution-led,and scenario-driven)and empirically validates the model through comparative case studies of the United States,Europe,and Japan.Applying the model to China reveals a distinct"hybrid scenario-driven path",characterized by demand-responsive pilots,decentralized institutional flexibility,and strong engineering capacity.Using Shanghai as a representative case,the study outlines five strategic levers to guide its transition from a localized pilot zone to a platform-based governance hub with national and international relevance.The research contributes to theoretical understanding of path differentiation in emerging industries and provides actionable insights for developing economies with strong mobilization capacity and industrial ecosystems.展开更多
This paper proposes a portable broadband high-gain antenna for unmanned aerial vehicle(UAV)low-altitude control,operating within the dedicated remotely piloted aircraft system(RPAS)band(5.03–5.91 GHz).The total size ...This paper proposes a portable broadband high-gain antenna for unmanned aerial vehicle(UAV)low-altitude control,operating within the dedicated remotely piloted aircraft system(RPAS)band(5.03–5.91 GHz).The total size of the antenna is 240×240×187 mm3.It uses a printed log-periodic dipole antenna(PLPDA)as feed,and a four-layer frequency selective surface(FSS)cascaded as radome to enhance gain.Experimental results demonstrate that the antenna gain ranges from 10.1 to 15.9 dB and the half-power beam width(HPBW,2θ0.5)<23°within the operation band.Compared to existing portable UAV low-altitude control systems,the proposed antenna achieves an average gain enhancement of 4.7 dB.展开更多
High-performance positioning,navigation and timing(PNT)service is critical to the safe flight of low-altitude aircraft and the effective management of low altitude traffic.In low-altitude economic sce-narios,the speci...High-performance positioning,navigation and timing(PNT)service is critical to the safe flight of low-altitude aircraft and the effective management of low altitude traffic.In low-altitude economic sce-narios,the specificity of massive unmanned aerial ve-hicle(UAV)flights and the complexity of low-altitude airspace traffic management impose stringent demand on the high-continuity,high-accuracy,real-time,and high-security PNT service.However,the current PNT service,which primarily relies on Global Navigation Satellite System(GNSS),Micro-Electro-Mechanical System Inertial Navigation System(MEMS INS),etc.,is completely inadequate to support the future needs of low-altitude economic development.In order to bridge the huge gap between existing capability and future demand,a three-layer PNT architecture based on the collaboration of space-based,air-based and ground-based PNT systems is proposed for low-altitude econ-omy.The space-based layer consists of high,medium even possible low orbit GNSS constellations,such as BeiDou Navigation Satellite System(BDS),for high-precision,high-security absolute positioning and tim-ing.The air-based layer leverages inter-aircraft links for high-reliability dynamic relative positioning.The ground-based layer includes pseudolite network,as well as 5G-advanced(5G-A)/6G network,for more comprehensive coverage and real-time positioning.To this end,it is imperative to make breakthroughs in key technologies,from systems to airborne terminal,in-cluding but not limited to high-precision anti-jamming GNSS signal processing,high-reliability relative po-sitioning,real-time pseudolite positioning,and high-efficient multi-source information fusion at airborne terminal,etc.Due to the moderate redundancy,hetero-geneous mechanism,and multiple coverage from mul-tiple PNT systems,the proposed layered PNT archi-tecture possesses high robustness and resilient.Addi-tionally,the integration of INS,LiDAR and vision etc.perception technologies can significantly enhance the PNT capability.As a result,the proposed three-layer PNT architecture enable greater autonomy for low-altitude aircraft and intelligent traffic management for massive UAV operations,and promoting the safe and efficient development of the low-altitude economy.展开更多
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a ...With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Further...With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.展开更多
In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results i...In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.展开更多
文摘The large-scale development of the lowaltitude economy imposes increasingly stringent requirements on the supporting information infrastructure,necessitating the establishment of a low-altitude intelligent network(LAIN)with wide-area communication,high-precision navigation,and efficient supervision capabilities.Benefiting from its broad coverage,high reliability,and large bandwidth,the 5G cellular network serves as a critical foundation for LAIN construction.However,conventional cellular networks are primarily designed for two-dimensional terrestrial scenarios,and thus face significant limitations in coverage and interference resistance within complex three-dimensional low-altitude environments.To address the unique demands of LAIN applications,key challenges must be tackled,including achieving seamless three-dimensional coverage,mitigating interference in multi-dimensional network deployments,and ensuring stringent requirements for service quality and security supervision.This paper proposes an integrated LAIN architecture characterized by the convergence of communication,navigation,sensing,and management,enhanced with artificial intelligence and security mechanisms to improve overall system intelligence and resilience.Furthermore,this paper conducts an in-depth analysis of the critical challenges in LAIN deployment,explores enabling technologies to address these issues,and offers insights into the future development direction of low-altitude intelligent networks.
基金Feng Wei was supported by the National Natural Science Foundation of China under Grant 62425110.
文摘This paper proposes a novel blended hyper-cellular architecture for low-altitude aerial intelligent networks(LAINs)to provide agile coverage tailored to active air routes and takeoff/landing spots.Traditional cellular networks struggle to meet the dynamic demands of low-altitude UAV communications due to their rigid structures.The hyper-cellular network(HCN)architecture separates control and traffic coverage,enabling flexible and energy-efficient operations.The key components include control base stations(CBSs)for wide-area signaling coverage and traffic base stations(TBSs)that can be dynamically activated based on traffic demands.The proposed solution also integrates space information networks(SINs)to enhance the coverage efficiency.Key technologies such as all-G CBS using RISC-V architecture,AI-powered radio maps for low-altitude environments,and agile TBS coverage adaptation are introduced with some preliminary studies.These designs aim to address challenges like mobility management,interference coordination,and the need for real-time spectrum sharing in blended satellite-terrestrial networks.The proposed solution offers a scalable and agile framework to support the rapidly growing demand for reliable,low-latency,and high-capacity UAV communications in urban environments.
基金supported by Shenzhen Science and Technology Program(KJZD20230923115210021)。
文摘Communications system has a signifi-cant impact on both operational safety and logisti-cal efficiency within low-altitude drone logistics net-works.Aiming at providing a systematic investiga-tion of real-world communication requirements and challenges encountered in Meituan UAV’s daily oper-ations,this article first introduces the operational sce-narios within current drone logistics networks and an-alyzes the related communication requirements.Then,the current communication solution and its inherent bottlenecks are elaborated.Finally,this paper explores emerging technologies and examines their application prospects in drone logistics networks.
基金supported by National Science and Technology Major Project of China(Project Number:2024ZD1300100)Fundamental Research Funds for the central universities(2024RC02)+1 种基金National Natural Science Foundation of China(62401077,62321001)Beijing Municipal Natural Science Foundation(L232003)。
文摘With the rapid growth of the low-altitude economy,the demand for typical low-altitude ap-plications has accelerated the advancement of inte-grated sensing and communications(ISAC)networks.This paper begins by analyzing representative ap-plication scenarios to clarify the core requirements of the low-altitude economy for modern ISAC net-works.By investigating the distinctive characteris-tics of ISAC networks in low-altitude environments,it presents a comprehensive analysis of key challenges and identifies four major issues:challenges in pre-cise target detection,interference management,in-consistent sensing and communication coverage,and the complexity of air-ground coordination and han-dover.Based on fundamental theories and principles,the paper proposes corresponding solutions,encom-passing advanced technologies for precise target de-tection and recognition,high-reliability networked de-tection,robust interference management,and seamless air-ground collaboration.These solutions aim to es-tablish a solid foundation for the future development of intelligent low-altitude networks and ensure effec-tive support for emerging applications.
基金supported by the National Key R&D Program of China(No.2022YFB3104502)the National Natural Science Foundation of China(No.62301251)+2 种基金the Natural Science Foundation of Jiangsu Province of China under Project(No.BK20220883)the open research fund of National Mobile Communications Research Laboratory,Southeast University,China(No.2024D04)the Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001).
文摘The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security.However,the timely information of surrounding situation is difficult to acquire by UAVs,which further brings security risks.As a mature technology leveraged in traditional civil aviation,the Automatic Dependent Surveillance-Broadcast(ADS-B)realizes continuous surveillance of the information of aircraft.Consequently,we leverage ADS-B for surveillance and information broadcasting,and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.In detail,we propose the secure Sub-airSpaces Planning(SSP)algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees(PSO-RRT)algorithm for the UAV trajectory planning in law-altitude airspace.The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory,and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.
基金supported in part by the National Natural Science Foundations of China(Nos.61175084,61673042 and 62203046)the China Postdoctoral Science Foundation(No.2022M713006).
文摘Aiming to address the Unmanned Aerial Vehicle(UAV) formation collision avoidance problem in Three-Dimensional(3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework named the Formation Interfered Fluid Dynamical System(FIFDS) with Moderate Evasive Maneuver Strategy(MEMS) is proposed in this study.First, the UAV formation collision avoidance problem including quantifiable performance indexes is formulated. Second, inspired by the phenomenon of fluids continuously flowing while bypassing objects, the FIFDS for multiple UAVs is presented, which contains a Parallel Streamline Tracking(PST) method for formation keeping and the traditional IFDS for collision avoidance. Third, to rationally balance flight safety and collision avoidance cost, MEMS is proposed to generate moderate evasive maneuvers that match up with collision risks. Comprehensively containing the time and distance safety information, the 3-D dynamic collision regions are modeled for collision prediction. Then, the moderate evasive maneuver principle is refined, which provides criterions of the maneuver amplitude and direction. On this basis, an analytical parameter mapping mechanism is designed to online optimize IFDS parameters. Finally, the performance of the proposed method is validated by comparative simulation results and real flight experiments using fixed-wing UAVs.
基金supported by the National Natural Science Foundation of China(52075027)the Fundamental Research Funds for the Central Universities(2020XJJD03).
文摘Unauthorized operations referred to as“black flights”of unmanned aerial vehicles(UAVs)pose a significant danger to public safety,and existing low-attitude object detection algorithms encounter difficulties in balancing detection precision and speed.Additionally,their accuracy is insufficient,particularly for small objects in complex environments.To solve these problems,we propose a lightweight feature-enhanced convolutional neural network able to perform detection with high precision detection for low-attitude flying objects in real time to provide guidance information to suppress black-flying UAVs.The proposed network consists of three modules.A lightweight and stable feature extraction module is used to reduce the computational load and stably extract more low-level feature,an enhanced feature processing module significantly improves the feature extraction ability of the model,and an accurate detection module integrates low-level and advanced features to improve the multiscale detection accuracy in complex environments,particularly for small objects.The proposed method achieves a detection speed of 147 frames per second(FPS)and a mean average precision(mAP)of 90.97%for a dataset composed of flying objects,indicating its potential for low-altitude object detection.Furthermore,evaluation results based on microsoft common objects in context(MS COCO)indicate that the proposed method is also applicable to object detection in general.
基金National Key Research and Development Program of China,No.2017YFB0503005Key Research Program of the Chinese Academy of Sciences,No.ZDRW-KT-2020-2+1 种基金National Natural Science Foundation of China,No.41971359,No.41771388Tianjin Intelligent Manufacturing Project Technology of Intelligent Networking by Autonomous Control UAVs for Observation and Application,No.Tianjin-IMP-2。
文摘With the rapid increase of Unmanned Aircraft Vehicle(UAV) numbers,the contradiction between extensive flight demands and limited low-altitude airspace resources has become increasingly prominent.To ensure the safety and efficiency of low-altitude UAV operations,the low-altitude UAV public air route creatively proposed by the Chinese Academy of Sciences(CAS) and supported by the Civil Aviation Administration of China(CAAC) has been gradually recognized.However,present planning research on UAV low-altitude air route is not enough to explore how to use the ground transportation infrastructure,how to closely combine the surface pattern characteristics,and how to form the mechanism of "network".Based on the solution proposed in the early stage and related researches,this paper further deepens the exploration of the low-altitude public air route network and the implementation of key technologies and steps with an actual case study in Tianjin,China.Firstly,a path-planning environment consisting of favorable spaces,obstacle spaces,and mobile communication spaces for UAV flights was pre-constructed.Subsequently,air routes were planned by using the conflict detection and path re-planning algorithm.Our study also assessed the network by computing the population exposure risk index(PERI) and found that the index value was greatly reduced after the construction of the network,indicating that the network can effectively reduce the operational risk.In this study,a low-altitude UAV air route network in an actual region was constructed using multidisciplinary approaches such as remote sensing,geographic information,aviation,and transportation;it indirectly verified the rationality of the outcomes.This can provide practical solutions to low-altitude traffic problems in urban areas.
文摘The“14th Five-Year Plan”and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters,promote the deep integration of the internet,big data,artificial intelligence,blockchain technology,etc.with the real economy,facilitate the development of advanced manufacturing,and consider unmanned aerial vehicles(UAVs)as an important breakthrough,providing significant opportunities for the development of the UAV industry.Therefore,this article takes the current status of the UAV industry development as a starting point,analyzes the exploration and practice of the UAV development model based on the low-altitude economy,and discusses strategic suggestions to promote the development of UAVs empowered by the low-altitude economy.Through analysis,this article aims to provide theoretical references and practical guidance for promoting the sustainable development of the UAV industry under the wave of the low-altitude economy.
文摘The“14th Five-Year Plan”and the Long-Range Objectives Through the Year 2035 propose to strengthen the construction of strategic emerging industrial clusters,promote the deep integration of the internet,big data,artificial intelligence,blockchain technology,etc.with the real economy,facilitate the development of advanced manufacturing,and consider UAVs as an important breakthrough,providing significant opportunities for the development of the UAV industry.Therefore,this article takes the current status of the UAV industry development as a starting point,analyzes the exploration and practice of the UAV development model based on the low-altitude economy,and discusses strategic suggestions to promote the development of UAVs empowered by the low-altitude economy.Through analysis,this article aims to provide theoretical reference and practical guidance for promoting the sustainable development of the UAV industry under the wave of the low-altitude economy.
文摘Taking Zhejiang Province as an example,this paper explores the mechanisms and implementation pathways through which the low-altitude economy drives the transformation and upgrading of the tourism industry.It finds that the low-altitude economy can effectively promote the development of high-end and diversified tourism in Zhejiang by innovating tourism formats,optimizing resource allocation,and enhancing tourist experiences.Besides,it analyzes the current development status of the low-altitude economy in Zhejiang and its potential for integration with tourism,revealing specific enabling pathways for tourism transformation,including low-altitude sightseeing,aviation tourism,and low-altitude sports.Finally,it proposes policy recommendations such as strengthening policy support,enhancing infrastructure development,and cultivating market entities.The findings aim to provide theoretical references and practical guidance for the high-quality development of tourism in Zhejiang Province.
文摘As low-altitude airspace becomes increasingly accessible and eVTOL(electric vertical take-off and landing)technologies advance,the low-altitude economy has emerged as a transformative frontier in urban mobility and industrial restructuring.Although countries face comparable technological opportunities,their development paths diverge significantly.This divergence is shaped not only by policy choices and innovation capacity but also by underlying differences in institutional architectures,resource configurations,and implementation mechanisms.This paper proposes a Development Path Evolution Model grounded in four structural elements:technological capability,institutional systems,infrastructure,and application scenarios.Based on this framework,the study identifies three archetypal path types(technology-led,institution-led,and scenario-driven)and empirically validates the model through comparative case studies of the United States,Europe,and Japan.Applying the model to China reveals a distinct"hybrid scenario-driven path",characterized by demand-responsive pilots,decentralized institutional flexibility,and strong engineering capacity.Using Shanghai as a representative case,the study outlines five strategic levers to guide its transition from a localized pilot zone to a platform-based governance hub with national and international relevance.The research contributes to theoretical understanding of path differentiation in emerging industries and provides actionable insights for developing economies with strong mobilization capacity and industrial ecosystems.
文摘This paper proposes a portable broadband high-gain antenna for unmanned aerial vehicle(UAV)low-altitude control,operating within the dedicated remotely piloted aircraft system(RPAS)band(5.03–5.91 GHz).The total size of the antenna is 240×240×187 mm3.It uses a printed log-periodic dipole antenna(PLPDA)as feed,and a four-layer frequency selective surface(FSS)cascaded as radome to enhance gain.Experimental results demonstrate that the antenna gain ranges from 10.1 to 15.9 dB and the half-power beam width(HPBW,2θ0.5)<23°within the operation band.Compared to existing portable UAV low-altitude control systems,the proposed antenna achieves an average gain enhancement of 4.7 dB.
基金supported in part by the National Key R&D Program of China under Grant 2021YFA0716600 and 2024ZD1300100in part by the National Natural Science Foundation of China under Grant 42274018,42425401,62371029,62271285 and U2233217.
文摘High-performance positioning,navigation and timing(PNT)service is critical to the safe flight of low-altitude aircraft and the effective management of low altitude traffic.In low-altitude economic sce-narios,the specificity of massive unmanned aerial ve-hicle(UAV)flights and the complexity of low-altitude airspace traffic management impose stringent demand on the high-continuity,high-accuracy,real-time,and high-security PNT service.However,the current PNT service,which primarily relies on Global Navigation Satellite System(GNSS),Micro-Electro-Mechanical System Inertial Navigation System(MEMS INS),etc.,is completely inadequate to support the future needs of low-altitude economic development.In order to bridge the huge gap between existing capability and future demand,a three-layer PNT architecture based on the collaboration of space-based,air-based and ground-based PNT systems is proposed for low-altitude econ-omy.The space-based layer consists of high,medium even possible low orbit GNSS constellations,such as BeiDou Navigation Satellite System(BDS),for high-precision,high-security absolute positioning and tim-ing.The air-based layer leverages inter-aircraft links for high-reliability dynamic relative positioning.The ground-based layer includes pseudolite network,as well as 5G-advanced(5G-A)/6G network,for more comprehensive coverage and real-time positioning.To this end,it is imperative to make breakthroughs in key technologies,from systems to airborne terminal,in-cluding but not limited to high-precision anti-jamming GNSS signal processing,high-reliability relative po-sitioning,real-time pseudolite positioning,and high-efficient multi-source information fusion at airborne terminal,etc.Due to the moderate redundancy,hetero-geneous mechanism,and multiple coverage from mul-tiple PNT systems,the proposed layered PNT archi-tecture possesses high robustness and resilient.Addi-tionally,the integration of INS,LiDAR and vision etc.perception technologies can significantly enhance the PNT capability.As a result,the proposed three-layer PNT architecture enable greater autonomy for low-altitude aircraft and intelligent traffic management for massive UAV operations,and promoting the safe and efficient development of the low-altitude economy.
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
基金National Natural Science Foundation of China(Grant No.62103434)National Science Fund for Distinguished Young Scholars(Grant No.62176263).
文摘With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
基金supported by the National Natural Science Foundation of China under Grant 62471493supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066。
文摘With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.
文摘In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.
