The integration of vehicle-to-everything (V2X) communication paradigms with sixth-generation (6G) wireless networks and artificial intelligence (AI) frameworks enables ultra-reliable, low-latency communication, which is essential for real-time decision-making in autonomous vehicles (AVs) and smart cities. Proprioceptive and exteroceptive sensors allow AVs to perceive both their internal states and external surroundings, ensuring rapid responses to critical events. Integrated sensing and communication (ISAC) enhances this capability by jointly leveraging perception and communication, enabling V2X systems to adapt intelligently to real-time emergencies. In this paper, we propose a probabilistic, data-driven, hierarchical, interactive, and explainable approach for an intelligent agent, i.e., a base station (BS), to learn the dynamic environmental perception from the 3D LiDAR point clouds and the strength of radio-frequency (RF) power signals between the connected BS and vehicles. An interactive coupled Markov jump particle filter (IC-MJPF) is proposed in the inference phase to leverage the probabilistic information provided by an interactive coupled generalized dynamic Bayesian network (IC-GDBN) to predict various types of LiDAR and RF power blockages, as well as to detect real-time abnormalities in an unsupervised manner arising from dynamic environmental changes. Experimental results demonstrate that the proposed approach consistently outperforms existing baseline studies, achieving superior performance in terms of blockage detection accuracy within 50 milliseconds across various blockage situations. These findings underscore the robustness and effectiveness of the proposed framework in addressing both physical and digital blockage challenges within the ISAC domain for connected V2X networks.
{"title":"Integrated Sensing and Communication for Blockage Detection in V2X Networks","authors":"Saleemullah Memon;Ali Krayani;Pamela Zontone;Lucio Marcenaro;David Martín Gómez;Carlo Regazzoni","doi":"10.1109/OJCOMS.2026.3652319","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3652319","url":null,"abstract":"The integration of vehicle-to-everything (V2X) communication paradigms with sixth-generation (6G) wireless networks and artificial intelligence (AI) frameworks enables ultra-reliable, low-latency communication, which is essential for real-time decision-making in autonomous vehicles (AVs) and smart cities. Proprioceptive and exteroceptive sensors allow AVs to perceive both their internal states and external surroundings, ensuring rapid responses to critical events. Integrated sensing and communication (ISAC) enhances this capability by jointly leveraging perception and communication, enabling V2X systems to adapt intelligently to real-time emergencies. In this paper, we propose a probabilistic, data-driven, hierarchical, interactive, and explainable approach for an intelligent agent, i.e., a base station (BS), to learn the dynamic environmental perception from the 3D LiDAR point clouds and the strength of radio-frequency (RF) power signals between the connected BS and vehicles. An interactive coupled Markov jump particle filter (IC-MJPF) is proposed in the inference phase to leverage the probabilistic information provided by an interactive coupled generalized dynamic Bayesian network (IC-GDBN) to predict various types of LiDAR and RF power blockages, as well as to detect real-time abnormalities in an unsupervised manner arising from dynamic environmental changes. Experimental results demonstrate that the proposed approach consistently outperforms existing baseline studies, achieving superior performance in terms of blockage detection accuracy within 50 milliseconds across various blockage situations. These findings underscore the robustness and effectiveness of the proposed framework in addressing both physical and digital blockage challenges within the ISAC domain for connected V2X networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"559-582"},"PeriodicalIF":6.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11340648","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026617","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}
Near-field wideband communication systems, empowered by extra-large scale antenna arrays (ELAAs), face a fundamental challenge: the spatial beam split effect due to frequency-flat analog beamformers. To mitigate this, true-time delay (TTD) based hybrid beamforming architectures have been proposed. Optimizing this architecture remains difficult due to non-convex constraints and frequency-selective channels. In this work, we propose a Riemannian optimization framework for hybrid analog-digital beamforming in fully-connected TTD-based architectures. We formulate the beamformer design as a constrained matrix approximation problem over product manifolds, incorporating unit-modulus constraints and realistic hardware limitations. A tailored Riemannian gradient descent algorithm is developed to efficiently approximate the fully-digital solution across all subcarriers. Through extensive numerical evaluations in near-field multi-user settings, the proposed method consistently delivers superior spectral efficiency and substantially reduced computational complexity relative to existing approaches across a broad frequency band.
{"title":"TTD-Based Hybrid Beamforming for Multi-User Near-Field Communications: A Riemannian Optimization Approach","authors":"Damir Salakhov;Nikola Zlatanov;Alexey Frolov;Manjesh Kumar Hanawal","doi":"10.1109/OJCOMS.2026.3651840","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3651840","url":null,"abstract":"Near-field wideband communication systems, empowered by extra-large scale antenna arrays (ELAAs), face a fundamental challenge: the spatial beam split effect due to frequency-flat analog beamformers. To mitigate this, true-time delay (TTD) based hybrid beamforming architectures have been proposed. Optimizing this architecture remains difficult due to non-convex constraints and frequency-selective channels. In this work, we propose a Riemannian optimization framework for hybrid analog-digital beamforming in fully-connected TTD-based architectures. We formulate the beamformer design as a constrained matrix approximation problem over product manifolds, incorporating unit-modulus constraints and realistic hardware limitations. A tailored Riemannian gradient descent algorithm is developed to efficiently approximate the fully-digital solution across all subcarriers. Through extensive numerical evaluations in near-field multi-user settings, the proposed method consistently delivers superior spectral efficiency and substantially reduced computational complexity relative to existing approaches across a broad frequency band.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"583-597"},"PeriodicalIF":6.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11339963","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026591","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 : 2026-01-05DOI: 10.1109/OJCOMS.2026.3651027
Ahmet Melih Ince;Ayse Elif Canbilen;Halim Yanikomeroglu
In the dynamic landscape of wireless communication systems, high-altitude platform stations (HAPS) technology heralds a new era of connectivity solutions. The HAPS ensures uninterrupted operation even under challenging conditions where connectivity via terrestrial networks is unavailable. This approach effectively supports real-time applications by dynamically optimizing resource allocation and communication modes. Considering that, this research addresses the strategic integration of HAPS into vehicle-to-everything (V2X) networks. Specifically, multiple autonomous platoons using V2X technology distribute cooperative awareness messages (CAMs) to their followers, attempting to ensure the timely delivery of safety-critical messages not only to the roadside unit (RSU) but also to the HAPS, introducing link-level redundancy to the wireless network. We formulate a multi-objective optimization problem to minimize the age of information (AoI) and power consumption while maximizing the probability of CAM delivery rate. We utilize a multi-agent deep reinforcement learning (MADRL) based resource allocation framework, where each platoon leader (PL) acts as an agent and interacts with the environment to learn its optimal policy. In this framework, based on a deep deterministic policy gradient (DDPG) algorithm, in addition to a local critic trained to predict the individual reward of each PL, a global critic is also trained to predict the global expected reward and motivate PLs to cooperative behavior. The presented simulation results demonstrate the effectiveness of HAPS integration in the considered V2X scenario and the superiority of the proposed algorithm over benchmark algorithms in terms of AoI and power consumption performance.
{"title":"AoI-Aware HAPS-Aided Multi-Agent Framework for Resource Management in V2X Networks","authors":"Ahmet Melih Ince;Ayse Elif Canbilen;Halim Yanikomeroglu","doi":"10.1109/OJCOMS.2026.3651027","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3651027","url":null,"abstract":"In the dynamic landscape of wireless communication systems, high-altitude platform stations (HAPS) technology heralds a new era of connectivity solutions. The HAPS ensures uninterrupted operation even under challenging conditions where connectivity via terrestrial networks is unavailable. This approach effectively supports real-time applications by dynamically optimizing resource allocation and communication modes. Considering that, this research addresses the strategic integration of HAPS into vehicle-to-everything (V2X) networks. Specifically, multiple autonomous platoons using V2X technology distribute cooperative awareness messages (CAMs) to their followers, attempting to ensure the timely delivery of safety-critical messages not only to the roadside unit (RSU) but also to the HAPS, introducing link-level redundancy to the wireless network. We formulate a multi-objective optimization problem to minimize the age of information (AoI) and power consumption while maximizing the probability of CAM delivery rate. We utilize a multi-agent deep reinforcement learning (MADRL) based resource allocation framework, where each platoon leader (PL) acts as an agent and interacts with the environment to learn its optimal policy. In this framework, based on a deep deterministic policy gradient (DDPG) algorithm, in addition to a local critic trained to predict the individual reward of each PL, a global critic is also trained to predict the global expected reward and motivate PLs to cooperative behavior. The presented simulation results demonstrate the effectiveness of HAPS integration in the considered V2X scenario and the superiority of the proposed algorithm over benchmark algorithms in terms of AoI and power consumption performance.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"598-613"},"PeriodicalIF":6.3,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11329056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026542","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 : 2026-01-05DOI: 10.1109/OJCOMS.2025.3646206
Jang-Geun Yoo;Jong-Moon Chung
This study investigates the joint optimization of camera control, target acquisition, trajectory planning, and transmission scheduling for surveillance uncrewed aerial vehicles (UAVs) that provide various imaging and delivery services, such as traffic monitoring and security. The objective of the proposed UAV flight and trajectory control for surveillance camera imaging and communication (UTSCC) scheme is to capture images of designated ground targets and deliver the image data to requesting user equipment (UE) within specified deadlines. The UTSCC scheme attempts to maximize the total amount of successfully delivered target data considering realistic characteristics of the camera, UAV trajectory, transmission schedule, and selected targets. The developed framework makes decisions of the target acquisition and transmission scheduling using the exact penalty method (EPM) and applies non-convex optimization of the camera tilt angle and UAV trajectory using successive convex approximation (SCA) considering deadlines for UE delivery. The UTSCC scheme is designed to be practical for real-world UAV surveillance operations, where the simulation results demonstrate that the proposed method outperforms existing schemes in terms of delivery performance and overall system efficiency.
{"title":"UAV Flight and Trajectory Control for Surveillance Camera Imaging and Wireless Communication Joint Optimization","authors":"Jang-Geun Yoo;Jong-Moon Chung","doi":"10.1109/OJCOMS.2025.3646206","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3646206","url":null,"abstract":"This study investigates the joint optimization of camera control, target acquisition, trajectory planning, and transmission scheduling for surveillance uncrewed aerial vehicles (UAVs) that provide various imaging and delivery services, such as traffic monitoring and security. The objective of the proposed UAV flight and trajectory control for surveillance camera imaging and communication (UTSCC) scheme is to capture images of designated ground targets and deliver the image data to requesting user equipment (UE) within specified deadlines. The UTSCC scheme attempts to maximize the total amount of successfully delivered target data considering realistic characteristics of the camera, UAV trajectory, transmission schedule, and selected targets. The developed framework makes decisions of the target acquisition and transmission scheduling using the exact penalty method (EPM) and applies non-convex optimization of the camera tilt angle and UAV trajectory using successive convex approximation (SCA) considering deadlines for UE delivery. The UTSCC scheme is designed to be practical for real-world UAV surveillance operations, where the simulation results demonstrate that the proposed method outperforms existing schemes in terms of delivery performance and overall system efficiency.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"182-195"},"PeriodicalIF":6.3,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11327452","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929603","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 : 2026-01-05DOI: 10.1109/OJCOMS.2026.3650906
Faisal Al-Kamali;Francois Chan;James H. Bayes;Claude D’Amours
Wireless communications in military and civilian applications demand robust security and energy efficiency against evolving threats. This paper presents a novel strategy for optimizing UAV placement via spatial threat exclusion to achieve secure and energy-efficient links without relying on threat channel state information or complex signal processing techniques like beamforming or artificial noise. Our approach is motivated by the inherent alignment between covertness and interference mitigation; it is therefore equally applicable to interference scenarios, enhancing security by lowering detection risk and improving energy efficiency by reducing the communication distance between the UAV and the base station (BS). To ensure robustness, the model is developed for a conservative coverage-edge scenario, which represents the worst-case condition for both signal quality and secrecy, thereby serving as a lower-bound performance benchmark. The proposed method combines closed-form solutions for initial placement with iterative refinements, enabling real-time adaptation to dynamic threat mobility without extra hardware or computational burden. Two algorithms are developed: first for a single-threat, single-user scenario and then extended to multi-threat, multi-user settings. Algorithm 1 is a computationally efficient, quadratic difference-based method for rapid deployment, while Algorithm 2 is an inverse quadratic penalty-based technique for threat avoidance in dynamic environments. Simulation results for both scenarios confirm that the proposed algorithms consistently achieve positive secrecy rates across diverse propagation environments while yielding significant energy and time savings compared to an optimal-placement baseline. A comparative evaluation with a particle swarm optimization (PSO) baseline further demonstrates that our algorithms match optimal performance with substantially lower computational overhead.
{"title":"Optimizing UAV Placement With Spatial Threat Exclusion for Secure and Energy-Efficient Wireless Communications","authors":"Faisal Al-Kamali;Francois Chan;James H. Bayes;Claude D’Amours","doi":"10.1109/OJCOMS.2026.3650906","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3650906","url":null,"abstract":"Wireless communications in military and civilian applications demand robust security and energy efficiency against evolving threats. This paper presents a novel strategy for optimizing UAV placement via spatial threat exclusion to achieve secure and energy-efficient links without relying on threat channel state information or complex signal processing techniques like beamforming or artificial noise. Our approach is motivated by the inherent alignment between covertness and interference mitigation; it is therefore equally applicable to interference scenarios, enhancing security by lowering detection risk and improving energy efficiency by reducing the communication distance between the UAV and the base station (BS). To ensure robustness, the model is developed for a conservative coverage-edge scenario, which represents the worst-case condition for both signal quality and secrecy, thereby serving as a lower-bound performance benchmark. The proposed method combines closed-form solutions for initial placement with iterative refinements, enabling real-time adaptation to dynamic threat mobility without extra hardware or computational burden. Two algorithms are developed: first for a single-threat, single-user scenario and then extended to multi-threat, multi-user settings. <xref>Algorithm 1</xref> is a computationally efficient, quadratic difference-based method for rapid deployment, while <xref>Algorithm 2</xref> is an inverse quadratic penalty-based technique for threat avoidance in dynamic environments. Simulation results for both scenarios confirm that the proposed algorithms consistently achieve positive secrecy rates across diverse propagation environments while yielding significant energy and time savings compared to an optimal-placement baseline. A comparative evaluation with a particle swarm optimization (PSO) baseline further demonstrates that our algorithms match optimal performance with substantially lower computational overhead.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"539-558"},"PeriodicalIF":6.3,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11329043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982369","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}
Securing the near-real-time (near-RT) control operations in Open Radio Access Networks (Open RAN) is increasingly critical, yet remains insufficiently addressed, as new runtime threats target the control loop while the system is operational. In this paper, we propose a multi-layer defence framework designed to enhance the security of near-RT RAN Intelligent Controller (RIC) operations. We classify operational-time threats into three categories—message-level, data-level, and control logic-level—and design and implement a dedicated detection and mitigation component for each: a signature-based E2 message inspection module performing structural and semantic validation of signalling exchanges, a telemetry poisoning detector based on temporal anomaly scoring using an LSTM network, and a runtime xApp attestation mechanism based on an execution-time hash challenge–response. The framework is evaluated on an Open RAN testbed comprising FlexRIC and a commercial RAN emulator, demonstrating effective detection rates, low latency overheads, and practical integration feasibility. Results indicate that the proposed safeguards can operate within near-RT time constraints while significantly improving protection against runtime attacks, introducing less than 80 ms overhead for a network with 500 User Equipment (UEs). Overall, this work lays the foundation for deployable, layered, and policy-driven runtime security architectures for the near-RT RIC control loop in Open RAN, and provides an extensible framework into which future mitigation policies and threat-specific modules can be integrated.
{"title":"Toward a Multi-Layer Defence Framework for Securing Near-Real-Time Operations in Open RAN","authors":"Hamed Alimohammadi;Samara Mayhoub;Sotiris Chatzimiltis;Mohammad Shojafar;Muhammad Nasir Mumtaz Bhutta","doi":"10.1109/OJCOMS.2025.3650736","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3650736","url":null,"abstract":"Securing the near-real-time (near-RT) control operations in Open Radio Access Networks (Open RAN) is increasingly critical, yet remains insufficiently addressed, as new runtime threats target the control loop while the system is operational. In this paper, we propose a multi-layer defence framework designed to enhance the security of near-RT RAN Intelligent Controller (RIC) operations. We classify operational-time threats into three categories—message-level, data-level, and control logic-level—and design and implement a dedicated detection and mitigation component for each: a signature-based E2 message inspection module performing structural and semantic validation of signalling exchanges, a telemetry poisoning detector based on temporal anomaly scoring using an LSTM network, and a runtime xApp attestation mechanism based on an execution-time hash challenge–response. The framework is evaluated on an Open RAN testbed comprising FlexRIC and a commercial RAN emulator, demonstrating effective detection rates, low latency overheads, and practical integration feasibility. Results indicate that the proposed safeguards can operate within near-RT time constraints while significantly improving protection against runtime attacks, introducing less than 80 ms overhead for a network with 500 User Equipment (UEs). Overall, this work lays the foundation for deployable, layered, and policy-driven runtime security architectures for the near-RT RIC control loop in Open RAN, and provides an extensible framework into which future mitigation policies and threat-specific modules can be integrated.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"480-497"},"PeriodicalIF":6.3,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11322785","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982353","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 : 2025-12-29DOI: 10.1109/OJCOMS.2025.3649168
José Manuel Rúa-Estévez;Pablo Fondo-Ferreiro;Felipe Gil-Castiñeira;Francisco Javier González-Castaño
Mobility management remains a key challenge in cellular networks, particularly handover management. The introduction of a wider range of frequencies and radio access technologies in 5G networks further intensifies this challenge. For example, the 5G standard considers the joint deployment of cells using new frequency bands beyond 24 GHz (frequency range 2, FR2), alongside cells operating in the sub-6 GHz bands (frequency range 1, FR1), referred to as millimeter-wave heterogeneous networks (mmWave HetNets). However, the 3GPP-defined handover algorithms do not fully leverage the network capacity of HetNets, as they originate from single-band scenarios and base their decisions on received power levels. We propose two novel strategies for handover management in HetNets and evaluate them using a new, realistic, end-to-end full-stack 5G metasimulator, which is another key contribution of this paper. One of the strategies is based on a heuristic algorithm, while the other relies on a Double Deep Q-Network (DDQN) architecture. The current main 5G full-stack simulators do not support arbitrary handover strategies, so state-of-the-art research in this field has relied on custom simulators with simplified system models. To enable realistic simulations, we propose a novel method that orchestrates multiple 5G simulations to emulate a complex 5G environment with arbitrary handover capabilities. The evaluation of the proposed strategies shows an improvement between 65% and 450% in aggregated throughput compared to the 3GPP Release 15 handover and Conditional Handover algorithm, depending on the scenario.
{"title":"Intelligent Handover Solutions for Heterogeneous B5G Cellular Networks: Proposals and Full-Stack Evaluation","authors":"José Manuel Rúa-Estévez;Pablo Fondo-Ferreiro;Felipe Gil-Castiñeira;Francisco Javier González-Castaño","doi":"10.1109/OJCOMS.2025.3649168","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3649168","url":null,"abstract":"Mobility management remains a key challenge in cellular networks, particularly handover management. The introduction of a wider range of frequencies and radio access technologies in 5G networks further intensifies this challenge. For example, the 5G standard considers the joint deployment of cells using new frequency bands beyond 24 GHz (frequency range 2, FR2), alongside cells operating in the sub-6 GHz bands (frequency range 1, FR1), referred to as millimeter-wave heterogeneous networks (mmWave HetNets). However, the 3GPP-defined handover algorithms do not fully leverage the network capacity of HetNets, as they originate from single-band scenarios and base their decisions on received power levels. We propose two novel strategies for handover management in HetNets and evaluate them using a new, realistic, end-to-end full-stack 5G metasimulator, which is another key contribution of this paper. One of the strategies is based on a heuristic algorithm, while the other relies on a Double Deep Q-Network (DDQN) architecture. The current main 5G full-stack simulators do not support arbitrary handover strategies, so state-of-the-art research in this field has relied on custom simulators with simplified system models. To enable realistic simulations, we propose a novel method that orchestrates multiple 5G simulations to emulate a complex 5G environment with arbitrary handover capabilities. The evaluation of the proposed strategies shows an improvement between 65% and 450% in aggregated throughput compared to the 3GPP Release 15 handover and Conditional Handover algorithm, depending on the scenario.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1-19"},"PeriodicalIF":6.3,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11316620","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929663","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}
Integrated sensing and communication (ISAC) has gained significant traction in recent years, primarily because it allows existing communication infrastructure to support sensing applications with minimal additional costs. In particular, millimeter-wave (mmWave) ISAC has the potential to offer improved sensing performance in applications such as pose estimation and gesture recognition. For complex sensing tasks and environments, data-driven sensing, which relies on deep learning, is becoming increasingly popular and has shown promising results. However, deep learning models for these tasks require large labeled datasets to achieve high accuracy. Dataset collection and labeling are labor-intensive and time-consuming. Consequently, there is growing interest in leveraging unlabeled data to overcome these challenges. To address this, we propose mmGAN, a semi-supervised method for ISAC-based gesture recognition. We propose a novel loss function for mmGAN based on softplus, feature matching, and manifold regularization to significantly improve gesture recognition performance. We evaluate mmGAN on a 5G Orthogonal Frequency Division Multiplexing (OFDM) mmWave dataset comprising power per beam pair measurements. When training both mmGAN and the supervised baseline with only 0.6% of the labeled data, mmGAN demonstrates up to 25 percentage points higher accuracy than the supervised baseline. Our method serves as a strong foundation for cross-subject transfer learning, demonstrating the significant value of leveraging unlabeled data to enhance cross-domain sensing performance in ISAC systems. Our results demonstrate that the proposed loss function achieves superior performance across diverse subjects. Further, mmGAN significantly narrows the performance gap between semi-supervised and fully supervised models on the publicly available Widar dataset. Moreover, we provide an interpretable analysis of mmGAN performance through saliency maps and ablation studies, revealing key insights into the model’s behavior and generalization. This work is the first to evaluate gesture recognition performance in 5G OFDM mmWave ISAC systems using a semi-supervised learning approach, covering the entire pipeline from testbed implementation to model evaluation.
{"title":"mmGAN: Semi-Supervised GAN for Improved Gesture Recognition in mmWave ISAC Systems","authors":"Nabeel Nisar Bhat;Siddhartha Kumar;Mohammad Hossein Moghaddam;Jakob Struye;Jesus Omar Lacruz;Jacopo Pegoraro;Joerg Widmer;Rafael Berkvens;Jeroen Famaey","doi":"10.1109/OJCOMS.2025.3649235","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3649235","url":null,"abstract":"Integrated sensing and communication (ISAC) has gained significant traction in recent years, primarily because it allows existing communication infrastructure to support sensing applications with minimal additional costs. In particular, millimeter-wave (mmWave) ISAC has the potential to offer improved sensing performance in applications such as pose estimation and gesture recognition. For complex sensing tasks and environments, data-driven sensing, which relies on deep learning, is becoming increasingly popular and has shown promising results. However, deep learning models for these tasks require large labeled datasets to achieve high accuracy. Dataset collection and labeling are labor-intensive and time-consuming. Consequently, there is growing interest in leveraging unlabeled data to overcome these challenges. To address this, we propose mmGAN, a semi-supervised method for ISAC-based gesture recognition. We propose a novel loss function for mmGAN based on softplus, feature matching, and manifold regularization to significantly improve gesture recognition performance. We evaluate mmGAN on a 5G Orthogonal Frequency Division Multiplexing (OFDM) mmWave dataset comprising power per beam pair measurements. When training both mmGAN and the supervised baseline with only 0.6% of the labeled data, mmGAN demonstrates up to 25 percentage points higher accuracy than the supervised baseline. Our method serves as a strong foundation for cross-subject transfer learning, demonstrating the significant value of leveraging unlabeled data to enhance cross-domain sensing performance in ISAC systems. Our results demonstrate that the proposed loss function achieves superior performance across diverse subjects. Further, mmGAN significantly narrows the performance gap between semi-supervised and fully supervised models on the publicly available Widar dataset. Moreover, we provide an interpretable analysis of mmGAN performance through saliency maps and ablation studies, revealing key insights into the model’s behavior and generalization. This work is the first to evaluate gesture recognition performance in 5G OFDM mmWave ISAC systems using a semi-supervised learning approach, covering the entire pipeline from testbed implementation to model evaluation.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"95-117"},"PeriodicalIF":6.3,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11317966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929600","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}
This survey provides a comprehensive overview of Cooperative non orthogonal multiple access (C-NOMA) systems, where cooperative relaying enhances reliability, extends coverage, and strengthens system robustness. The study begins by classifying the fundamental structures of C-NOMA into two key categories: user-assisted relaying, where end-user devices forward information to other users, and dedicated relaying, where separate relay nodes assist transmission. Beyond its basic principles, the survey explores the integration of C-NOMA with advanced technologies such as cognitive radio (CR), full-duplex (FD) transmission, simultaneous wireless information and power transfer (SWIPT), power line communication (PLC), multiple-input multiple-output (MIMO), heterogeneous networks (HetNets), rate-splitting multiple access (RSMA), visible light communication (VLC), and intelligent reflecting surfaces (IRS). These integrations significantly improve energy efficiency, spectral utilization, and coverage reliability. Moreover, artificial intelligence (AI), including deep reinforcement learning (DRL) and federated learning (FL), is identified as a vital enabler for intelligent power allocation, relay selection, and user clustering, addressing the limitations of traditional optimization approaches. This survey elaborates in detail the integration of C-NOMA with blockchain, edge computing, quantum computing, extended reality (XR), i.e., virtual reality/augmented reality (VR/AR), and unmanned aerial vehicles (UAV). Furthermore, this survey identifies quantum-enabled C-NOMA as a transformative paradigm, where quantum key distribution (QKD) ensures physical layer security (PLS), and quantum machine learning (QML) enhances interference mitigation and large-scale optimization. Performance evaluation in terms of spectral efficiency (SE), energy efficiency, and quality-of-service (QoS) requirements is critically analyzed, with special emphasis on challenges such as accurate channel state information (CSI) acquisition, robust successive interference cancellation (SIC), scalable relay selection, and security in heterogeneous environments. In summary, this survey consolidates the state-of-the-art developments in C-NOMA and outlines future research directions, including AI-driven optimization, quantum-assisted communication, UAV and satellite integration, reconfigurable intelligent surface (RIS)-enabled designs, and hybrid OMA-NOMA switching. By addressing these open challenges, C-NOMA is expected to serve as a cornerstone for secure, energy-efficient, and ultra-reliable connectivity in 6G and beyond, enabling massive IoT, autonomous systems, and next-generation smart city infrastructures.
{"title":"Survey of Cooperative NOMA for Beyond 5G: State-of-the-Art, Applications and Research Directions","authors":"Swathi Priya Indraganti;Suseela Vappangi;Anoop Kumar Mishra;Sudha Ellison Mathe;Ali Arshad Nasir;T. Deepa;Neha Gupta","doi":"10.1109/OJCOMS.2025.3648469","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3648469","url":null,"abstract":"This survey provides a comprehensive overview of Cooperative non orthogonal multiple access (C-NOMA) systems, where cooperative relaying enhances reliability, extends coverage, and strengthens system robustness. The study begins by classifying the fundamental structures of C-NOMA into two key categories: user-assisted relaying, where end-user devices forward information to other users, and dedicated relaying, where separate relay nodes assist transmission. Beyond its basic principles, the survey explores the integration of C-NOMA with advanced technologies such as cognitive radio (CR), full-duplex (FD) transmission, simultaneous wireless information and power transfer (SWIPT), power line communication (PLC), multiple-input multiple-output (MIMO), heterogeneous networks (HetNets), rate-splitting multiple access (RSMA), visible light communication (VLC), and intelligent reflecting surfaces (IRS). These integrations significantly improve energy efficiency, spectral utilization, and coverage reliability. Moreover, artificial intelligence (AI), including deep reinforcement learning (DRL) and federated learning (FL), is identified as a vital enabler for intelligent power allocation, relay selection, and user clustering, addressing the limitations of traditional optimization approaches. This survey elaborates in detail the integration of C-NOMA with blockchain, edge computing, quantum computing, extended reality (XR), i.e., virtual reality/augmented reality (VR/AR), and unmanned aerial vehicles (UAV). Furthermore, this survey identifies quantum-enabled C-NOMA as a transformative paradigm, where quantum key distribution (QKD) ensures physical layer security (PLS), and quantum machine learning (QML) enhances interference mitigation and large-scale optimization. Performance evaluation in terms of spectral efficiency (SE), energy efficiency, and quality-of-service (QoS) requirements is critically analyzed, with special emphasis on challenges such as accurate channel state information (CSI) acquisition, robust successive interference cancellation (SIC), scalable relay selection, and security in heterogeneous environments. In summary, this survey consolidates the state-of-the-art developments in C-NOMA and outlines future research directions, including AI-driven optimization, quantum-assisted communication, UAV and satellite integration, reconfigurable intelligent surface (RIS)-enabled designs, and hybrid OMA-NOMA switching. By addressing these open challenges, C-NOMA is expected to serve as a cornerstone for secure, energy-efficient, and ultra-reliable connectivity in 6G and beyond, enabling massive IoT, autonomous systems, and next-generation smart city infrastructures.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"40-94"},"PeriodicalIF":6.3,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11315150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929665","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 : 2025-12-26DOI: 10.1109/OJCOMS.2025.3648840
Balqis Yafis;Jane-Hwa Huang;Chih-Min Yu;Li-Chun Wang
Reconfigurable Intelligent Surface (RIS) technology enables programmable wireless propagation through passive beamforming. However, large-scale implementations often incur excessive control overhead and computational complexity. This paper proposes a Spirograph-based (SG) discrete beamforming framework that constructs a scalable RIS codebook using non-uniform quantization points derived from parametric Spirograph geometry. The proposed approach quantizes beam directions rather than individual phase shifts, thereby minimizing signaling requirements while maintaining near-optimal phase alignment between reflected and direct paths. A reduced-codebook mechanism is further developed to eliminate redundant beams in irregular coverage regions, significantly lowering complexity without compromising performance. Simulation results demonstrate that the SG scheme achieves over 95% of the average data rate of the ideal continuous phase-shift method while reducing control overhead by more than 98% compared with the discrete phase-shift (DPS) approach. Notably, SG outperforms DPS by up to 2.5 Mbit/s in average data rate. Moreover, the Beam-Angle Region (BAR) selection algorithm achieves a favorable trade-off between data rate and complexity, providing near–exhaustive-search performance with approximately 20% fewer beam evaluations. These results confirm that the proposed SG framework offers an efficient, scalable, and geometrically robust solution for large-scale RIS-assisted wireless communication systems.
{"title":"Spirograph-Based Discrete Beamforming and Scalable Codebook Design for Reconfigurable Intelligent Surface Systems","authors":"Balqis Yafis;Jane-Hwa Huang;Chih-Min Yu;Li-Chun Wang","doi":"10.1109/OJCOMS.2025.3648840","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3648840","url":null,"abstract":"Reconfigurable Intelligent Surface (RIS) technology enables programmable wireless propagation through passive beamforming. However, large-scale implementations often incur excessive control overhead and computational complexity. This paper proposes a Spirograph-based (SG) discrete beamforming framework that constructs a scalable RIS codebook using non-uniform quantization points derived from parametric Spirograph geometry. The proposed approach quantizes beam directions rather than individual phase shifts, thereby minimizing signaling requirements while maintaining near-optimal phase alignment between reflected and direct paths. A reduced-codebook mechanism is further developed to eliminate redundant beams in irregular coverage regions, significantly lowering complexity without compromising performance. Simulation results demonstrate that the SG scheme achieves over 95% of the average data rate of the ideal continuous phase-shift method while reducing control overhead by more than 98% compared with the discrete phase-shift (DPS) approach. Notably, SG outperforms DPS by up to 2.5 Mbit/s in average data rate. Moreover, the Beam-Angle Region (BAR) selection algorithm achieves a favorable trade-off between data rate and complexity, providing near–exhaustive-search performance with approximately 20% fewer beam evaluations. These results confirm that the proposed SG framework offers an efficient, scalable, and geometrically robust solution for large-scale RIS-assisted wireless communication systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"521-538"},"PeriodicalIF":6.3,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11316334","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982354","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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