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}
Deep neural network-based image classification models are vulnerable to adversarial examples, which are meticulously crafted to mislead the model by adding perturbations to clean images. Although adversarial training demonstrates outstanding performance in enhancing models robustness against adversarial examples, it often incurs the expense of accuracy. To address this problem, this article proposes a strategy to achieve a better tradeoff between accuracy and robustness, which mainly consists of symbol perturbations and examples mixing. First, we employ a symbol processing approach for randomly generated initial perturbations, which makes model identify the correct parameter attack direction faster during the training process. Second, we put forward a methodology that utilizes a mixture of different examples to generate more distinct adversarial features. Further, we utilize scaling conditions for tensor feature modulation, enabling the model to achieve both improved accuracy and robustness after learning more diverse adversarial features. Finally, we conduct extensive experiments to show the feasibility and effectiveness of the proposed methods.
{"title":"Toward a Better Tradeoff Between Accuracy and Robustness for Image Classification via Adversarial Feature Diversity","authors":"Wei Xue;Yonghao Wang;Yuchi Wang;Yue Wang;Mingyang Du;Xiao Zheng","doi":"10.1109/JMASS.2024.3462548","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3462548","url":null,"abstract":"Deep neural network-based image classification models are vulnerable to adversarial examples, which are meticulously crafted to mislead the model by adding perturbations to clean images. Although adversarial training demonstrates outstanding performance in enhancing models robustness against adversarial examples, it often incurs the expense of accuracy. To address this problem, this article proposes a strategy to achieve a better tradeoff between accuracy and robustness, which mainly consists of symbol perturbations and examples mixing. First, we employ a symbol processing approach for randomly generated initial perturbations, which makes model identify the correct parameter attack direction faster during the training process. Second, we put forward a methodology that utilizes a mixture of different examples to generate more distinct adversarial features. Further, we utilize scaling conditions for tensor feature modulation, enabling the model to achieve both improved accuracy and robustness after learning more diverse adversarial features. Finally, we conduct extensive experiments to show the feasibility and effectiveness of the proposed methods.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"254-264"},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679371","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 : 2024-08-29DOI: 10.1109/JMASS.2024.3451477
Li Zhao;Chuan Qin;Qiuni Li;Chongchong Han;Jialong Jian;Yuanfei Liu
An improved dynamic surface control (IDSC) method is proposed for a class of strict-feedback nonlinear systems with internal uncertainties and external disturbances. First, compared with the typical first-order sliding-mode differentiator, this article presents an improved method to obtain the first-order differential approximation of the virtual control signals, which tackles the obstacle of “explosion of complexity.” Second, to eliminate the effect of filtering errors that exist in traditional dynamic surface control method, in this article, the tracking errors are directly constructed using the virtual control signal. Third, composite disturbances were estimated and compensated by designing a novel disturbance observer, which eliminates the limitations that the disturbance terms must be differentiable or even slow tensors. Finally, to illustrate that the proposed method has a great ability to suppress fast time-varying and nondifferentiable disturbances, the simulation results of a numerical example and a practical example of a modern advanced fighter jet system were presented.
{"title":"Improved Dynamic Surface Control for Uncertain Nonlinear Systems With Application to Fighter Jet System","authors":"Li Zhao;Chuan Qin;Qiuni Li;Chongchong Han;Jialong Jian;Yuanfei Liu","doi":"10.1109/JMASS.2024.3451477","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3451477","url":null,"abstract":"An improved dynamic surface control (IDSC) method is proposed for a class of strict-feedback nonlinear systems with internal uncertainties and external disturbances. First, compared with the typical first-order sliding-mode differentiator, this article presents an improved method to obtain the first-order differential approximation of the virtual control signals, which tackles the obstacle of “explosion of complexity.” Second, to eliminate the effect of filtering errors that exist in traditional dynamic surface control method, in this article, the tracking errors are directly constructed using the virtual control signal. Third, composite disturbances were estimated and compensated by designing a novel disturbance observer, which eliminates the limitations that the disturbance terms must be differentiable or even slow tensors. Finally, to illustrate that the proposed method has a great ability to suppress fast time-varying and nondifferentiable disturbances, the simulation results of a numerical example and a practical example of a modern advanced fighter jet system were presented.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"246-253"},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679334","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 : 2024-08-28DOI: 10.1109/JMASS.2024.3451011
B. Sainath;Sai Kartik Tadinada
Rapid advancements in the Internet of Things (IoT), uncrewed aerial vehicles (UAVs), and energy harvesting (EH) technologies can be leveraged to design and develop green and reliable cooperative Cube satellite communication (CSC) systems and networks. In this work, we propose a novel cooperative CSC system model comprising green UAVs as intelligent relays equipped with IoT sensors, intelligent processing and EH modules, and transceivers. Using a novel and intelligent probabilistic transmission policy (PTP) that we propose, CubeSats can conserve energy by deactivating transmissions in unfavorable weather conditions based on control signals from the smart UAV via a telemetry link. We extend this model to include multiple CubeSats and analyze it by deriving and evaluating network energy efficiency and its lower bound. Our numerical plots show that the proposed PTP significantly outperforms the continuous transmission policy (CTP). At a specific transmission probability of 0.125, PTP is 40 times more energy efficient than CTP. We extend the work and develop a novel and insightful performance analysis for energy efficiency outage (EEO) probability. Specifically, we derive closed-form approximate expressions for EEO probability and present numerical results. Furthermore, we analyze the performance of clustered CSC networks (CSCNs) and present numerical results to assess EEO probability, providing valuable insights for future large-scale green CSCN design and deployment.
{"title":"Environment-Aware Green UAV-Assisted, CubeSat Communication Network Energy Efficiency, and Outage Probability Analysis","authors":"B. Sainath;Sai Kartik Tadinada","doi":"10.1109/JMASS.2024.3451011","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3451011","url":null,"abstract":"Rapid advancements in the Internet of Things (IoT), uncrewed aerial vehicles (UAVs), and energy harvesting (EH) technologies can be leveraged to design and develop green and reliable cooperative Cube satellite communication (CSC) systems and networks. In this work, we propose a novel cooperative CSC system model comprising green UAVs as intelligent relays equipped with IoT sensors, intelligent processing and EH modules, and transceivers. Using a novel and intelligent probabilistic transmission policy (PTP) that we propose, CubeSats can conserve energy by deactivating transmissions in unfavorable weather conditions based on control signals from the smart UAV via a telemetry link. We extend this model to include multiple CubeSats and analyze it by deriving and evaluating network energy efficiency and its lower bound. Our numerical plots show that the proposed PTP significantly outperforms the continuous transmission policy (CTP). At a specific transmission probability of 0.125, PTP is 40 times more energy efficient than CTP. We extend the work and develop a novel and insightful performance analysis for energy efficiency outage (EEO) probability. Specifically, we derive closed-form approximate expressions for EEO probability and present numerical results. Furthermore, we analyze the performance of clustered CSC networks (CSCNs) and present numerical results to assess EEO probability, providing valuable insights for future large-scale green CSCN design and deployment.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"6 2","pages":"125-132"},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178877","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 : 2024-08-23DOI: 10.1109/JMASS.2024.3449071
Elisa Robert;Mathieu Barthelemy;Thierry Sequies
The satellites for auroral tomography in space (SATIS) project is a mission concept that proposes to perform auroral tomography from space using imagers placed on a constellation of satellites. Auroral tomography is particularly interesting for reconstructing the flux of particles precipitating into the atmosphere. The advantage of space observations is that they avoid cloud cover problems, allowing larger set of data and with a dedicated ground-based infrastructure ensure quasi-continuous monitoring. However, the main difficulty of this mission is to synchronize orbits and attitudes of the satellites in order to observe the same volume of emission at the same time and from different perspectives. The attitude and determination control system will thus have to be very precise and stable. The data volume is also an issue especially in a monitoring point of view. Furthermore, atmospheric drag will have to be correctly considered to limit orbit disturbances and keep satellites synchronized. We present here the preliminary study of this project and the initial requirements identified to be able to perform this mission concept.
{"title":"The Satellites for Auroral Tomography in Space (SATIS) Project: Tomographic Reconstruction of the Auroral Emissions From Space","authors":"Elisa Robert;Mathieu Barthelemy;Thierry Sequies","doi":"10.1109/JMASS.2024.3449071","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3449071","url":null,"abstract":"The satellites for auroral tomography in space (SATIS) project is a mission concept that proposes to perform auroral tomography from space using imagers placed on a constellation of satellites. Auroral tomography is particularly interesting for reconstructing the flux of particles precipitating into the atmosphere. The advantage of space observations is that they avoid cloud cover problems, allowing larger set of data and with a dedicated ground-based infrastructure ensure quasi-continuous monitoring. However, the main difficulty of this mission is to synchronize orbits and attitudes of the satellites in order to observe the same volume of emission at the same time and from different perspectives. The attitude and determination control system will thus have to be very precise and stable. The data volume is also an issue especially in a monitoring point of view. Furthermore, atmospheric drag will have to be correctly considered to limit orbit disturbances and keep satellites synchronized. We present here the preliminary study of this project and the initial requirements identified to be able to perform this mission concept.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"237-245"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679377","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 : 2024-08-23DOI: 10.1109/JMASS.2024.3448433
Heng Zhang;Zhemin Sun;Chaoqun Yang;Xianghui Cao
Mobile edge computing (MEC) revolutionizes data processing by shifting it from the network core to the edge, significantly reducing latency and ensuring Quality of Service. Integrating the agile and flexible unmanned- aerial-vehicle (UAV) technology with MEC offers new opportunities and challenges in decision making for dynamic and complex environments due to the UAVs’ mobility and Line of Sight advantages. Motivated by the potential of UAV-assisted MEC systems with caching mechanisms, this study addresses the optimization problem under uncertain conditions and user demand. To tackle the complex nonconvex sequential decision problem, a deep reinforcement learning framework named delay hybrid action actor-critic is proposed, possessing the capability to handle scenarios requiring both continuous and discrete actions. Comprehensive simulations are conducted to validate the capability of the proposed framework, demonstrating its superiority over traditional methods.
{"title":"Latency Optimization in UAV-Assisted Mobile Edge Computing Empowered by Caching Mechanisms","authors":"Heng Zhang;Zhemin Sun;Chaoqun Yang;Xianghui Cao","doi":"10.1109/JMASS.2024.3448433","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3448433","url":null,"abstract":"Mobile edge computing (MEC) revolutionizes data processing by shifting it from the network core to the edge, significantly reducing latency and ensuring Quality of Service. Integrating the agile and flexible unmanned- aerial-vehicle (UAV) technology with MEC offers new opportunities and challenges in decision making for dynamic and complex environments due to the UAVs’ mobility and Line of Sight advantages. Motivated by the potential of UAV-assisted MEC systems with caching mechanisms, this study addresses the optimization problem under uncertain conditions and user demand. To tackle the complex nonconvex sequential decision problem, a deep reinforcement learning framework named delay hybrid action actor-critic is proposed, possessing the capability to handle scenarios requiring both continuous and discrete actions. Comprehensive simulations are conducted to validate the capability of the proposed framework, demonstrating its superiority over traditional methods.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"228-236"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679335","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 : 2024-08-22DOI: 10.1109/JMASS.2024.3440776
{"title":"The Journal of Miniaturized Air and Space Systems","authors":"","doi":"10.1109/JMASS.2024.3440776","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3440776","url":null,"abstract":"","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 3","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643755","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041461","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}
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