Pub Date : 2025-01-23DOI: 10.1109/OJCOMS.2025.3533358
Abdalla Salih;Suhail Al-Dharrab
Autonomous aerial vehicles (AAVs) are expected to play a significant role in future Integrated Sensing and Communication (ISAC) systems. However, the performance of multi-user communications in airborne ISAC systems relies significantly on the AAV position and flight trajectory, and accurate radio propagation channel model for the scene/environment among other factors. In this paper, we propose a low-complexity AAV-enabled ISAC system model and investigate the spectral efficiency and information-outage probability. Our model employs a hybrid beamforming (HBF) design, where both the AAV and users have multiple antenna arrays which enables multi-user communications with the AAV along with the AAV’s sensing capabilities of targets of interest. We propose a large-scale path loss model taking into consideration the realistic scatterers, deflection, and reflections in the scene. We investigate and analyze the average spectral efficiency for various scenarios, observe the optimal AAV altitude, and consider sensing and user interference while studying the system parameters.
{"title":"Spectral Efficiency and Hybrid Beamforming for Multi-User in Airborne Integrated Sensing and Communication Systems","authors":"Abdalla Salih;Suhail Al-Dharrab","doi":"10.1109/OJCOMS.2025.3533358","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3533358","url":null,"abstract":"Autonomous aerial vehicles (AAVs) are expected to play a significant role in future Integrated Sensing and Communication (ISAC) systems. However, the performance of multi-user communications in airborne ISAC systems relies significantly on the AAV position and flight trajectory, and accurate radio propagation channel model for the scene/environment among other factors. In this paper, we propose a low-complexity AAV-enabled ISAC system model and investigate the spectral efficiency and information-outage probability. Our model employs a hybrid beamforming (HBF) design, where both the AAV and users have multiple antenna arrays which enables multi-user communications with the AAV along with the AAV’s sensing capabilities of targets of interest. We propose a large-scale path loss model taking into consideration the realistic scatterers, deflection, and reflections in the scene. We investigate and analyze the average spectral efficiency for various scenarios, observe the optimal AAV altitude, and consider sensing and user interference while studying the system parameters.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1190-1201"},"PeriodicalIF":6.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10851318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455291","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}
The article introduces an innovative wireless backhauling approach employing non-orthogonal multiple access (NOMA) and automatic repeat request (ARQ) mechanisms. In this novel scheme, power allocation follows a round-robin (RR) method, ensuring equitable performance among paired users. To address the potential packet loss after ARQ, an intelligent packet repair technique is incorporated to recover the dropped packets. A key feature involves storing dropped data packets for subsequent processing before forwarding them to their respective IoT devices (IoDs). The proposed methodology hinges on recognizing that interference within a dropped packet may correspond to a packet retrievable in a forthcoming transmission, facilitating recovery through iterative successive interference cancellation (SIC). Significantly, the scheme enhances data reliability without necessitating an increase in the ARQ retransmission limit, which makes it particularly suited for certain Internet of things (IoT) applications. Empirical results confirm a substantial success rate in recovering dropped packets. Notably, the iterative interference cancellation (IIC) technique demonstrated a noteworthy reduction in the packet drop rate (PDR) from $10^{-1}$ to $10^{-3}$ , representing a 100-fold improvement, which implies the successful recovery of 99% of the packets initially dropped in specific scenarios, showcasing the efficacy of the proposed approach.
{"title":"Intelligent NOMA-Based Wireless Backhauling for IoT Applications Without End-Device CSI","authors":"Ashfaq Ahmed;Arafat Al-Dweik;Youssef Iraqi;Hamad Yahya;Ernesto Damiani","doi":"10.1109/OJCOMS.2025.3532998","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3532998","url":null,"abstract":"The article introduces an innovative wireless backhauling approach employing non-orthogonal multiple access (NOMA) and automatic repeat request (ARQ) mechanisms. In this novel scheme, power allocation follows a round-robin (RR) method, ensuring equitable performance among paired users. To address the potential packet loss after ARQ, an intelligent packet repair technique is incorporated to recover the dropped packets. A key feature involves storing dropped data packets for subsequent processing before forwarding them to their respective IoT devices (IoDs). The proposed methodology hinges on recognizing that interference within a dropped packet may correspond to a packet retrievable in a forthcoming transmission, facilitating recovery through iterative successive interference cancellation (SIC). Significantly, the scheme enhances data reliability without necessitating an increase in the ARQ retransmission limit, which makes it particularly suited for certain Internet of things (IoT) applications. Empirical results confirm a substantial success rate in recovering dropped packets. Notably, the iterative interference cancellation (IIC) technique demonstrated a noteworthy reduction in the packet drop rate (PDR) from <inline-formula> <tex-math>$10^{-1}$ </tex-math></inline-formula> to <inline-formula> <tex-math>$10^{-3}$ </tex-math></inline-formula>, representing a 100-fold improvement, which implies the successful recovery of 99% of the packets initially dropped in specific scenarios, showcasing the efficacy of the proposed approach.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1070-1090"},"PeriodicalIF":6.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10851350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430533","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 : 2025-01-23DOI: 10.1109/OJCOMS.2025.3533296
Joarder Jafor Sadique;Imtiaz Nasim;Ahmed S. Ibrahim
Low Earth Orbit (LEO) satellites play a crucial role in enhancing global connectivity, serving a complementary solution to existing terrestrial systems. In wireless networks, scheduling is a vital process that allocates time-frequency resources to users for interference management. However, LEO satellite networks face significant challenges in scheduling their links towards ground users due to the satellites’ mobility and overlapping coverage. This paper addresses the dynamic link scheduling problem in LEO satellite networks by considering spatio-temporal correlations introduced by the satellites’ movements. The first step in the proposed solution involves modeling the network over Riemannian manifolds, thanks to their representation as symmetric positive definite matrices. We introduce two machine learning (ML)-based link scheduling techniques that model the dynamic evolution of satellite positions and link conditions over time and space. To accurately predict satellite link states, we present a recurrent neural network (RNN) over Riemannian manifolds, which captures spatio-temporal characteristics over time. Furthermore, we introduce a separate model, the convolutional neural network (CNN) over Riemannian manifolds, which captures geometric relationships between satellites and users by extracting spatial features from the network topology across all links. Simulation results demonstrate that both RNN and CNN over Riemannian manifolds deliver comparable performance to the fractional programming-based link scheduling (FPLinQ) benchmark. Remarkably, unlike other ML-based models that require extensive training data, both models only need 30 training samples to achieve over 99% of the sum rate while maintaining similar computational complexity relative to the benchmark.
{"title":"Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds","authors":"Joarder Jafor Sadique;Imtiaz Nasim;Ahmed S. Ibrahim","doi":"10.1109/OJCOMS.2025.3533296","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3533296","url":null,"abstract":"Low Earth Orbit (LEO) satellites play a crucial role in enhancing global connectivity, serving a complementary solution to existing terrestrial systems. In wireless networks, scheduling is a vital process that allocates time-frequency resources to users for interference management. However, LEO satellite networks face significant challenges in scheduling their links towards ground users due to the satellites’ mobility and overlapping coverage. This paper addresses the dynamic link scheduling problem in LEO satellite networks by considering spatio-temporal correlations introduced by the satellites’ movements. The first step in the proposed solution involves modeling the network over Riemannian manifolds, thanks to their representation as symmetric positive definite matrices. We introduce two machine learning (ML)-based link scheduling techniques that model the dynamic evolution of satellite positions and link conditions over time and space. To accurately predict satellite link states, we present a recurrent neural network (RNN) over Riemannian manifolds, which captures spatio-temporal characteristics over time. Furthermore, we introduce a separate model, the convolutional neural network (CNN) over Riemannian manifolds, which captures geometric relationships between satellites and users by extracting spatial features from the network topology across all links. Simulation results demonstrate that both RNN and CNN over Riemannian manifolds deliver comparable performance to the fractional programming-based link scheduling (FPLinQ) benchmark. Remarkably, unlike other ML-based models that require extensive training data, both models only need 30 training samples to achieve over 99% of the sum rate while maintaining similar computational complexity relative to the benchmark.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"972-985"},"PeriodicalIF":6.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10851312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361077","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 : 2025-01-23DOI: 10.1109/OJCOMS.2025.3533093
Khaled A. Alaghbari;Heng-Siong Lim;Charilaos C. Zarakovitis;N. M. Abdul Latiff;Sharifah Hafizah Syed Ariffin;Su Fong Chien
Computation offloading in Internet of Vehicles (IoV) networks is a promising technology for transferring computation-intensive and latency-sensitive tasks to mobile-edge computing (MEC) or cloud servers. Privacy is an important concern in vehicular networks, as centralized system can compromise it by sharing raw data from MEC servers with cloud servers. A distributed system offers a more attractive solution, allowing each MEC server to process data locally and make offloading decisions without sharing sensitive information. However, without a mechanism to control its load, the cloud server’s computation capacity can become overloaded. In this study, we propose distributed computation offloading systems using reinforcement learning, such as Q-learning, to optimize offloading decisions and balance computation load across the network while minimizing the number of task offloading switches. We introduce both fixed and adaptive low-complexity mechanisms to allocate resources of the cloud server, formulating the reward function of the Q-learning method to achieve efficient offloading decisions. The proposed adaptive approach enables cooperative utilization of cloud resources by multiple agents. A joint optimization framework is established to maximize overall communication and computing resource utilization, where task offloading is performed on a small-time scale at local edge servers, while radio resource slicing is adjusted on a larger time scale at the cloud server. Simulation results using real vehicle tracing datasets demonstrate the effectiveness of the proposed distributed systems in achieving lower computation load costs, offloading switching costs, and reduce latency while increasing cloud server utilization compared to centralized systems.
{"title":"Joint Distributed Computation Offloading and Radio Resource Slicing Based on Reinforcement Learning in Vehicular Networks","authors":"Khaled A. Alaghbari;Heng-Siong Lim;Charilaos C. Zarakovitis;N. M. Abdul Latiff;Sharifah Hafizah Syed Ariffin;Su Fong Chien","doi":"10.1109/OJCOMS.2025.3533093","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3533093","url":null,"abstract":"Computation offloading in Internet of Vehicles (IoV) networks is a promising technology for transferring computation-intensive and latency-sensitive tasks to mobile-edge computing (MEC) or cloud servers. Privacy is an important concern in vehicular networks, as centralized system can compromise it by sharing raw data from MEC servers with cloud servers. A distributed system offers a more attractive solution, allowing each MEC server to process data locally and make offloading decisions without sharing sensitive information. However, without a mechanism to control its load, the cloud server’s computation capacity can become overloaded. In this study, we propose distributed computation offloading systems using reinforcement learning, such as Q-learning, to optimize offloading decisions and balance computation load across the network while minimizing the number of task offloading switches. We introduce both fixed and adaptive low-complexity mechanisms to allocate resources of the cloud server, formulating the reward function of the Q-learning method to achieve efficient offloading decisions. The proposed adaptive approach enables cooperative utilization of cloud resources by multiple agents. A joint optimization framework is established to maximize overall communication and computing resource utilization, where task offloading is performed on a small-time scale at local edge servers, while radio resource slicing is adjusted on a larger time scale at the cloud server. Simulation results using real vehicle tracing datasets demonstrate the effectiveness of the proposed distributed systems in achieving lower computation load costs, offloading switching costs, and reduce latency while increasing cloud server utilization compared to centralized systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1231-1245"},"PeriodicalIF":6.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10851385","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455220","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}
Geofencing technologies have become pivotal in creating virtual boundaries for both real and virtual environments, offering a secure means to control and monitor designated areas. They are now considered essential tools for defining and controlling boundaries across various applications, from aviation safety in drone management to access control within mixed reality platforms like the metaverse. Effective geofencing relies heavily on precise tracking capabilities, a critical component for maintaining the integrity and functionality of these systems. Leveraging the advantages of 5G technology, including its large bandwidth and extensive accessibility, presents a promising solution to enhance geofencing performance. In this paper, we introduce MetaFence: Meta-Reinforcement Learning for Geofencing Enhancement, a novel approach for precise geofencing utilizing indoor 5G small cells, termed “5G Points”, which are optimally deployed using a meta-reinforcement learning (meta-RL) framework. Our proposed meta-RL method addresses the NP-hard problem of determining an optimal placement of 5G Points to minimize spatial geometry-induced errors. Moreover, the meta-training approach enables the learned policy to quickly adapt to diverse new environments. We devised a comprehensive test campaign to evaluate the performance of MetaFence. Our results demonstrate that this strategic placement significantly improves tracking accuracy compared to traditional methods. Furthermore, we show that the meta-training strategy enables the learned policy to generalize effectively and perform efficiently when faced with new environments.
{"title":"Harnessing Meta-Reinforcement Learning for Enhanced Tracking in Geofencing Systems","authors":"Alireza Famili;Shihua Sun;Tolga Atalay;Angelos Stavrou","doi":"10.1109/OJCOMS.2025.3531318","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3531318","url":null,"abstract":"Geofencing technologies have become pivotal in creating virtual boundaries for both real and virtual environments, offering a secure means to control and monitor designated areas. They are now considered essential tools for defining and controlling boundaries across various applications, from aviation safety in drone management to access control within mixed reality platforms like the metaverse. Effective geofencing relies heavily on precise tracking capabilities, a critical component for maintaining the integrity and functionality of these systems. Leveraging the advantages of 5G technology, including its large bandwidth and extensive accessibility, presents a promising solution to enhance geofencing performance. In this paper, we introduce MetaFence: Meta-Reinforcement Learning for Geofencing Enhancement, a novel approach for precise geofencing utilizing indoor 5G small cells, termed “5G Points”, which are optimally deployed using a meta-reinforcement learning (meta-RL) framework. Our proposed meta-RL method addresses the NP-hard problem of determining an optimal placement of 5G Points to minimize spatial geometry-induced errors. Moreover, the meta-training approach enables the learned policy to quickly adapt to diverse new environments. We devised a comprehensive test campaign to evaluate the performance of MetaFence. Our results demonstrate that this strategic placement significantly improves tracking accuracy compared to traditional methods. Furthermore, we show that the meta-training strategy enables the learned policy to generalize effectively and perform efficiently when faced with new environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"944-960"},"PeriodicalIF":6.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10845873","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361063","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 : 2025-01-16DOI: 10.1109/OJCOMS.2025.3530094
Nicolò Longhi;Lorenzo Mario Amorosa;Sara Cavallero;Enrico Buracchini;Roberto Verdone
In the ever-evolving landscape of industrial connectivity, significant strides have been made in the integration of 5th generation (5G) cellular technology with Industrial Internet of Things (IIoT) systems. At the same time, data-driven analytics has become an effective tool for leveraging information from interconnected industrial devices, enabling organizations to gain valuable insights and make informed decisions. However, among these advancements, the holistic perspective of end-to-end analysis related to their integration remains a critical aspect that has yet to be comprehensively addressed. To this end, we investigate 5G IIoT network architectures that support automated guided vehicles (AGVs) on a factory floor as an illustrative example. In particular, we leverage real sensor data collected by AGVs to estimate their remaining useful life (RUL) using a deep learning (DL)-based pipeline. We conduct an in-depth analysis to assess the compatibility of 5G New Radio infrastructures with the aforementioned case study, focusing on round trip time (RTT) requirements and emphasizing the inter-dependencies between communication network and data-driven application.
{"title":"5G Architectures Enabling Remaining Useful Life Estimation for Industrial IoT: A Holistic Study","authors":"Nicolò Longhi;Lorenzo Mario Amorosa;Sara Cavallero;Enrico Buracchini;Roberto Verdone","doi":"10.1109/OJCOMS.2025.3530094","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3530094","url":null,"abstract":"In the ever-evolving landscape of industrial connectivity, significant strides have been made in the integration of 5th generation (5G) cellular technology with Industrial Internet of Things (IIoT) systems. At the same time, data-driven analytics has become an effective tool for leveraging information from interconnected industrial devices, enabling organizations to gain valuable insights and make informed decisions. However, among these advancements, the holistic perspective of end-to-end analysis related to their integration remains a critical aspect that has yet to be comprehensively addressed. To this end, we investigate 5G IIoT network architectures that support automated guided vehicles (AGVs) on a factory floor as an illustrative example. In particular, we leverage real sensor data collected by AGVs to estimate their remaining useful life (RUL) using a deep learning (DL)-based pipeline. We conduct an in-depth analysis to assess the compatibility of 5G New Radio infrastructures with the aforementioned case study, focusing on round trip time (RTT) requirements and emphasizing the inter-dependencies between communication network and data-driven application.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"1016-1029"},"PeriodicalIF":6.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403866","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 : 2025-01-16DOI: 10.1109/OJCOMS.2025.3529982
Sixi Cheng;Xiang Ling;Lidong Zhu
Increasing the hopping frequency speed and integrating artificial intelligence (AI) technologies are currently two of the most effective strategies for enhancing the anti-jamming performance of frequency hopping (FH) systems. However, due to the complexity of the decision-making process in intelligent agents, the system cannot complete decisions within the intervals between hops in fast frequency hopping (FFH) systems. As a result, there is no existing strategy for directly applying AI technologies to FFH systems. In this work, we introduce the concept of the available frequency set (AFS) and apply deep reinforcement learning (DRL) methods to FFH systems, enabling them to retain their inherent advantages while also gaining adaptability to dynamic environments. Building on this, we propose an improved multi-action deep recurrent Q-network (MA-DRQN) algorithm to determine the AFS for hopping sequence generation. Finally, the proposed method is shown to outperform both traditional FFH systems and advanced intelligent FH systems in handling passive and active jammers. Moreover, the hopping sequences generated based on AFS exhibit strong unpredictability.
{"title":"Deep Reinforcement Learning-Based Anti-Jamming Approach for Fast Frequency Hopping Systems","authors":"Sixi Cheng;Xiang Ling;Lidong Zhu","doi":"10.1109/OJCOMS.2025.3529982","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3529982","url":null,"abstract":"Increasing the hopping frequency speed and integrating artificial intelligence (AI) technologies are currently two of the most effective strategies for enhancing the anti-jamming performance of frequency hopping (FH) systems. However, due to the complexity of the decision-making process in intelligent agents, the system cannot complete decisions within the intervals between hops in fast frequency hopping (FFH) systems. As a result, there is no existing strategy for directly applying AI technologies to FFH systems. In this work, we introduce the concept of the available frequency set (AFS) and apply deep reinforcement learning (DRL) methods to FFH systems, enabling them to retain their inherent advantages while also gaining adaptability to dynamic environments. Building on this, we propose an improved multi-action deep recurrent Q-network (MA-DRQN) algorithm to determine the AFS for hopping sequence generation. Finally, the proposed method is shown to outperform both traditional FFH systems and advanced intelligent FH systems in handling passive and active jammers. Moreover, the hopping sequences generated based on AFS exhibit strong unpredictability.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"961-971"},"PeriodicalIF":6.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843343","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361076","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 : 2025-01-14DOI: 10.1109/OJCOMS.2025.3529717
Rafiq Ahmad Khan;Habib Ullah Khan;Hathal Salamah Alwageed;Hussein Al Hashimi;Ismail Keshta
The advent of Fifth-Generation (5G) networks has introduced significant security challenges due to increased complexity and diverse use cases. Conventional threat models may fall short of addressing these emerging threats effectively. This paper presents a new security mitigation model using artificial neural network (ANN) with interpretive structure modeling (ISM) to improve the 5G network security system. The main goal of this study is to develop a 5G network security mitigation model (5GN-SMM) that leverages the predictive capabilities of ANN and the analysis of ISM to identify and mitigate security threats by providing practices in 5G networks. This model aims to improve the accuracy and effectiveness of security measures by integrating advanced computational practices with systematic modeling. Initially, a systematic evaluation of existing 5G network security threats was conducted to identify gaps and incorporate best practices into the proposed model. In the second phase, an empirical survey was conducted to identify and validate the systematic literature review (SLR) findings. In the third phase, we employed a hybrid approach integrating ANN for real-time threat detection and risk assessment and utilizing ISM to analyze the relationships between security threats and vulnerabilities, creating a structured framework for understanding their interdependencies. A case study was conducted in the last stage to test and evaluate 5GN-SMM. The given article illustrates that the proposed hybrid model of ANN-ISM shows a better understanding and management of the security threats than the conventional techniques. The component of the ANN then comes up with the potential of the security breach with improved accuracy, and the ISM framework helps in understanding the relationship and the priorities of the threats. We identified 15 security threats and 144 practices in 5G networks through SLR and empirical surveys. The identified security threats were then analyzed and categorized into 15 process areas and five levels of 5GN-SMM. The proposed model includes state-of-the-art machine learning with traditional information security paradigms to offer an integrated solution to the emerging complex security issues related to 5G. This approach enhances the capacity to detect threats and contributes to good policy enforcement and other risk-related activities to enhance safer 5G networks.
{"title":"5G Networks Security Mitigation Model: An ANN-ISM Hybrid Approach","authors":"Rafiq Ahmad Khan;Habib Ullah Khan;Hathal Salamah Alwageed;Hussein Al Hashimi;Ismail Keshta","doi":"10.1109/OJCOMS.2025.3529717","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3529717","url":null,"abstract":"The advent of Fifth-Generation (5G) networks has introduced significant security challenges due to increased complexity and diverse use cases. Conventional threat models may fall short of addressing these emerging threats effectively. This paper presents a new security mitigation model using artificial neural network (ANN) with interpretive structure modeling (ISM) to improve the 5G network security system. The main goal of this study is to develop a 5G network security mitigation model (5GN-SMM) that leverages the predictive capabilities of ANN and the analysis of ISM to identify and mitigate security threats by providing practices in 5G networks. This model aims to improve the accuracy and effectiveness of security measures by integrating advanced computational practices with systematic modeling. Initially, a systematic evaluation of existing 5G network security threats was conducted to identify gaps and incorporate best practices into the proposed model. In the second phase, an empirical survey was conducted to identify and validate the systematic literature review (SLR) findings. In the third phase, we employed a hybrid approach integrating ANN for real-time threat detection and risk assessment and utilizing ISM to analyze the relationships between security threats and vulnerabilities, creating a structured framework for understanding their interdependencies. A case study was conducted in the last stage to test and evaluate 5GN-SMM. The given article illustrates that the proposed hybrid model of ANN-ISM shows a better understanding and management of the security threats than the conventional techniques. The component of the ANN then comes up with the potential of the security breach with improved accuracy, and the ISM framework helps in understanding the relationship and the priorities of the threats. We identified 15 security threats and 144 practices in 5G networks through SLR and empirical surveys. The identified security threats were then analyzed and categorized into 15 process areas and five levels of 5GN-SMM. The proposed model includes state-of-the-art machine learning with traditional information security paradigms to offer an integrated solution to the emerging complex security issues related to 5G. This approach enhances the capacity to detect threats and contributes to good policy enforcement and other risk-related activities to enhance safer 5G networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"881-925"},"PeriodicalIF":6.3,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10841375","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105744","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 : 2025-01-13DOI: 10.1109/OJCOMS.2025.3528644
Simona Marinova;Yifang Tian;Alberto Leon-Garcia
Assurance for network slices is a cornerstone for emerging application verticals such as vehicle-to-everything (V2X) and Industry 5.0. To achieve per-slice Service Level Agreement (SLA) assurance, an efficient network slice assurance framework is required. In this paper, we propose an end-to-end (E2E) slice assurance framework that addresses the requirements of assurance use cases. We design and implement the major components for a slice assurance framework for the E2E network: data collection, MLOps, and closed-control loops. We leverage open-source software to build the framework, and we provide experimental evaluations on real network devices and datasets.
{"title":"E2E Network Slice Assurance for B5G/6G: Realizing Data Collection and Management, MLOps, and Closed-Loop Control","authors":"Simona Marinova;Yifang Tian;Alberto Leon-Garcia","doi":"10.1109/OJCOMS.2025.3528644","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3528644","url":null,"abstract":"Assurance for network slices is a cornerstone for emerging application verticals such as vehicle-to-everything (V2X) and Industry 5.0. To achieve per-slice Service Level Agreement (SLA) assurance, an efficient network slice assurance framework is required. In this paper, we propose an end-to-end (E2E) slice assurance framework that addresses the requirements of assurance use cases. We design and implement the major components for a slice assurance framework for the E2E network: data collection, MLOps, and closed-control loops. We leverage open-source software to build the framework, and we provide experimental evaluations on real network devices and datasets.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"759-774"},"PeriodicalIF":6.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184268","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 : 2025-01-13DOI: 10.1109/OJCOMS.2025.3528718
Ashutosh Bhatia;Sainath Bitragunta;Kamlesh Tiwari
Quantum Key Distribution (QKD) provides secure communication by leveraging quantum mechanics, with the BB84 protocol being one of its most widely adopted implementations. However, the classical post-processing steps in BB84, such as sifting, error correction, and key verification, often result in significant communication overhead, limiting its efficiency and scalability. In this work, we propose three key optimizations for BB84: (1) PRNG-based predetermined key bit positioning, which eliminates redundant bit exchanges during sifting, (2) hash-based subsequence comparison, enabling lightweight and efficient key verification, and (3) adaptive basis reconciliation, which minimizes the communication costs associated with basis matching. The proposed optimizations achieve a 50% reduction in communication overhead for large key sizes compared to traditional QKD protocols, as demonstrated through rigorous performance analysis. While the focus of this work is on the BB84 protocol, these optimizations are also directly applicable to a broader class of Discrete-Variable QKD (DV-QKD) protocols, such as six-state, B92, and E91, which share a fundamentally similar post-processing structure. This generality highlights the modularity and adaptability of the proposed methods across diverse QKD implementations. The proposed optimizations enhance post-processing efficiency and scalability, enabling practical deployment in bandwidth-limited environments like IoT networks, secure financial systems, and defense communications, thereby supporting broader adoption of quantum communication systems.
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