Pub Date : 2024-11-08DOI: 10.1109/OJVT.2024.3494740
Yulin Zhou;Aziz Altaf Khuwaja;Hua Yan
Downlink communications with multiple ground targets using autonomous aerial vehicles (AAVs) as a base station is critical across various fields. However, the presence of potential eavesdroppers introduces significant challenges that must be addressed to ensure the effectiveness and security of these operations. In this work, we investigate the AAV-to-ground communications in the presence of an eavesdropper, focusing on enhancing physical layer security by maximizing the secrecy rate. We propose a Proceed-Hover-Return (PHR) approach, which optimizes the AAV's trajectory, flight time, speed, and hovering duration to achieve the highest possible secrecy rate during communication with ground users. Our numerical results demonstrate the effectiveness of the PHR scheme, which consistently outperforms the fixed-time baseline approach, with particularly significant improvements for users located farther from the eavesdropper. This work provides essential insights into designing secure and efficient AAV communication systems in scenarios involving multiple ground users and potential eavesdroppers.
{"title":"Enhancing AAV-to-Ground Communication Security With the Proceed-Hover-Return (PHR) Approach","authors":"Yulin Zhou;Aziz Altaf Khuwaja;Hua Yan","doi":"10.1109/OJVT.2024.3494740","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3494740","url":null,"abstract":"Downlink communications with multiple ground targets using autonomous aerial vehicles (AAVs) as a base station is critical across various fields. However, the presence of potential eavesdroppers introduces significant challenges that must be addressed to ensure the effectiveness and security of these operations. In this work, we investigate the AAV-to-ground communications in the presence of an eavesdropper, focusing on enhancing physical layer security by maximizing the secrecy rate. We propose a Proceed-Hover-Return (PHR) approach, which optimizes the AAV's trajectory, flight time, speed, and hovering duration to achieve the highest possible secrecy rate during communication with ground users. Our numerical results demonstrate the effectiveness of the PHR scheme, which consistently outperforms the fixed-time baseline approach, with particularly significant improvements for users located farther from the eavesdropper. This work provides essential insights into designing secure and efficient AAV communication systems in scenarios involving multiple ground users and potential eavesdroppers.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1746-1755"},"PeriodicalIF":5.3,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10748360","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713839","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 : 2024-11-07DOI: 10.1109/OJVT.2024.3494040
Aparna P. T. Adithyababu;Stefano Caizzone;Ramon Martinez Rodríguez-Osorio
The integration of non-terrestrial networks (NTN) and terrestrial networks, driven by the developments in 5G, 5G-advanced, and 6G, have resulted in an abundance of new and unique application scenarios for exploiting existing and upcoming satellite networks. With an increasing number of available satellites, there is a growing demand for user terminals to support NTN services, particularly for earth stations on mobile platforms (ESOMP). In order to allow usability of these user terminals on ESOMPs, low cost, small, and power-efficient antennas need to be developed. Moreover, regulatory issues must be taken into account, in order for the new terminals to be capable to interoperate and not interfere with existing systems. This paper investigates the radiation performance of small Ka band satcom antennas as well as their compliance with current European Telecommunications Standards Institute (ETSI) radiation regulations.
{"title":"Analysis of Performance and Radiation Regulation Compliance for a Small Sub-Array Based Ka Band Antenna","authors":"Aparna P. T. Adithyababu;Stefano Caizzone;Ramon Martinez Rodríguez-Osorio","doi":"10.1109/OJVT.2024.3494040","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3494040","url":null,"abstract":"The integration of non-terrestrial networks (NTN) and terrestrial networks, driven by the developments in 5G, 5G-advanced, and 6G, have resulted in an abundance of new and unique application scenarios for exploiting existing and upcoming satellite networks. With an increasing number of available satellites, there is a growing demand for user terminals to support NTN services, particularly for earth stations on mobile platforms (ESOMP). In order to allow usability of these user terminals on ESOMPs, low cost, small, and power-efficient antennas need to be developed. Moreover, regulatory issues must be taken into account, in order for the new terminals to be capable to interoperate and not interfere with existing systems. This paper investigates the radiation performance of small Ka band satcom antennas as well as their compliance with current European Telecommunications Standards Institute (ETSI) radiation regulations.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1756-1765"},"PeriodicalIF":5.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10747243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761390","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 : 2024-10-30DOI: 10.1109/OJVT.2024.3488084
Roopa Tirumalasetti;Sunil Kumar Singh
Intelligent Transport Systems (ITS) rely heavily on Vehicular Ad hoc Networks (VANET) to facilitate effective communication, especially Vehicle-to-Everything (V2X) communication. However, current research has identified challenges in node management, security, and routing within VANET, calling for bespoke solutions to address these issues. This study introduces an innovative cluster-based routing strategy using Enhanced Slap Swarm Optimization (ESSO) and Evaluation with Mixed Data Multi-criteria Decision-Making (EVAmix MCDM) Method tailored to optimize routing in V2X communication. Unlike existing meta-heuristic methods, which often face slow convergence, premature convergence, and local optima stability, the proposed approach demonstrates striking results. Notably, it enhances throughput by 6278 kbps, elevates the Packet Delivery Ratio (PDR) by 95.77 $%$ , and reduces end-to-end delay by 1856ms in the 300th iteration, outperforming existing cluster routing methodologies. Our findings suggest a substantial leap toward surmounting the existing challenges in V2X communication. This innovative solution advances the field and sets a course for real-time applications. This approach allows vehicles to continually monitor, adjust their position, and control their speed on highways, enhancing safety and traffic control.
智能交通系统（ITS）在很大程度上依赖于车载 Ad hoc 网络（VANET）来促进有效通信，尤其是车对物（V2X）通信。然而，目前的研究发现，VANET 在节点管理、安全性和路由选择方面存在挑战，因此需要定制解决方案来解决这些问题。本研究介绍了一种基于集群的创新路由策略，该策略采用了增强型蜻蜓群优化（ESSO）和混合数据多标准决策评估（EVAmix MCDM）方法，专门用于优化 V2X 通信中的路由。现有的元启发式方法通常面临收敛速度慢、过早收敛和局部最优稳定性等问题，与之不同的是，所提出的方法取得了令人瞩目的成果。值得注意的是，在第 300 次迭代中，它将吞吐量提高了 6278 kbps，将数据包交付率（PDR）提高了 95.77$/%$，并将端到端延迟减少了 1856ms，表现优于现有的集群路由方法。我们的研究结果表明，在克服 V2X 通信的现有挑战方面，我们取得了实质性的飞跃。这一创新解决方案推动了该领域的发展，并为实时应用指明了方向。这种方法允许车辆在高速公路上持续监控、调整位置并控制速度，从而提高安全性并加强交通控制。
{"title":"OptiFlow: Optimizing Traffic Flow in ITS With Improved Cluster Routing","authors":"Roopa Tirumalasetti;Sunil Kumar Singh","doi":"10.1109/OJVT.2024.3488084","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3488084","url":null,"abstract":"Intelligent Transport Systems (ITS) rely heavily on Vehicular Ad hoc Networks (VANET) to facilitate effective communication, especially Vehicle-to-Everything (V2X) communication. However, current research has identified challenges in node management, security, and routing within VANET, calling for bespoke solutions to address these issues. This study introduces an innovative cluster-based routing strategy using Enhanced Slap Swarm Optimization (ESSO) and Evaluation with Mixed Data Multi-criteria Decision-Making (EVAmix MCDM) Method tailored to optimize routing in V2X communication. Unlike existing meta-heuristic methods, which often face slow convergence, premature convergence, and local optima stability, the proposed approach demonstrates striking results. Notably, it enhances throughput by 6278 kbps, elevates the Packet Delivery Ratio (PDR) by 95.77\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000, and reduces end-to-end delay by 1856ms in the 300th iteration, outperforming existing cluster routing methodologies. Our findings suggest a substantial leap toward surmounting the existing challenges in V2X communication. This innovative solution advances the field and sets a course for real-time applications. This approach allows vehicles to continually monitor, adjust their position, and control their speed on highways, enhancing safety and traffic control.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1727-1745"},"PeriodicalIF":5.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713927","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 : 2024-10-28DOI: 10.1109/OJVT.2024.3487245
Surya Samantha Beri;Nitul Dutta
Vehicular networks (V-Nets) of the current era is widely used for information sharing and entertainment. Considering the popularity and demand for faster data communications, V-Nets are evolving towards an information-centric approach, namely the Information Centric Vehicular Networks (ICVN). In ICVN, nodes such as vehicles, roadside units, pedestrian devices, and sensors, generate interest packets and forward them to appropriate cache stores to retrieve desired data. Hence, an efficient interest forwarding algorithm in ICVN considerably enhances the content retrieval performance and make supports road safety, traffic management, environmental monitoring in real-vehicular environment. However, designing such efficient scheme is critical due to the involvement of various communication scenarios like vehicle-to-vehicle (V2V), vehicle-to-roadside unit (V2R), vehicle-to-in-vehicle sensors (V2S), vehicle-to-mobile infrastructure (V2I), and vehicle-to-personal device (V2P) and it demands a comprehensive and connected vehicular network. In this paper, we propose an interest forwarding scheme for ICVN with the above said realistic scenario. The interest forwarding process is governed by the Secretary Selection Problem (SSP) which guides a vehicular node to select the next level forwarder for locating the desired content. The goal is to select the best neighboring vehicle as the next forwarder thereby maximizing the chances of obtaining the requested data. The proposed scheme, named as Selection Based Interest Forwarding (SBIF) which uses Secretary Selection Problem (SSP) for governing the forward process to select the next level forwarder for locating the desired content. This scheme is analyzed mathematically and implemented in $ndn$ simulator. Performance is compared with existing state of the art schemes and observation shows that SBIF outperforms existing approaches considered for comparison.
{"title":"Efficient Modeling of Interest Forwarding in Information Centric Vehicular Networks","authors":"Surya Samantha Beri;Nitul Dutta","doi":"10.1109/OJVT.2024.3487245","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3487245","url":null,"abstract":"Vehicular networks (V-Nets) of the current era is widely used for information sharing and entertainment. Considering the popularity and demand for faster data communications, V-Nets are evolving towards an information-centric approach, namely the Information Centric Vehicular Networks (ICVN). In ICVN, nodes such as vehicles, roadside units, pedestrian devices, and sensors, generate interest packets and forward them to appropriate cache stores to retrieve desired data. Hence, an efficient interest forwarding algorithm in ICVN considerably enhances the content retrieval performance and make supports road safety, traffic management, environmental monitoring in real-vehicular environment. However, designing such efficient scheme is critical due to the involvement of various communication scenarios like vehicle-to-vehicle (V2V), vehicle-to-roadside unit (V2R), vehicle-to-in-vehicle sensors (V2S), vehicle-to-mobile infrastructure (V2I), and vehicle-to-personal device (V2P) and it demands a comprehensive and connected vehicular network. In this paper, we propose an interest forwarding scheme for ICVN with the above said realistic scenario. The interest forwarding process is governed by the Secretary Selection Problem (SSP) which guides a vehicular node to select the next level forwarder for locating the desired content. The goal is to select the best neighboring vehicle as the next forwarder thereby maximizing the chances of obtaining the requested data. The proposed scheme, named as Selection Based Interest Forwarding (SBIF) which uses Secretary Selection Problem (SSP) for governing the forward process to select the next level forwarder for locating the desired content. This scheme is analyzed mathematically and implemented in \u0000<inline-formula><tex-math>$ndn$</tex-math></inline-formula>\u0000 simulator. Performance is compared with existing state of the art schemes and observation shows that SBIF outperforms existing approaches considered for comparison.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1680-1691"},"PeriodicalIF":5.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636570","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}
Unmanned aerial vehicles (UAVs) serve as airborne access points or base stations, delivering network services to the Internet of Things devices (IoTDs) in areas with compromised or absent infrastructure. However, urban obstacles like trees and high buildings can obstruct the connection between UAVs and IoTDs, leading to degraded communication performance. High altitudes can also result in significant path losses. To address these challenges, this paper introduces the deployment of reconfigurable intelligent surfaces (RISs) that smartly reflect signals to improve communication quality. It proposes a method to jointly optimize the 3D trajectory of the UAV and the phase shifts of the RIS to maximize communication coverage and ensure satisfactory average achievable data rates for RIS-assisted UAV-enabled wireless communications by considering mobile multi-user scenarios. In this paper, a multi-agent double-deep Q -network (MADDQN) algorithm is presented, which each agent dynamically adjusts either the positioning of the UAV or the phase shifts of the RIS. Agents learn to collaborate with each other by sharing the same reward to achieve a common goal. In the simulation, results demonstrate that the proposed method significantly outperforms baseline strategies in terms of improving communication coverage and average achievable data rates. The proposed method achieves 98.6% of a communication coverage score, while IoTDs are guaranteed to have acceptable achievable data rates.
{"title":"Multi-Agent Deep Reinforcement Learning Based Optimizing Joint 3D Trajectories and Phase Shifts in RIS-Assisted UAV-Enabled Wireless Communications","authors":"Belayneh Abebe Tesfaw;Rong-Terng Juang;Hsin-Piao Lin;Getaneh Berie Tarekegn;Wendenda Nathanael Kabore","doi":"10.1109/OJVT.2024.3486197","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3486197","url":null,"abstract":"Unmanned aerial vehicles (UAVs) serve as airborne access points or base stations, delivering network services to the Internet of Things devices (IoTDs) in areas with compromised or absent infrastructure. However, urban obstacles like trees and high buildings can obstruct the connection between UAVs and IoTDs, leading to degraded communication performance. High altitudes can also result in significant path losses. To address these challenges, this paper introduces the deployment of reconfigurable intelligent surfaces (RISs) that smartly reflect signals to improve communication quality. It proposes a method to jointly optimize the 3D trajectory of the UAV and the phase shifts of the RIS to maximize communication coverage and ensure satisfactory average achievable data rates for RIS-assisted UAV-enabled wireless communications by considering mobile multi-user scenarios. In this paper, a multi-agent double-deep \u0000<italic>Q</i>\u0000-network (MADDQN) algorithm is presented, which each agent dynamically adjusts either the positioning of the UAV or the phase shifts of the RIS. Agents learn to collaborate with each other by sharing the same reward to achieve a common goal. In the simulation, results demonstrate that the proposed method significantly outperforms baseline strategies in terms of improving communication coverage and average achievable data rates. The proposed method achieves 98.6% of a communication coverage score, while IoTDs are guaranteed to have acceptable achievable data rates.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1712-1726"},"PeriodicalIF":5.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10734161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679359","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}
Evolving transportation networks need seamless integration and effective infrastructure utilisation to form the next-generation transportation networks. Also, they should be capable of capturing the traffic flow data at the right time and promptly applying sustainable actions toward emission reduction. However, traditional transportation networks cannot handle right-time updates and act upon the requirements in dynamic conditions. Here, Digital Twin (DT) enables the development of enhanced transportation management via robust modelling and intelligence capabilities. Therefore, we propose a DT-empowered Eco-Regulation (DTER) framework with a novel twinning approach. We define a transport-specific twin sampling rate to catch right-time data in a transportation network. Besides, we perform emission prediction using Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory (Bi-LSTM), and BANE embeddings. We perform Laplacian matrix analysis to cluster the risk zones regarding the emissions. Thereafter, we recommend actions by setting the number of vehicle limits of junctions for high-emission areas according to the outputs of Q-learning. In summary, DTER takes control of the emission with its transport-specific twin sampling rate and automated management of transportation actions by considering the emission predictions. We note DTER achieves 19% more successful right-time data capturing, with 30% reduced query time. Moreover, our hybrid implementation of intelligent algorithms for emission prediction resulted in higher accuracy when compared to baselines. Lastly, the autonomous recommendations of DTER achieved $sim$ 20% decrease in emissions by presenting an effective carbon tracing framework.
{"title":"Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks","authors":"Kubra Duran;Lal Verda Cakir;Achille Fonzone;Trung Q. Duong;Berk Canberk","doi":"10.1109/OJVT.2024.3484956","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3484956","url":null,"abstract":"Evolving transportation networks need seamless integration and effective infrastructure utilisation to form the next-generation transportation networks. Also, they should be capable of capturing the traffic flow data at the right time and promptly applying sustainable actions toward emission reduction. However, traditional transportation networks cannot handle right-time updates and act upon the requirements in dynamic conditions. Here, Digital Twin (DT) enables the development of enhanced transportation management via robust modelling and intelligence capabilities. Therefore, we propose a DT-empowered Eco-Regulation (DTER) framework with a novel twinning approach. We define a transport-specific twin sampling rate to catch right-time data in a transportation network. Besides, we perform emission prediction using Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory (Bi-LSTM), and BANE embeddings. We perform Laplacian matrix analysis to cluster the risk zones regarding the emissions. Thereafter, we recommend actions by setting the number of vehicle limits of junctions for high-emission areas according to the outputs of Q-learning. In summary, DTER takes control of the emission with its transport-specific twin sampling rate and automated management of transportation actions by considering the emission predictions. We note DTER achieves 19% more successful right-time data capturing, with 30% reduced query time. Moreover, our hybrid implementation of intelligent algorithms for emission prediction resulted in higher accuracy when compared to baselines. Lastly, the autonomous recommendations of DTER achieved \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u0000 20% decrease in emissions by presenting an effective carbon tracing framework.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1650-1662"},"PeriodicalIF":5.3,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10726797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595789","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 : 2024-10-21DOI: 10.1109/OJVT.2024.3477268
Nishant Gupta;Satyam Agarwal;Aymen Fakhreddine
Integrating unmanned aerial vehicles (UAVs) with terrestrial networks can enable high-speed communication in various applications. UAVs can serve as aerial base stations (ABSs), offering several benefits to the existing terrestrial networks, such as enhanced coverage, increased capacity, rapid deployment, and mobile communication support. However, this integration presents various technical challenges, including coordination, interference management, and dynamic allocation of resources. To address these key challenges, in this paper, we maximize the network utility by jointly optimizing the scheduling and cell association, transmit power of all base stations, and ABS deployment locations in the presence of co-channel interference. A two-stage approach is proposed to obtain a solution. In the first stage, we propose a heuristic solution by using the clustering algorithm to determine the initial ABS locations and user scheduling while ignoring the co-channel interference. In the second stage, we utilize the solution obtained in the first part and develop an interference-aware iterative scheme to jointly optimize user scheduling, resource allocation, and ABS placement. Given the non-convex nature of this problem, we employ the successive convex approximation technique to approximate the non-convex objectives and constraints. Numerical results show the proposed approach's insights and effectiveness over other schemes. Specifically, our proposed approach provides an average of 25% improvement over the benchmark schemes.
{"title":"Fairness-Aware Utility Maximization for Multi-UAV-Aided Terrestrial Networks","authors":"Nishant Gupta;Satyam Agarwal;Aymen Fakhreddine","doi":"10.1109/OJVT.2024.3477268","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3477268","url":null,"abstract":"Integrating unmanned aerial vehicles (UAVs) with terrestrial networks can enable high-speed communication in various applications. UAVs can serve as aerial base stations (ABSs), offering several benefits to the existing terrestrial networks, such as enhanced coverage, increased capacity, rapid deployment, and mobile communication support. However, this integration presents various technical challenges, including coordination, interference management, and dynamic allocation of resources. To address these key challenges, in this paper, we maximize the network utility by jointly optimizing the scheduling and cell association, transmit power of all base stations, and ABS deployment locations in the presence of co-channel interference. A two-stage approach is proposed to obtain a solution. In the first stage, we propose a heuristic solution by using the clustering algorithm to determine the initial ABS locations and user scheduling while ignoring the co-channel interference. In the second stage, we utilize the solution obtained in the first part and develop an interference-aware iterative scheme to jointly optimize user scheduling, resource allocation, and ABS placement. Given the non-convex nature of this problem, we employ the successive convex approximation technique to approximate the non-convex objectives and constraints. Numerical results show the proposed approach's insights and effectiveness over other schemes. Specifically, our proposed approach provides an average of 25% improvement over the benchmark schemes.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1611-1624"},"PeriodicalIF":5.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10726751","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587652","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 : 2024-10-16DOI: 10.1109/OJVT.2024.3481884
Kai Lennert Bober;Anselm Ebmeyer;Falko Dressler;Ronald Freund;Volker Jungnickel
As industrial communication continues to evolve to increase flexibility through wireless communication, networked optical wireless communication (OWC), also known as LiFi, has emerged as a promising candidate technology due to its unlicensed spectrum and relatively deterministic propagation. The inherent containment of light improves security, enables dense cellular networks with spatial reuse, and results in reduced sporadic interference while providing high-capacity short range communication links to mobile end devices. This paper outlines the features of the new IEEE Std 802.15.13-2013, suitable for industrial OWC, and presents details of our prototype implementation along with initial experiments. The standard specifies deterministic medium access control (MAC), based on dynamic time division multiple access (TDMA), as well as two physical layers (PHYs) for extended range and robustness, and for spectral efficiency, respectively. Our prototype includes a central coordinator, implemented entirely in software, running on commodity server hardware. It connects to distributed ceiling-mounted optical wireless frontends via a packet-switched network (Ethernet) and is capable of forming them into adaptive virtual cells on a per-user basis. This approach enhances reliability through multiple-input multiple-output (MIMO) transmission and allows for smooth mobility. We implemented the Pulsed Modulation PHY (PM-PHY) on a commercially available field programmable gate array (FPGA) evaluation board. Initial test results indicate that the PM-PHY supports typical distances of up to 6 m between the ceiling and the mobile device. The MAC achieves deterministic latency values below 4 ms.
{"title":"LiFi for Industry 4.0: Main Features, Implementation and Initial Testing of IEEE Std 802.15.13","authors":"Kai Lennert Bober;Anselm Ebmeyer;Falko Dressler;Ronald Freund;Volker Jungnickel","doi":"10.1109/OJVT.2024.3481884","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3481884","url":null,"abstract":"As industrial communication continues to evolve to increase flexibility through wireless communication, networked optical wireless communication (OWC), also known as LiFi, has emerged as a promising candidate technology due to its unlicensed spectrum and relatively deterministic propagation. The inherent containment of light improves security, enables dense cellular networks with spatial reuse, and results in reduced sporadic interference while providing high-capacity short range communication links to mobile end devices. This paper outlines the features of the new IEEE Std 802.15.13-2013, suitable for industrial OWC, and presents details of our prototype implementation along with initial experiments. The standard specifies deterministic medium access control (MAC), based on dynamic time division multiple access (TDMA), as well as two physical layers (PHYs) for extended range and robustness, and for spectral efficiency, respectively. Our prototype includes a central coordinator, implemented entirely in software, running on commodity server hardware. It connects to distributed ceiling-mounted optical wireless frontends via a packet-switched network (Ethernet) and is capable of forming them into adaptive virtual cells on a per-user basis. This approach enhances reliability through multiple-input multiple-output (MIMO) transmission and allows for smooth mobility. We implemented the Pulsed Modulation PHY (PM-PHY) on a commercially available field programmable gate array (FPGA) evaluation board. Initial test results indicate that the PM-PHY supports typical distances of up to 6 m between the ceiling and the mobile device. The MAC achieves deterministic latency values below 4 ms.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1625-1636"},"PeriodicalIF":5.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579107","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}
One of the major challenges in multiple input multiple output (MIMO) system design is the salient trade-off between performance and computational complexity. For instance, the maximum likelihood (Max-L) detection is capable of achieving optimal performance based on exhaustive search, but its exponential computational complexity renders it impractical. By contrast, zero-forcing detection has low computational complexity, while having significantly worse performance compared to that of the Max-L. The recent developments in deep learning (DL) based detection techniques relying on back propagation neural networks (BPNN) constitute promising candidates for the open challenge of the MIMO detection performance versus complexity trade-off. Against this background, in this paper, we propose a novel partial learning (PL) model for MIMO detection with soft-bit decisions that can be incorporated into channel-coded communication systems. More explicitly, the proposed PL model consists of two parts: first, a subset of the transmitted MIMO symbols is detected by the data-driven DL technique and then the detected symbols are removed from the received MIMO signals for the sake of interference cancellation. Afterwards, the classic model-based zero-forcing detector is invoked to detect the remaining symbols at a linear complexity. As a result, near-optimal MIMO performance can be achieved with substantially reduced computational complexity compared to Max-L and BPNN. The proposed solution is adapted to both accept and produce soft information, so that iterative detection can be performed, where the iteration gain is analyzed by extrinsic information transfer (EXIT) charts. Our simulation results demonstrate that the proposed partial learning-based iterative detection is capable of attaining near-Max-L performance while attaining a flexible performance versus complexity trade-off.
多输入多输出（MIMO）系统设计的主要挑战之一是性能与计算复杂度之间的突出权衡。例如，最大似然（Max-L）检测能够在穷举搜索的基础上实现最佳性能，但其指数级的计算复杂度使其不切实际。相比之下，零强迫检测的计算复杂度较低，但性能却比 Max-L 差很多。基于反向传播神经网络（BPNN）的深度学习（DL）检测技术的最新发展，为解决 MIMO 检测性能与复杂性权衡这一难题带来了希望。在此背景下，我们在本文中提出了一种新颖的部分学习（PL）模型，用于可纳入信道编码通信系统的具有软位决策的 MIMO 检测。更明确地说，所提出的 PL 模型由两部分组成：首先，通过数据驱动的 DL 技术检测传输的 MIMO 符号子集，然后为了消除干扰，从接收的 MIMO 信号中去除检测到的符号。之后，再使用基于模型的经典零干扰检测器，以线性复杂度检测剩余的符号。因此，与 Max-L 和 BPNN 相比，在大幅降低计算复杂度的同时，还能实现近乎最佳的 MIMO 性能。所提出的解决方案既能接受软信息，也能产生软信息，因此可以进行迭代检测，迭代增益通过外在信息传输（EXIT）图进行分析。我们的仿真结果表明，所提出的基于部分学习的迭代检测能够达到接近 Max-L 的性能，同时实现灵活的性能与复杂性权衡。
{"title":"Partial Learning-Based Iterative Detection of MIMO Systems","authors":"Abdulaziz Babulghum;Chao Xu;Soon Xin Ng;Mohammed El-Hajjar","doi":"10.1109/OJVT.2024.3482008","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3482008","url":null,"abstract":"One of the major challenges in multiple input multiple output (MIMO) system design is the salient trade-off between performance and computational complexity. For instance, the maximum likelihood (Max-L) detection is capable of achieving optimal performance based on exhaustive search, but its exponential computational complexity renders it impractical. By contrast, zero-forcing detection has low computational complexity, while having significantly worse performance compared to that of the Max-L. The recent developments in deep learning (DL) based detection techniques relying on back propagation neural networks (BPNN) constitute promising candidates for the open challenge of the MIMO detection performance versus complexity trade-off. Against this background, in this paper, we propose a novel partial learning (PL) model for MIMO detection with soft-bit decisions that can be incorporated into channel-coded communication systems. More explicitly, the proposed PL model consists of two parts: first, a subset of the transmitted MIMO symbols is detected by the data-driven DL technique and then the detected symbols are removed from the received MIMO signals for the sake of interference cancellation. Afterwards, the classic model-based zero-forcing detector is invoked to detect the remaining symbols at a linear complexity. As a result, near-optimal MIMO performance can be achieved with substantially reduced computational complexity compared to Max-L and BPNN. The proposed solution is adapted to both accept and produce soft information, so that iterative detection can be performed, where the iteration gain is analyzed by extrinsic information transfer (EXIT) charts. Our simulation results demonstrate that the proposed partial learning-based iterative detection is capable of attaining near-Max-L performance while attaining a flexible performance versus complexity trade-off.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1600-1610"},"PeriodicalIF":5.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579186","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 : 2024-10-14DOI: 10.1109/OJVT.2024.3479927
Yakov Miron;Dan Navon;Yuval Goldfracht;Dotan Di Castro;Itzik Klein
Multi-agent collaboration involves multiple participants working together in a shared environment to achieve a common goal. These agents share information, divide tasks, and synchronize their actions. Key aspects of multi-agent collaboration include coordination, communication, task allocation, cooperation, adaptation, and decentralization. On construction sites, surface grading is the process of leveling sand piles to increase a specific area's height. There, a bulldozer grades while a dumper allocates sand piles. Our work aims to utilize a multi-agent approach to enable these vehicles to collaborate effectively. To this end, we propose a decentralized and asymmetric multi-agent learning approach for construction sites (DAMALCS). We formulate DAMALCS to reduce expected collisions for operating vehicles. Therefore, we develop two heuristic experts capable of achieving their joint goal optimally, by applying an innovative prioritization method. In this approach, the bulldozer's movements take precedence over the dumper's operations. This enables the dozer to clear the path for the dumper and ensure continuous operation of both vehicles. As heuristics alone are insufficient in real-world scenarios, we utilize them to train AI agents, which proves to be highly effective. We simultaneously train dozer and dumper agents to operate within the same environment, aiming to avoid collisions and optimizing performance in terms of time efficiency and sand volume handling. Our trained agents and heuristics are evaluated in both simulation and real-world lab experiments, testing them under various conditions such as visual noise and localization errors. The results demonstrate that our approach significantly reduces collision rates for these vehicles.
{"title":"Decentralized and Asymmetric Multi-Agent Learning in Construction Sites","authors":"Yakov Miron;Dan Navon;Yuval Goldfracht;Dotan Di Castro;Itzik Klein","doi":"10.1109/OJVT.2024.3479927","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3479927","url":null,"abstract":"Multi-agent collaboration involves multiple participants working together in a shared environment to achieve a common goal. These agents share information, divide tasks, and synchronize their actions. Key aspects of multi-agent collaboration include coordination, communication, task allocation, cooperation, adaptation, and decentralization. On construction sites, surface grading is the process of leveling sand piles to increase a specific area's height. There, a bulldozer grades while a dumper allocates sand piles. Our work aims to utilize a multi-agent approach to enable these vehicles to collaborate effectively. To this end, we propose a decentralized and asymmetric multi-agent learning approach for construction sites (DAMALCS). We formulate DAMALCS to reduce expected collisions for operating vehicles. Therefore, we develop two heuristic experts capable of achieving their joint goal optimally, by applying an innovative prioritization method. In this approach, the bulldozer's movements take precedence over the dumper's operations. This enables the dozer to clear the path for the dumper and ensure continuous operation of both vehicles. As heuristics alone are insufficient in real-world scenarios, we utilize them to train AI agents, which proves to be highly effective. We simultaneously train dozer and dumper agents to operate within the same environment, aiming to avoid collisions and optimizing performance in terms of time efficiency and sand volume handling. Our trained agents and heuristics are evaluated in both simulation and real-world lab experiments, testing them under various conditions such as visual noise and localization errors. The results demonstrate that our approach significantly reduces collision rates for these vehicles.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1587-1599"},"PeriodicalIF":5.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10715664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579185","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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