Pub Date : 2025-07-31DOI: 10.1109/OJVT.2025.3593944
Si-Yu Zhang;Jia-Qi Zhang;Xin-Wei Yue;Chao-Wei Wang
Short to medium length polar codes achieve inferior decoding performance than other advanced channel codes under successive cancellation (SC). Sophisticated polar decoding enhances the corresponding performance while degrading the coding rate and complexity. For better decoding performance and efficiency, this paper presents a polar coding scheme with selected index modulation (PC-SIM). At the transmitter, PC-SIM integrates the concept of index modulation (IM) into polar encoding, using the indices of inactive unfrozen positions (IUPs) to carry implicit information. To boost coding rate without sacrificing decoding performance, PC-SIM selects more reliable unfrozen positions for IM and adds inactive information bits (IIBs) to offset rate losses. Additionally, Walsh-Hadamard Transform (WHT) is incorporated to lower the high peak-to-average power ratio (PAPR) in multi-carrier systems and reduce interference. At the receiver, PC-SIM performs polar decoding followed by repetition decoding to obtain index bits and information bits. Simulation results indicate that in Orthogonal Frequency Division Multiplexing (OFDM) systems, compared to conventional polar codes and existing IM-aided polar coding schemes, the proposed PC-SIM scheme significantly improves error performance, coding rate, and PAPR reduction. The proposed PC-SIM achieves around 0.3 dB over the conventional CRC-aided polar codes and IM-aided polar codes with higher coding rate at the bit error ratio (BER) of $4times 10^{-4}$.
{"title":"A Polar Coding Scheme With Selected Index Modulation","authors":"Si-Yu Zhang;Jia-Qi Zhang;Xin-Wei Yue;Chao-Wei Wang","doi":"10.1109/OJVT.2025.3593944","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3593944","url":null,"abstract":"Short to medium length polar codes achieve inferior decoding performance than other advanced channel codes under successive cancellation (SC). Sophisticated polar decoding enhances the corresponding performance while degrading the coding rate and complexity. For better decoding performance and efficiency, this paper presents a polar coding scheme with selected index modulation (PC-SIM). At the transmitter, PC-SIM integrates the concept of index modulation (IM) into polar encoding, using the indices of inactive unfrozen positions (IUPs) to carry implicit information. To boost coding rate without sacrificing decoding performance, PC-SIM selects more reliable unfrozen positions for IM and adds inactive information bits (IIBs) to offset rate losses. Additionally, Walsh-Hadamard Transform (WHT) is incorporated to lower the high peak-to-average power ratio (PAPR) in multi-carrier systems and reduce interference. At the receiver, PC-SIM performs polar decoding followed by repetition decoding to obtain index bits and information bits. Simulation results indicate that in Orthogonal Frequency Division Multiplexing (OFDM) systems, compared to conventional polar codes and existing IM-aided polar coding schemes, the proposed PC-SIM scheme significantly improves error performance, coding rate, and PAPR reduction. The proposed PC-SIM achieves around 0.3 dB over the conventional CRC-aided polar codes and IM-aided polar codes with higher coding rate at the bit error ratio (BER) of <inline-formula><tex-math>$4times 10^{-4}$</tex-math></inline-formula>.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2143-2154"},"PeriodicalIF":4.8,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11105420","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842977","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}
With the rapid advancement of Vehicle-to-Everything (V2X) technology, there is a growing demand for collaborative perception among vehicles and multimodal devices (e.g., roadside units, pedestrian terminals). However, traditional centralized learning and federated learning (FL) face challenges in model convergence and performance degradation due to non-IID data distribution, privacy protection requirements, and communication bandwidth constraints among massive heterogeneous devices in V2X scenarios. To address these issues, this paper proposes a heterogeneous federated learning framework based on feature prototype alignment and generative knowledge transfer, enabling efficient and secure cross-device collaborative learning. The framework employs dynamic edge-enhanced contrastive learning on the server side to generate trainable global feature prototypes. These prototypes are subsequently decoded into composite images through a pre-trained generative adversarial network, achieving lightweight privacy-preserving knowledge transfer. Experimental results on CIFAR-10, CIFAR-100, and BelgiumTSC datasets demonstrate that our method achieves significant accuracy improvements compared with baseline approaches such as FedDistill and FedTGP. This study establishes a novel theoretical framework and technical pathway for collaborative learning in V2X environments that effectively balances privacy protection with model performance.
{"title":"Heterogeneous Federated Learning for Vehicle-to-Everything: Feature Prototype Aggregation and Generative Feedback Mechanism","authors":"Xianhui Liu;Jianle Liu;Yingyao Zhang;Ning Jia;Chenlin Zhu","doi":"10.1109/OJVT.2025.3594030","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3594030","url":null,"abstract":"With the rapid advancement of Vehicle-to-Everything (V2X) technology, there is a growing demand for collaborative perception among vehicles and multimodal devices (e.g., roadside units, pedestrian terminals). However, traditional centralized learning and federated learning (FL) face challenges in model convergence and performance degradation due to non-IID data distribution, privacy protection requirements, and communication bandwidth constraints among massive heterogeneous devices in V2X scenarios. To address these issues, this paper proposes a heterogeneous federated learning framework based on feature prototype alignment and generative knowledge transfer, enabling efficient and secure cross-device collaborative learning. The framework employs dynamic edge-enhanced contrastive learning on the server side to generate trainable global feature prototypes. These prototypes are subsequently decoded into composite images through a pre-trained generative adversarial network, achieving lightweight privacy-preserving knowledge transfer. Experimental results on CIFAR-10, CIFAR-100, and BelgiumTSC datasets demonstrate that our method achieves significant accuracy improvements compared with baseline approaches such as FedDistill and FedTGP. This study establishes a novel theoretical framework and technical pathway for collaborative learning in V2X environments that effectively balances privacy protection with model performance.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2332-2342"},"PeriodicalIF":4.8,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11103507","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011313","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}
In the face of dynamic electromagnetic environments, unmanned aerial vehicle (UAV) swarm-based sensing technologies have gained considerable attention due to their superior mobility, adaptable coverage, and reliable line-of-sight (LoS) connectivity.These advantages make UAVs well-suited for a wide range of sensing applications. However, optimizing UAV deployment to enhance sensing accuracy presents a considerable challenge for multi-UAV systems, particularly when dealing with complex target environments. This paper investigates a cooperative sensing problem within a multi-UAV framework, where multiple UAVs collaboratively perform energy detection for a set of ground targets (GTs). To evaluate the system's sensing accuracy, we use energy detection probability as the performance metric, with the objective of maximizing the network's overall detection probability through optimized UAV placement. Given the non-convex nature of the problem, we develop an efficient, low-complexity algorithm based on Gibbs Sampling (GS) to iteratively optimize UAV positions. Extensive simulation results validate the effectiveness of the proposed algorithm, demonstrating its robustness in various scenarios and providing practical insights for the design of real-world multi-UAV sensing systems.
{"title":"Efficient Deployment Optimization Design for Multi-UAV Cooperative Sensing System","authors":"Lifeng Chen;Zhiqiang Zhang;Lingyun Zhou;Zichen Wang;Shuo Zhao;Jiangwei Ding;Hong Guo;Fei Xing","doi":"10.1109/OJVT.2025.3594076","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3594076","url":null,"abstract":"In the face of dynamic electromagnetic environments, unmanned aerial vehicle (UAV) swarm-based sensing technologies have gained considerable attention due to their superior mobility, adaptable coverage, and reliable line-of-sight (LoS) connectivity.These advantages make UAVs well-suited for a wide range of sensing applications. However, optimizing UAV deployment to enhance sensing accuracy presents a considerable challenge for multi-UAV systems, particularly when dealing with complex target environments. This paper investigates a cooperative sensing problem within a multi-UAV framework, where multiple UAVs collaboratively perform energy detection for a set of ground targets (GTs). To evaluate the system's sensing accuracy, we use energy detection probability as the performance metric, with the objective of maximizing the network's overall detection probability through optimized UAV placement. Given the non-convex nature of the problem, we develop an efficient, low-complexity algorithm based on Gibbs Sampling (GS) to iteratively optimize UAV positions. Extensive simulation results validate the effectiveness of the proposed algorithm, demonstrating its robustness in various scenarios and providing practical insights for the design of real-world multi-UAV sensing systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2305-2316"},"PeriodicalIF":4.8,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11103739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011311","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}
Modern and autonomous hybrid electric vehicles (HEVs), as complex cyber-physical systems, represent a key innovation in the future of transportation. However, the increasing interconnectivity and reliance on digital components expose these vehicles to significant cybersecurity risks. To address these challenges, Zero Trust Architecture (ZTA) has emerged as a promising security framework. Operating on the principle of ‘never trust, always verify,’ ZTA offers a comprehensive approach to ensuring continuous trust verification in HEV systems. Despite its potential, the application of ZTA within cyber-physical vehicular systems remains underexplored, and its practical benefits and limitations are not yet fully understood by the engineering community. To bridge this gap, this article presents a detailed survey of ZTA tailored specifically to the needs of vehicular CPSs, highlighting existing technologies, security challenges, and the application of zero-trust principles in HEVs. Additionally, this work proposes a deep learning-based replay attack detection scheme for the battery management system (BMS) of HEVs. The approach leverages a deep learning model to estimate the battery's State of Charge (SoC), analyzing the Error of Estimation using the Inter-Quartile Range (IQR) technique. The detection system analyzes the Error of Estimation using the IQR technique, demonstrating a 74.25% containment ratio and detecting deviations up to 2.39 units during attack scenarios. The system maintains a balanced detection sensitivity with 25.75% detection density. While the proposed method demonstrates high effectiveness in detecting stealth replay attacks through simulation results, it faces certain limitations including computational overhead for real-time processing, dependence on high-quality training data, and potential vulnerability to adversarial attacks on the underlying deep learning model. These challenges highlight the need for careful consideration in practical implementations while opening avenues for future research.
{"title":"Zero Trust Architecture for Electric Transportation Systems: A Systematic Survey and Deep Learning Framework for Replay Attack Detection","authors":"Grace Muriithi;Behnaz Papari;Ali Arsalan;Laxman Timilsina;Alex Muriithi;Elutunji Buraimoh;Asif Khan;Gokhan Ozkan;Christopher Edrington;Akram Papari","doi":"10.1109/OJVT.2025.3592041","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3592041","url":null,"abstract":"Modern and autonomous hybrid electric vehicles (HEVs), as complex cyber-physical systems, represent a key innovation in the future of transportation. However, the increasing interconnectivity and reliance on digital components expose these vehicles to significant cybersecurity risks. To address these challenges, Zero Trust Architecture (ZTA) has emerged as a promising security framework. Operating on the principle of ‘never trust, always verify,’ ZTA offers a comprehensive approach to ensuring continuous trust verification in HEV systems. Despite its potential, the application of ZTA within cyber-physical vehicular systems remains underexplored, and its practical benefits and limitations are not yet fully understood by the engineering community. To bridge this gap, this article presents a detailed survey of ZTA tailored specifically to the needs of vehicular CPSs, highlighting existing technologies, security challenges, and the application of zero-trust principles in HEVs. Additionally, this work proposes a deep learning-based replay attack detection scheme for the battery management system (BMS) of HEVs. The approach leverages a deep learning model to estimate the battery's State of Charge (SoC), analyzing the Error of Estimation using the Inter-Quartile Range (IQR) technique. The detection system analyzes the Error of Estimation using the IQR technique, demonstrating a 74.25% containment ratio and detecting deviations up to 2.39 units during attack scenarios. The system maintains a balanced detection sensitivity with 25.75% detection density. While the proposed method demonstrates high effectiveness in detecting stealth replay attacks through simulation results, it faces certain limitations including computational overhead for real-time processing, dependence on high-quality training data, and potential vulnerability to adversarial attacks on the underlying deep learning model. These challenges highlight the need for careful consideration in practical implementations while opening avenues for future research.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2171-2194"},"PeriodicalIF":4.8,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11091432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853051","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 : 2025-07-23DOI: 10.1109/OJVT.2025.3591872
Sofia Borgosano;Michela Longo
The use of vehicles for operational and logistical purposes remains a cornerstone of modern business activities. With the growing push toward sustainability and the decarbonization of transport, the transition from internal combustion engine vehicles to Electric Vehicles (EVs) presents both significant opportunities and complex challenges for corporate fleets. This study investigates the electrification of company-owned fleets through a comprehensive survey of 20 Italian companies in the mobility and logistics sectors, combined with an optimization model applied to simulated fleets of 10, 25, and 40 vehicles, representing Small, Medium, and Big Enterprises. The survey captures real-world constraints, strategic priorities, and decision-making drivers for EV adoption, while the model incorporates renewable energy sources and battery storage systems to propose optimized charging strategies. Results show that under optimal summer conditions (June 2023), charging costs can be reduced by up to 8% and CO$_{2}$ emissions by up to 17%. A medium enterprise fleet achieved a cost reduction from € 37.51 to € 34.40 and emission savings from 70.59 to 61.30 kgCO$_{2}$ eq. These findings underscore the value of integrating smart charging strategies and clean energy sources, offering a scalable, cost-effective, and environmentally responsible framework for fleet electrification.
{"title":"A Systematic Approach to Corporate Electric Fleets Implementation","authors":"Sofia Borgosano;Michela Longo","doi":"10.1109/OJVT.2025.3591872","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3591872","url":null,"abstract":"The use of vehicles for operational and logistical purposes remains a cornerstone of modern business activities. With the growing push toward sustainability and the decarbonization of transport, the transition from internal combustion engine vehicles to Electric Vehicles (EVs) presents both significant opportunities and complex challenges for corporate fleets. This study investigates the electrification of company-owned fleets through a comprehensive survey of 20 Italian companies in the mobility and logistics sectors, combined with an optimization model applied to simulated fleets of 10, 25, and 40 vehicles, representing Small, Medium, and Big Enterprises. The survey captures real-world constraints, strategic priorities, and decision-making drivers for EV adoption, while the model incorporates renewable energy sources and battery storage systems to propose optimized charging strategies. Results show that under optimal summer conditions (June 2023), charging costs can be reduced by up to 8% and CO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> emissions by up to 17%. A medium enterprise fleet achieved a cost reduction from € 37.51 to € 34.40 and emission savings from 70.59 to 61.30 kgCO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> eq. These findings underscore the value of integrating smart charging strategies and clean energy sources, offering a scalable, cost-effective, and environmentally responsible framework for fleet electrification.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2131-2142"},"PeriodicalIF":4.8,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11091451","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831825","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 : 2025-07-21DOI: 10.1109/OJVT.2025.3585072
Qin Tao;Shuangyang Li;Weijie Yuan;Slawomir Stanczak;Emanuele Viterbo;Xianbin Wang
{"title":"Guest Editorial: Special Issue on Orthogonal Time Frequency Space Modulation and Delay-Doppler Signal Processing","authors":"Qin Tao;Shuangyang Li;Weijie Yuan;Slawomir Stanczak;Emanuele Viterbo;Xianbin Wang","doi":"10.1109/OJVT.2025.3585072","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3585072","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1832-1836"},"PeriodicalIF":5.3,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11086608","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671131","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 : 2025-07-18DOI: 10.1109/OJVT.2025.3590673
Nivetha Kanthasamy;Raghvendra V. Cowlagi;Alexander Wyglinski
This paper presents a Vehicle-to-Vehicle (V2V) communication modeling framework that addresses the challenges of reliable state estimation and beamforming control in dynamic, multi-lane road environments. By integrating an extended Unscented Kalman Filter (UKF) with adaptive process and measurement noise models, the proposed approach accurately tracks vehicle trajectories under abrupt speed variations, frequent lane changes, and adverse weather conditions. A Markov chain-based lane-switching mechanism enables realistic multi-lane traffic simulations with smooth centerline trajectories spanning straight and curved road segments. To further enhance robustness, an adaptive Minimum Variance Distortionless Response (MVDR) beamforming scheme compensates for beam misalignment and mitigates interference, thereby significantly improving the Signal-to-Interference-Plus-Noise Ratio (SINR). The results demonstrate that the framework not only offers improved positioning accuracy but also achieves reliable communication performance compared to conventional methods, reinforcing its effectiveness in complex vehicular scenarios.
{"title":"A Unified Framework for Adaptive Beamforming and State Estimation in Dynamic Multi-Lane V2V Networks","authors":"Nivetha Kanthasamy;Raghvendra V. Cowlagi;Alexander Wyglinski","doi":"10.1109/OJVT.2025.3590673","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3590673","url":null,"abstract":"This paper presents a Vehicle-to-Vehicle (V2V) communication modeling framework that addresses the challenges of reliable state estimation and beamforming control in dynamic, multi-lane road environments. By integrating an extended Unscented Kalman Filter (UKF) with adaptive process and measurement noise models, the proposed approach accurately tracks vehicle trajectories under abrupt speed variations, frequent lane changes, and adverse weather conditions. A Markov chain-based lane-switching mechanism enables realistic multi-lane traffic simulations with smooth centerline trajectories spanning straight and curved road segments. To further enhance robustness, an adaptive Minimum Variance Distortionless Response (MVDR) beamforming scheme compensates for beam misalignment and mitigates interference, thereby significantly improving the Signal-to-Interference-Plus-Noise Ratio (SINR). The results demonstrate that the framework not only offers improved positioning accuracy but also achieves reliable communication performance compared to conventional methods, reinforcing its effectiveness in complex vehicular scenarios.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2085-2100"},"PeriodicalIF":4.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11083750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773247","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 : 2025-07-16DOI: 10.1109/OJVT.2025.3589661
Alif Rahmatullah Umar;Hasan Albinsaid;Chia-Po Wei;Chih-Peng Li
Uncrewed aerial vehicles (UAVs) have emerged as a promising solution for enhancing wireless networks, especially in challenging environments. However, recent studies that integrate reconfigurable intelligent surfaces (RIS) with UAVs tend to focus on limited aspects, such as single-UAV deployments or partial optimization of system parameters, thereby neglecting a comprehensive system-level design. To overcome these limitations, we propose a multi-user MISO communication network that leverages RIS-assisted UAVs to maximize both sum-rate and energy efficiency as two distinct objectives. Our approach stands out by considering multiple UAVs and incorporating four critical constraints: UAV flying areas, power limitations, transmit beamforming, and RIS requirements. We formulate separate optimization problems for sum-rate and energy efficiency, and address them using deep reinforcement learning (DRL) algorithms, namely proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG). By jointly optimizing UAV coordinates, the transmit beamforming matrix, and RIS phase shifts, our method achieves significant performance improvements under dynamic environmental conditions. Extensive simulations show that our comprehensive strategy delivers higher sum-rates and enhanced energy efficiency, underscoring its practical potential for next-generation RIS-assisted UAV communication systems.
{"title":"Deep Reinforcement Learning for RIS-Assisted Multi-UAV MU-MISO Communication Networks: Sum-Rate and Energy Efficiency Maximization","authors":"Alif Rahmatullah Umar;Hasan Albinsaid;Chia-Po Wei;Chih-Peng Li","doi":"10.1109/OJVT.2025.3589661","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3589661","url":null,"abstract":"Uncrewed aerial vehicles (UAVs) have emerged as a promising solution for enhancing wireless networks, especially in challenging environments. However, recent studies that integrate reconfigurable intelligent surfaces (RIS) with UAVs tend to focus on limited aspects, such as single-UAV deployments or partial optimization of system parameters, thereby neglecting a comprehensive system-level design. To overcome these limitations, we propose a multi-user MISO communication network that leverages RIS-assisted UAVs to maximize both sum-rate and energy efficiency as two distinct objectives. Our approach stands out by considering multiple UAVs and incorporating four critical constraints: UAV flying areas, power limitations, transmit beamforming, and RIS requirements. We formulate separate optimization problems for sum-rate and energy efficiency, and address them using deep reinforcement learning (DRL) algorithms, namely proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG). By jointly optimizing UAV coordinates, the transmit beamforming matrix, and RIS phase shifts, our method achieves significant performance improvements under dynamic environmental conditions. Extensive simulations show that our comprehensive strategy delivers higher sum-rates and enhanced energy efficiency, underscoring its practical potential for next-generation RIS-assisted UAV communication systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2033-2047"},"PeriodicalIF":4.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11081475","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739802","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 : 2025-07-14DOI: 10.1109/OJVT.2025.3588543
Tarun Jain;Sainath Bitragunta;Ashutosh Bhatia
We propose an adaptive RIS-based cooperative transmission strategy that jointly selects one of two RIS paths and dynamically optimizes the number of active meta-atoms to maximize physical layer (PHY) secrecy capacity under a total average power constraint. Unlike existing approaches that fix the RIS size K or assume identical fading on all links, our framework uses long-term statistics to probabilistically choose between two RISs (upper or lower) with arbitrary first-hop fading, and leverages instantaneous channel state information (CSI) on the selected path to solve a convex K-sizing problem via a Lagrangian multiplier approach. We derive and present the solution for optimal K, and numerically evaluate the average PHY secrecy capacity and average PHY secrecy efficiency for the proposed optimal strategy. Numerical results show that the proposed optimal-$text{K}$ strategy achieves up to 35% higher average PHY secrecy capacity and 50% improvement in average PHY secrecy efficiency compared to a fixed-K benchmark strategy across moderate power thresholds. Furthermore, we present an insightful asymptotic analysis for average PHY secrecy capacity in an interesting scaling regime. Our findings demonstrate the practical benefits of adaptive RIS for cooperative PHY secure and energy-efficient beyond fifth generation (B5G) wireless systems.
{"title":"Adaptive RIS Design and Optimization for Cooperative RIS-Assisted Wireless Systems","authors":"Tarun Jain;Sainath Bitragunta;Ashutosh Bhatia","doi":"10.1109/OJVT.2025.3588543","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3588543","url":null,"abstract":"We propose an adaptive RIS-based cooperative transmission strategy that jointly selects one of two RIS paths and dynamically optimizes the number of active meta-atoms to maximize physical layer (PHY) secrecy capacity under a total average power constraint. Unlike existing approaches that fix the RIS size <italic>K</i> or assume identical fading on all links, our framework uses long-term statistics to probabilistically choose between two RISs (upper or lower) with arbitrary first-hop fading, and leverages instantaneous channel state information (CSI) on the selected path to solve a convex <italic>K</i>-sizing problem via a Lagrangian multiplier approach. We derive and present the solution for optimal K, and numerically evaluate the average PHY secrecy capacity and average PHY secrecy efficiency for the proposed optimal strategy. Numerical results show that the proposed optimal-<inline-formula><tex-math>$text{K}$</tex-math></inline-formula> strategy achieves up to 35% higher average PHY secrecy capacity and 50% improvement in average PHY secrecy efficiency compared to a fixed-<italic>K</i> benchmark strategy across moderate power thresholds. Furthermore, we present an insightful asymptotic analysis for average PHY secrecy capacity in an interesting scaling regime. Our findings demonstrate the practical benefits of adaptive RIS for cooperative PHY secure and energy-efficient beyond fifth generation (B5G) wireless systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2022-2032"},"PeriodicalIF":4.8,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11078926","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739947","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 : 2025-07-08DOI: 10.1109/OJVT.2025.3587028
Sherief Hashima;Ahmad Gendia;Kohei Hatano;Osamu Muta;Mostafa S. Nada;Ehab Mahmoud Mohamed
The convergence of sixth-generation (6G) networks with unmanned aerial vehicles (UAVs) and satellites is poised to introduce substantial improvements to the landscape of wireless communication, paving the way for a unified and uninterrupted space-air-ground-sea network that ensures comprehensive global connectivity. At the heart of this transformative paradigm lies artificial intelligence (AI), which drives innovation across diverse sectors by enhancing decision-making autonomy, enabling real-time data processing, and optimizing network performance and coverage. This survey paper explores AI-enabled UAV-satellite communications for 6G applications, focusing on its challenges, potential, and future. This new system combines the strengths of 6G networks, UAVs (advanced drones), and satellites. It opens up new possibilities in precision agriculture, disaster management, enhanced telecommunication services, and remote sensing. Despite its promise, this field faces complex challenges. These include spectrum management, security risks, regulatory barriers, and integrating AI operations seamlessly. This paper comprehensively analyzes these challenges, offering innovative solutions and outlining future research directions to unlock the complete capabilities of 6G-enabled UAV-satellite communications. Furthermore, it includes a case study demonstrating the effectiveness of multi-armed bandit (MAB) algorithms in optimizing resource allocation and decision-making processes for UAV-low Earth orbit (LEO) satellite communication scenarios, showcasing significant improvements in network performance. This work lays the foundation for a new generation of ultra-connected, data-driven applications that will redefine global connectivity and technological advancement by addressing these critical aspects.
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