Pub Date : 2024-12-16DOI: 10.1109/JMASS.2024.3516312
Jindou Xie;Mengqi Shi;Yixuan Liu
With the demand for real-time data processing in mobile environments has surged, uncrewed aerial vehicle (UAVs) are regrading as flying base stations (BSs) for real-time application and emergency communication. In this article, we investigate a task collaborative offloading in UAV-assisted edge computing environments, integrating dynamic pricing mechanisms and UAVS group formation to optimize resource allocation. We explore the challenges posed by the heterogeneity of UAVs and the dynamic workload distribution. Our proposed system leverages a multiagent deep reinforcement learning framework to intelligently assist computing UAVs to form a collaborative group, considering the constraints of latency, service budget, and computational capacity. The dynamic pricing model incentivizes leading UAV to help efficient task offloading by task collaborative scheduling within groups and task relaying to BS. Through extensive simulations, we demonstrate that our approach significantly enhances the overall system performance, reduces task completion time, and optimizes resource utilization.
{"title":"Task Collaborative Offloading for UAV-Assisted Edge Computing With Dynamic Pricing","authors":"Jindou Xie;Mengqi Shi;Yixuan Liu","doi":"10.1109/JMASS.2024.3516312","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3516312","url":null,"abstract":"With the demand for real-time data processing in mobile environments has surged, uncrewed aerial vehicle (UAVs) are regrading as flying base stations (BSs) for real-time application and emergency communication. In this article, we investigate a task collaborative offloading in UAV-assisted edge computing environments, integrating dynamic pricing mechanisms and UAVS group formation to optimize resource allocation. We explore the challenges posed by the heterogeneity of UAVs and the dynamic workload distribution. Our proposed system leverages a multiagent deep reinforcement learning framework to intelligently assist computing UAVs to form a collaborative group, considering the constraints of latency, service budget, and computational capacity. The dynamic pricing model incentivizes leading UAV to help efficient task offloading by task collaborative scheduling within groups and task relaying to BS. Through extensive simulations, we demonstrate that our approach significantly enhances the overall system performance, reduces task completion time, and optimizes resource utilization.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"6 2","pages":"157-164"},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article investigates the resource sharing problem in a multiuncrewed aerial vehicle (UAV) wireless network by utilizing the multiagent reinforcement learning (MARL) method. Specifically, the considered multi-UAV system involves two transmission modes, i.e., UAV-to-device (U2D) mode and UAV-to-network (U2N) mode, in which the U2D mode is allowed to reuse the spectrum of U2N mode to improve the spectrum efficiency. Then, we formulate an optimization problem to maximize the throughput of U2D links by jointly optimizing the channel allocation, power level selection, and UAV trajectory, while ensuring the communication quality of U2N links. Due to the highly complex and dynamic nature, as well as the challenging nonconvex objective function and constraints, the resulting problem is hard to address. Accordingly, we propose a novel multiagent deep deterministic policy gradient (MADDPG)-based resource allocation and multi-UAV trajectory optimization policy. Simulation results illustrate the efficacy of our method in improving the system transmission rate.
{"title":"Multiagent Reinforcement Learning-Based Resource Sharing in Multi-UAV Wireless Networks","authors":"Yaxiu Zhang;Mingan Luan;Zheng Chang;Timo Hämäläinen","doi":"10.1109/JMASS.2024.3510808","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3510808","url":null,"abstract":"This article investigates the resource sharing problem in a multiuncrewed aerial vehicle (UAV) wireless network by utilizing the multiagent reinforcement learning (MARL) method. Specifically, the considered multi-UAV system involves two transmission modes, i.e., UAV-to-device (U2D) mode and UAV-to-network (U2N) mode, in which the U2D mode is allowed to reuse the spectrum of U2N mode to improve the spectrum efficiency. Then, we formulate an optimization problem to maximize the throughput of U2D links by jointly optimizing the channel allocation, power level selection, and UAV trajectory, while ensuring the communication quality of U2N links. Due to the highly complex and dynamic nature, as well as the challenging nonconvex objective function and constraints, the resulting problem is hard to address. Accordingly, we propose a novel multiagent deep deterministic policy gradient (MADDPG)-based resource allocation and multi-UAV trajectory optimization policy. Simulation results illustrate the efficacy of our method in improving the system transmission rate.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"6 2","pages":"103-112"},"PeriodicalIF":0.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1109/JMASS.2024.3507735
Xiangwei Bu;Ruining Luo;Jiaxi Chen;Humin Lei
Our objective is to explore a finite-time tracking control protocol with fragility-rejection for discrete-time systems subject to saturation constrained dynamics, specifically in the field of UAV flight control. This protocol is capable of imposing desired transient and steady-state behaviors on tracking errors, while introducing transformed errors utilizing finite-time performance functions and stabilizing them indirectly through feedback terms developed using these functions in a back-stepping-like control design. Our approach introduces a structure that distinguishes it from existing transformed-error-stabilization-based prescribed performance control (PPC) methods. Furthermore, we propose a compensated system to modify the final feedback term and address actuator saturation, effectively resolving the challenging fragility issue associated with existing PPC approaches caused by error fluctuation due to actuator saturation in discrete-time systems. Finally, comparative simulation results obtained for flight control applications validate the effectiveness of our design.
{"title":"Fragility-Rejection UAV Flight Control With Discrete-Time Constrained Dynamics Endowing Preselected Qualities","authors":"Xiangwei Bu;Ruining Luo;Jiaxi Chen;Humin Lei","doi":"10.1109/JMASS.2024.3507735","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3507735","url":null,"abstract":"Our objective is to explore a finite-time tracking control protocol with fragility-rejection for discrete-time systems subject to saturation constrained dynamics, specifically in the field of UAV flight control. This protocol is capable of imposing desired transient and steady-state behaviors on tracking errors, while introducing transformed errors utilizing finite-time performance functions and stabilizing them indirectly through feedback terms developed using these functions in a back-stepping-like control design. Our approach introduces a structure that distinguishes it from existing transformed-error-stabilization-based prescribed performance control (PPC) methods. Furthermore, we propose a compensated system to modify the final feedback term and address actuator saturation, effectively resolving the challenging fragility issue associated with existing PPC approaches caused by error fluctuation due to actuator saturation in discrete-time systems. Finally, comparative simulation results obtained for flight control applications validate the effectiveness of our design.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"6 1","pages":"27-35"},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-25DOI: 10.1109/JMASS.2024.3504992
{"title":"2024 Index IEEE Journal on Miniaturization for Air and Space Systems Vol. 5","authors":"","doi":"10.1109/JMASS.2024.3504992","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3504992","url":null,"abstract":"","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"274-281"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766876","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-20DOI: 10.1109/JMASS.2024.3496303
{"title":"The Journal of Miniaturized Air and Space Systems","authors":"","doi":"10.1109/JMASS.2024.3496303","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3496303","url":null,"abstract":"","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10759326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1109/JMASS.2024.3491319
Shilpi Singh;Shakti Singh Chauhan;Ananjan Basu
This article presents a dual circularly polarized slotted waveguide leaky wave antenna for CubeSat communications at W-band. The proposed fully metallic, low profile, and high-performing antenna offers wideband operating bandwidth, which makes it suitable for space applications. To achieve circular polarization, an array of circular holes is perforated at an offset position from the narrow wall of the WR-10 waveguide. The prototype antenna provides a wide axial ratio bandwidth of 13% and an average half-power beamwidth of 4.5° on the elevation plane. At high frequencies, the thickness of the slot affects the emission through the slot, which is not typically encountered at low frequencies. Therefore, to increase the magnitude of the radiated power, the wall thickness of the hole is reduced. The proposed circular hole slotted waveguide antenna design provides superior tolerance, accuracy, and precision compared to any other structures. These characteristics eliminate fabrication challenges, especially within the W-band, and can seamlessly extend into the sub-THz domain as well. The proposed antenna is robust, easy to fabricate, and appropriate for integration into CubeSat. It can be adapted for W-band CubeSat LEO, intersatellite, and constellation missions.
{"title":"A Low Profile Wideband Circularly Polarized Slotted Waveguide Antenna for W-Band CubeSat Data-Links","authors":"Shilpi Singh;Shakti Singh Chauhan;Ananjan Basu","doi":"10.1109/JMASS.2024.3491319","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3491319","url":null,"abstract":"This article presents a dual circularly polarized slotted waveguide leaky wave antenna for CubeSat communications at W-band. The proposed fully metallic, low profile, and high-performing antenna offers wideband operating bandwidth, which makes it suitable for space applications. To achieve circular polarization, an array of circular holes is perforated at an offset position from the narrow wall of the WR-10 waveguide. The prototype antenna provides a wide axial ratio bandwidth of 13% and an average half-power beamwidth of 4.5° on the elevation plane. At high frequencies, the thickness of the slot affects the emission through the slot, which is not typically encountered at low frequencies. Therefore, to increase the magnitude of the radiated power, the wall thickness of the hole is reduced. The proposed circular hole slotted waveguide antenna design provides superior tolerance, accuracy, and precision compared to any other structures. These characteristics eliminate fabrication challenges, especially within the W-band, and can seamlessly extend into the sub-THz domain as well. The proposed antenna is robust, easy to fabricate, and appropriate for integration into CubeSat. It can be adapted for W-band CubeSat LEO, intersatellite, and constellation missions.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"6 1","pages":"19-26"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the randomness of channel fading, communication devices, and malicious interference sources, uncrewed aerial vehicles (UAVs) face a complex and ever-changing task scenario, which poses significant communication security challenges, such as transmission outages. Fortunately, these communication security challenges can be transformed into path-planning problems that minimize the weighted sum of UAV mission time and transmission outage time. In order to design the complex communication environment faced by UAVs in actual scenarios, we propose a system model, including building distribution, communication channel, and antenna design, in this article. Besides, we introduce other UAVs with fixed flight paths and ground interference resources with random locations to ensure mission UAVs have better anti-interference ability. However, it is challenging for classical search algorithms and heuristic algorithms to cope with the complex path problems mentioned above. In this article, we propose an improved deep deterministic policy gradient (DDPG) algorithm with better performance compared with basic DDPG and double deep Q-network learning (DDQN) algorithms. Specifically, a post-decision state (PDS) mechanism has been introduced to accelerate the convergence rate and enhance the stability of the training process. In addition, a transmission outage probability experience memory (TOPEM) has been designed to quickly generate wireless communication quality maps and provide temporary experience for the post-decision process, resulting in better training results. Simulation experiments have proven that, compared to basic DDPG, the improved algorithm increases training speed by at least 50 %, significantly improves convergence rate, and reduces the episode required for convergence to 20 %. It can alsohelp UAVs choose better paths than basic DDPG and DDQN algorithms.
{"title":"Cellular Connected UAV Anti-Interference Path Planning Based on PDS-DDPG and TOPEM","authors":"Quanxi Zhou;Yongjing Wang;Ruiyu Shen;Jin Nakazato;Manabu Tsukada;Zhenyu Guan","doi":"10.1109/JMASS.2024.3490762","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3490762","url":null,"abstract":"Due to the randomness of channel fading, communication devices, and malicious interference sources, uncrewed aerial vehicles (UAVs) face a complex and ever-changing task scenario, which poses significant communication security challenges, such as transmission outages. Fortunately, these communication security challenges can be transformed into path-planning problems that minimize the weighted sum of UAV mission time and transmission outage time. In order to design the complex communication environment faced by UAVs in actual scenarios, we propose a system model, including building distribution, communication channel, and antenna design, in this article. Besides, we introduce other UAVs with fixed flight paths and ground interference resources with random locations to ensure mission UAVs have better anti-interference ability. However, it is challenging for classical search algorithms and heuristic algorithms to cope with the complex path problems mentioned above. In this article, we propose an improved deep deterministic policy gradient (DDPG) algorithm with better performance compared with basic DDPG and double deep Q-network learning (DDQN) algorithms. Specifically, a post-decision state (PDS) mechanism has been introduced to accelerate the convergence rate and enhance the stability of the training process. In addition, a transmission outage probability experience memory (TOPEM) has been designed to quickly generate wireless communication quality maps and provide temporary experience for the post-decision process, resulting in better training results. Simulation experiments have proven that, compared to basic DDPG, the improved algorithm increases training speed by at least 50 %, significantly improves convergence rate, and reduces the episode required for convergence to 20 %. It can alsohelp UAVs choose better paths than basic DDPG and DDQN algorithms.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"6 1","pages":"2-18"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-25DOI: 10.1109/JMASS.2024.3486374
Yanpeng Dai;Lijiao Zhang
Uncrewed aerial vehicle (UAV)-assisted mobile-edge computing (MEC) has been a promising architecture to enable seamless aerial computing and communications. With evolving requirements of heterogeneous services in future wireless networks, it is challenging to realize on-demand resource management and network deployment in UAV-assisted MEC systems. This article investigates unified communication and computation resource management as well as network deployment to meet the quality of service (QoS) of enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC) simultaneously. A network utility minimization problem is formulated which jointly considers UAV deployment, user association, spectrum slicing, communication, and computation resource allocation. First, a coalition game-based UAV deployment and eMBB user (eUE) association algorithm is designed, based on which a communication and computation resource allocation algorithm is devised by convex optimization. The mMTC user (mUE) association and power control is optimized via successive convex approximation. Then, a spectrum slicing and allocation algorithm is designed by the bisection search method. Finally, a joint resource allocation and network deployment scheme is proposed. Simulation results demonstrate that our proposed algorithm can effectively reduce average service delay of eUEs and increase the number of served mUEs in UAV-assisted MEC systems.
{"title":"Heterogeneous Service-Oriented Resource Provisioning and UAV Deployment for Aerial Edge Computing Networks","authors":"Yanpeng Dai;Lijiao Zhang","doi":"10.1109/JMASS.2024.3486374","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3486374","url":null,"abstract":"Uncrewed aerial vehicle (UAV)-assisted mobile-edge computing (MEC) has been a promising architecture to enable seamless aerial computing and communications. With evolving requirements of heterogeneous services in future wireless networks, it is challenging to realize on-demand resource management and network deployment in UAV-assisted MEC systems. This article investigates unified communication and computation resource management as well as network deployment to meet the quality of service (QoS) of enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC) simultaneously. A network utility minimization problem is formulated which jointly considers UAV deployment, user association, spectrum slicing, communication, and computation resource allocation. First, a coalition game-based UAV deployment and eMBB user (eUE) association algorithm is designed, based on which a communication and computation resource allocation algorithm is devised by convex optimization. The mMTC user (mUE) association and power control is optimized via successive convex approximation. Then, a spectrum slicing and allocation algorithm is designed by the bisection search method. Finally, a joint resource allocation and network deployment scheme is proposed. Simulation results demonstrate that our proposed algorithm can effectively reduce average service delay of eUEs and increase the number of served mUEs in UAV-assisted MEC systems.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"6 2","pages":"133-143"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1109/JMASS.2024.3479456
Hongjiang Lei;Mingxu Yang;Jiacheng Jiang;Ki-Hong Park;Gaofeng Pan
Mobile edge computing (MEC) technology can reduce user latency and energy consumption by offloading computationally intensive tasks to the edge servers. Uncrewed aerial vehicles (UAVs) and nonorthogonal multiple access (NOMA) technology enable the MEC networks to provide offloaded computing services for massively accessed terrestrial users conveniently. However, the broadcast nature of signal propagation in NOMA-based UAV-MEC networks makes it vulnerable to eavesdropping by malicious eavesdroppers. In this work, a secure offload scheme is proposed for NOMA-based UAV-MEC systems with the existence of an aerial eavesdropper. The long-term average network computational cost is minimized by jointly designing the UAV’s trajectory, the terrestrial users’ transmit power, and computational frequency while ensuring the security of users’ offloaded data. Due to the eavesdropper’s location uncertainty, the worst-case security scenario is considered through the estimated eavesdropping range. Due to the high-dimensional continuous action space, the deep deterministic policy gradient algorithm is utilized to solve the nonconvex optimization problem. Simulation results validate the effectiveness of the proposed scheme.
{"title":"Secure Offloading in NOMA-Aided Aerial MEC Systems Based on Deep Reinforcement Learning","authors":"Hongjiang Lei;Mingxu Yang;Jiacheng Jiang;Ki-Hong Park;Gaofeng Pan","doi":"10.1109/JMASS.2024.3479456","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3479456","url":null,"abstract":"Mobile edge computing (MEC) technology can reduce user latency and energy consumption by offloading computationally intensive tasks to the edge servers. Uncrewed aerial vehicles (UAVs) and nonorthogonal multiple access (NOMA) technology enable the MEC networks to provide offloaded computing services for massively accessed terrestrial users conveniently. However, the broadcast nature of signal propagation in NOMA-based UAV-MEC networks makes it vulnerable to eavesdropping by malicious eavesdroppers. In this work, a secure offload scheme is proposed for NOMA-based UAV-MEC systems with the existence of an aerial eavesdropper. The long-term average network computational cost is minimized by jointly designing the UAV’s trajectory, the terrestrial users’ transmit power, and computational frequency while ensuring the security of users’ offloaded data. Due to the eavesdropper’s location uncertainty, the worst-case security scenario is considered through the estimated eavesdropping range. Due to the high-dimensional continuous action space, the deep deterministic policy gradient algorithm is utilized to solve the nonconvex optimization problem. Simulation results validate the effectiveness of the proposed scheme.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"6 2","pages":"113-124"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1109/JMASS.2024.3479151
Ju Gao;Zhangziyi Jin;Zonghui Li;Zixian Chen;Qingwang Wang
As unmanned aerial vehicles (UAVs) continue to play an increasingly critical role in reconnaissance missions, establishing dependable communication links between UAVs and ground stations has become imperative. Nevertheless, ensuring reliable communication remains a great challenge, particularly in environments characterized by weak signals or high levels of electromagnetic interference. To tackle this challenge, this study presents a design and optimization approach for a miniature UAV antenna. This antenna achieves significant performance improvements by optimizing the magnetic field (MF) distribution and convergence within its central section. Specifically with the aim of capturing and amplifying signals in a specified direction, the antenna enhances reception sensitivity, especially in challenging operational settings. The structure ensures robust and consistent signal reception with a maximum gain of up to 12.8 dB and a converging MF magnitude of 2279 A/m at its center. Furthermore, it operates effectively within the C band, exhibiting a relative bandwidth of 12.2%. This capability empowers UAV to transmit reconnaissance data accurately and swiftly, regardless of the distance traveled or the complexity of the electromagnetic environment. This advancement not only enhances UAV capabilities but also opens new possibility for applications requiring dependable communication in diverse and demanding scenarios.
{"title":"Broadband Miniaturized Antenna Based on Enhanced Magnetic Field Convergence in UAV","authors":"Ju Gao;Zhangziyi Jin;Zonghui Li;Zixian Chen;Qingwang Wang","doi":"10.1109/JMASS.2024.3479151","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3479151","url":null,"abstract":"As unmanned aerial vehicles (UAVs) continue to play an increasingly critical role in reconnaissance missions, establishing dependable communication links between UAVs and ground stations has become imperative. Nevertheless, ensuring reliable communication remains a great challenge, particularly in environments characterized by weak signals or high levels of electromagnetic interference. To tackle this challenge, this study presents a design and optimization approach for a miniature UAV antenna. This antenna achieves significant performance improvements by optimizing the magnetic field (MF) distribution and convergence within its central section. Specifically with the aim of capturing and amplifying signals in a specified direction, the antenna enhances reception sensitivity, especially in challenging operational settings. The structure ensures robust and consistent signal reception with a maximum gain of up to 12.8 dB and a converging MF magnitude of 2279 A/m at its center. Furthermore, it operates effectively within the C band, exhibiting a relative bandwidth of 12.2%. This capability empowers UAV to transmit reconnaissance data accurately and swiftly, regardless of the distance traveled or the complexity of the electromagnetic environment. This advancement not only enhances UAV capabilities but also opens new possibility for applications requiring dependable communication in diverse and demanding scenarios.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"265-273"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：