The Direction Finding (DF) feature in Bluetooth Low Energy (BLE) 5.1 has made the technology an increasingly attractive choice for Indoor Positioning Systems (IPS), as it allows devices to estimate the Angle of Arrival (AoA) of incoming signals. This capability enables high-accuracy, low-power estimation of transmitter positions. Despite the availability of some BLE-based datasets in the literature, this work presents a more comprehensive BLE 5.1 dataset containing measurements of AoA, Received Signal Strength Indicator (RSSI), and In-phase and Quadrature (IQ) samples under several different setups. More specifically, data were collected in a room ${8}{mathrm { m}} times {10}{mathrm { m}}$ with obstacles, under calibration, static, and mobility scenarios, with the aid of seven anchors (assessed at two different heights and orientations) and four transmitters. Furthermore, the transmitters were installed inside Personal Protective Equipment (PPE), making the dataset suitable for complex safety-related applications. This paper also analyzes packet loss, AoA accuracy, RSSI, and positioning accuracy obtained from receivers by two different manufacturers, highlighting performance differences within the same environment. Finally, the collected data are made publicly available to the research community for the development and evaluation of both positioning and angle estimation algorithms.
{"title":"A Comprehensive BLE-Compliant Dataset for Indoor Positioning Systems","authors":"Andrey Fabris;Ohara Kerusauskas Rayel;João Luiz Rebelatto;Guilherme Luiz Moritz;Marcos Eduardo Pivaro Monteiro;Guilherme De Santi Peron","doi":"10.1109/OJCOMS.2025.3640550","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3640550","url":null,"abstract":"The Direction Finding (DF) feature in Bluetooth Low Energy (BLE) 5.1 has made the technology an increasingly attractive choice for Indoor Positioning Systems (IPS), as it allows devices to estimate the Angle of Arrival (AoA) of incoming signals. This capability enables high-accuracy, low-power estimation of transmitter positions. Despite the availability of some BLE-based datasets in the literature, this work presents a more comprehensive BLE 5.1 dataset containing measurements of AoA, Received Signal Strength Indicator (RSSI), and In-phase and Quadrature (IQ) samples under several different setups. More specifically, data were collected in a room <inline-formula> <tex-math>${8}{mathrm { m}} times {10}{mathrm { m}}$ </tex-math></inline-formula> with obstacles, under calibration, static, and mobility scenarios, with the aid of seven anchors (assessed at two different heights and orientations) and four transmitters. Furthermore, the transmitters were installed inside Personal Protective Equipment (PPE), making the dataset suitable for complex safety-related applications. This paper also analyzes packet loss, AoA accuracy, RSSI, and positioning accuracy obtained from receivers by two different manufacturers, highlighting performance differences within the same environment. Finally, the collected data are made publicly available to the research community for the development and evaluation of both positioning and angle estimation algorithms.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"10171-10190"},"PeriodicalIF":6.3,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11277305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729434","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 : 2025-12-02DOI: 10.1109/OJCOMS.2025.3639445
Fan Yu;Mingqi Guo;Guangzheng Jing;Yixiao Tong;José Rodríguez-Piñeiro;Xuefeng Yin
Integrated Sensing and Communication (ISAC) technology not only enables information transmission but also delivers accurate localization and environmental sensing capabilities. Existing spherical-wavefront model based Methods face two primary drawbacks. First, their underlying assumptions are often invalid in complex real-world scenarios, particularly under Non-Line-of-Sight (NLoS) conditions. This leads to inaccurate channel parameter estimation and subsequent errors in localization and environmental sensing. Second, they necessitate costly and complex MIMO systems, rendering them impractical for many deployments constrained by budget or hardware. Our proposed method directly addresses these limitations. Our proposed method directly addresses these limitations. It incorporates novel two-step suppression framework of clusters acquisition and preferred expected cluster selection to effectively eliminates the errors caused by non-conforming MPCs, thereby improving the accuracy of both localization and environmental sensing. Crucially, the system only requires a multi-antenna setup at one end, significantly reducing complexity and cost. Additionally, an iterative procedure is embedded in the method to enhance computational efficiency. Through indoor simulations and measurements in various LoS and NLoS scenarios, our method consistently demonstrates higher target localization accuracy and better environmental sensing than comparable benchmarks. This confirms that our solution is not only more robust and widely applicable but also maintains a high level of computational efficiency.
{"title":"Incorporating Clustering for Joint 3-D Localization and Environmental Sensing Method Using the Spherical-Wavefront Parametric Model","authors":"Fan Yu;Mingqi Guo;Guangzheng Jing;Yixiao Tong;José Rodríguez-Piñeiro;Xuefeng Yin","doi":"10.1109/OJCOMS.2025.3639445","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3639445","url":null,"abstract":"Integrated Sensing and Communication (ISAC) technology not only enables information transmission but also delivers accurate localization and environmental sensing capabilities. Existing spherical-wavefront model based Methods face two primary drawbacks. First, their underlying assumptions are often invalid in complex real-world scenarios, particularly under Non-Line-of-Sight (NLoS) conditions. This leads to inaccurate channel parameter estimation and subsequent errors in localization and environmental sensing. Second, they necessitate costly and complex MIMO systems, rendering them impractical for many deployments constrained by budget or hardware. Our proposed method directly addresses these limitations. Our proposed method directly addresses these limitations. It incorporates novel two-step suppression framework of clusters acquisition and preferred expected cluster selection to effectively eliminates the errors caused by non-conforming MPCs, thereby improving the accuracy of both localization and environmental sensing. Crucially, the system only requires a multi-antenna setup at one end, significantly reducing complexity and cost. Additionally, an iterative procedure is embedded in the method to enhance computational efficiency. Through indoor simulations and measurements in various LoS and NLoS scenarios, our method consistently demonstrates higher target localization accuracy and better environmental sensing than comparable benchmarks. This confirms that our solution is not only more robust and widely applicable but also maintains a high level of computational efficiency.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"10042-10060"},"PeriodicalIF":6.3,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271784","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729366","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 rapid expansion of smart applications has accelerated the growth of the Internet of Things across various domains, starting from home automation to industrial systems and healthcare. Internet of Things devices are typically resource-constrained and rely on an efficient routing protocol—the IPv6 routing protocol for low-power and lossy networks, which organizes nodes into tree-like topologies, where parent selection plays a pivotal role in determining both network reliability and energy efficiency. Conventional parent selection depends on static metrics, and the centralized machine learning struggles to adapt in real time, faces privacy concerns, and fails to cope with dynamic networks. In this proposed work, a new approach is introduced using a collaborative learning method that allows each device to locally predict the energy usage of potential parent nodes. This enables predicted and more adaptable routing decisions without sharing sensitive data. The proposed system improves energy efficiency by 27.68 percent compared to standard methods while maintaining a prediction accuracy of 99.23 percent. The total energy consumption was reduced to 433.946 millijoules, and there were also corresponding drops in average power and current consumption. As a result, the device is expected to last 54.1 days, which is 38% longer than the baseline methods—objective function zero—and 26.4% longer than the minimum rank with hysteresis objective function. Overall, the experimental results demonstrate that this learning-based routing approach reduces communication overhead and improves the ability of the network to adapt to real-world changes.
{"title":"Federated Learning-Based Parent Selection in Low Power and Lossy Networks to Enhance Energy Efficiency","authors":"Rakseda Keerthi Alagarsamy Sangeetha;Senthilkumar Mathi;Akibu Mahmoud Abdullahi;Ganesh Neelakanta Iyer","doi":"10.1109/OJCOMS.2025.3639481","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3639481","url":null,"abstract":"The rapid expansion of smart applications has accelerated the growth of the Internet of Things across various domains, starting from home automation to industrial systems and healthcare. Internet of Things devices are typically resource-constrained and rely on an efficient routing protocol—the IPv6 routing protocol for low-power and lossy networks, which organizes nodes into tree-like topologies, where parent selection plays a pivotal role in determining both network reliability and energy efficiency. Conventional parent selection depends on static metrics, and the centralized machine learning struggles to adapt in real time, faces privacy concerns, and fails to cope with dynamic networks. In this proposed work, a new approach is introduced using a collaborative learning method that allows each device to locally predict the energy usage of potential parent nodes. This enables predicted and more adaptable routing decisions without sharing sensitive data. The proposed system improves energy efficiency by 27.68 percent compared to standard methods while maintaining a prediction accuracy of 99.23 percent. The total energy consumption was reduced to 433.946 millijoules, and there were also corresponding drops in average power and current consumption. As a result, the device is expected to last 54.1 days, which is 38% longer than the baseline methods—objective function zero—and 26.4% longer than the minimum rank with hysteresis objective function. Overall, the experimental results demonstrate that this learning-based routing approach reduces communication overhead and improves the ability of the network to adapt to real-world changes.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"10123-10138"},"PeriodicalIF":6.3,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271870","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729315","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 : 2025-12-02DOI: 10.1109/OJCOMS.2025.3639435
Muhammad Afaq
Software-Defined Networks (SDNs) with their centralized control system and enhanced programmability requires a sophisticated approach to predict link states in complex network topologies. While predictive performance of traditional machine learning (ML) models remains high on static data, they struggle under dynamic and imbalanced network behavior. Although Quantum Machine Learning (QML) offers a promising solution by leveraging quantum-enhanced kernels to discover non-linear patterns, current quantum hardware constraints limit its standalone applicability. This paper presents an extended hybrid classical-quantum learning framework for SDN link state prediction under both benign (stable) and attack (compromised) conditions, integrating classical preprocessing with quantum kernel embedding via the ZZFeatureMap. A tunable parameter $alpha in [{0,1}]$ enables dynamic interpolation between classical Radial Basis Function (RBF) and quantum kernels. On a stratified 5,000-sample subset of the InSDN dataset, which contains both normal and attack traffic flows, the hybrid Quantum Support Vector Machine (QSVM) achieves 85% accuracy, 0.92 ROC–AUC, and 0.73 Average Precision (AP). Scalability experiments on 20,000 samples confirm stable performance using Nyström-approximated kernels. Comparative evaluations with existing core classical, gradient boosting, and deep learning approaches highlight hybrid QSVM’s tunable expressivity, controlled computational scaling, and robustness to data distribution shifts, demonstrating its potential for future quantum-enabled SDN analytics.
{"title":"Hybrid Classical–Quantum Kernel Learning for Scalable and Secure Link State Prediction in Software-Defined Networks","authors":"Muhammad Afaq","doi":"10.1109/OJCOMS.2025.3639435","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3639435","url":null,"abstract":"Software-Defined Networks (SDNs) with their centralized control system and enhanced programmability requires a sophisticated approach to predict link states in complex network topologies. While predictive performance of traditional machine learning (ML) models remains high on static data, they struggle under dynamic and imbalanced network behavior. Although Quantum Machine Learning (QML) offers a promising solution by leveraging quantum-enhanced kernels to discover non-linear patterns, current quantum hardware constraints limit its standalone applicability. This paper presents an extended hybrid classical-quantum learning framework for SDN link state prediction under both benign (stable) and attack (compromised) conditions, integrating classical preprocessing with quantum kernel embedding via the ZZFeatureMap. A tunable parameter <inline-formula> <tex-math>$alpha in [{0,1}]$ </tex-math></inline-formula> enables dynamic interpolation between classical Radial Basis Function (RBF) and quantum kernels. On a stratified 5,000-sample subset of the InSDN dataset, which contains both normal and attack traffic flows, the hybrid Quantum Support Vector Machine (QSVM) achieves 85% accuracy, 0.92 ROC–AUC, and 0.73 Average Precision (AP). Scalability experiments on 20,000 samples confirm stable performance using Nyström-approximated kernels. Comparative evaluations with existing core classical, gradient boosting, and deep learning approaches highlight hybrid QSVM’s tunable expressivity, controlled computational scaling, and robustness to data distribution shifts, demonstrating its potential for future quantum-enabled SDN analytics.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"10094-10110"},"PeriodicalIF":6.3,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271813","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729278","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 : 2025-12-01DOI: 10.1109/OJCOMS.2025.3638874
Masoud Sadeghian;Angel Lozano;Gabor Fodor
This paper investigates reconfigurable intelligent surface (RIS)-assisted wireless communications under a two-timescale architecture, in which the RIS phase shifts are optimized from long-term channel statistics, eliminating per-element training and thereby slashing channel estimation overhead. It is shown that, while the power captured by the RIS scales linearly with the number of its elements, the two-timescale beamforming gain upon re-radiation towards the receiver saturates rapidly as the number of RIS elements increases, for a broad class of power angular spectra (PAS). The saturation ceiling depends on the PAS angular decay, which governs how quickly inter-element spatial correlation vanishes. Steeper decays yield stronger correlations and, hence, a higher ceiling. The implications of this saturation on the effectiveness of two-timescale RIS-assisted communications are discussed.
{"title":"Saturation in Two-Timescale RIS Beamforming","authors":"Masoud Sadeghian;Angel Lozano;Gabor Fodor","doi":"10.1109/OJCOMS.2025.3638874","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3638874","url":null,"abstract":"This paper investigates reconfigurable intelligent surface (RIS)-assisted wireless communications under a two-timescale architecture, in which the RIS phase shifts are optimized from long-term channel statistics, eliminating per-element training and thereby slashing channel estimation overhead. It is shown that, while the power captured by the RIS scales linearly with the number of its elements, the two-timescale beamforming gain upon re-radiation towards the receiver saturates rapidly as the number of RIS elements increases, for a broad class of power angular spectra (PAS). The saturation ceiling depends on the PAS angular decay, which governs how quickly inter-element spatial correlation vanishes. Steeper decays yield stronger correlations and, hence, a higher ceiling. The implications of this saturation on the effectiveness of two-timescale RIS-assisted communications are discussed.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"10111-10122"},"PeriodicalIF":6.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271630","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729383","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 : 2025-12-01DOI: 10.1109/OJCOMS.2025.3638985
Huancheng Hu;Ziyun Li;Christian Doerr
Constantly evolving botnets pose high risks to cyber infrastructure, resulting in evasion of defence system, disruption of critical services and catastrophic financial damage. Therefore, it is crucial for effective botnet traffic detection to discover and identify new emerging threat. Although recent deep learning-based methods performed well at classifying known botnet traffic but failed to handle unseen threats. This deficiency enables novel botnets to remain undetected, evade existing defenses, and inflict severe damage in real-world scenarios. To address these gaps, we propose a unified framework, Botnet $N$ ovel-class $C$ lassification (BoNC), which multi-classifies unlabelled encrypted botnet traffic by leveraging known classes to establish decision boundaries for previously unseen ones. We evaluate BoNC on three public encrypted traffic datasets with diverse labelled and unlabelled ratio configurations. BoNC consistently outperforms prior state-of-the-art methods and accurately classifies known botnets and discovers unseen botnet variants in open-world scenarios.
不断发展的僵尸网络给网络基础设施带来了很高的风险，导致防御系统的规避、关键服务的中断和灾难性的经济损失。因此，有效的僵尸网络流量检测发现和识别新的威胁是至关重要的。尽管最近基于深度学习的方法在分类已知的僵尸网络流量方面表现良好，但无法处理看不见的威胁。这一缺陷使新型僵尸网络能够不被发现，逃避现有防御，并在现实世界中造成严重破坏。为了解决这些差距，我们提出了一个统一的框架，Botnet $N$ over -class $C$分类（BoNC），它通过利用已知类为以前未见过的类建立决策边界来对未标记的加密僵尸网络流量进行多分类。我们在三个具有不同标记和未标记比率配置的公共加密流量数据集上评估BoNC。BoNC始终优于先前最先进的方法，能够准确地对已知的僵尸网络进行分类，并在开放世界场景中发现未见过的僵尸网络变体。
{"title":"BoNC: Discovering and Classifying Novel Encrypted Botnet Traffic","authors":"Huancheng Hu;Ziyun Li;Christian Doerr","doi":"10.1109/OJCOMS.2025.3638985","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3638985","url":null,"abstract":"Constantly evolving botnets pose high risks to cyber infrastructure, resulting in evasion of defence system, disruption of critical services and catastrophic financial damage. Therefore, it is crucial for effective botnet traffic detection to discover and identify new emerging threat. Although recent deep learning-based methods performed well at classifying known botnet traffic but failed to handle unseen threats. This deficiency enables novel botnets to remain undetected, evade existing defenses, and inflict severe damage in real-world scenarios. To address these gaps, we propose a unified framework, Botnet <inline-formula> <tex-math>$N$ </tex-math></inline-formula>ovel-class <inline-formula> <tex-math>$C$ </tex-math></inline-formula>lassification (BoNC), which multi-classifies unlabelled encrypted botnet traffic by leveraging known classes to establish decision boundaries for previously unseen ones. We evaluate BoNC on three public encrypted traffic datasets with diverse labelled and unlabelled ratio configurations. BoNC consistently outperforms prior state-of-the-art methods and accurately classifies known botnets and discovers unseen botnet variants in open-world scenarios.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"10845-10860"},"PeriodicalIF":6.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929276","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 : 2025-12-01DOI: 10.1109/OJCOMS.2025.3639000
Marc Jean;Zaheen E. Muktadi Syed;Mustafa Tarik Sanic;Murat Yuksel;Elizabeth S. Bentley
This paper presents a novel framework that applies deep Q-learning (DQN) with transfer learning to millimeter-wave (mmWave) beam selection using a software-defined radio (SDR) testbed. We implement a three-thread software architecture integrating GNU Radio, ZeroMQ, and Python-based APIs to control beam steering. The testbed attains sub-microsecond timescales to perform beamforming and establish a control loop between SDR software and the underlying mmWave phased array. We design a DQN architecture to collect received signal strength (RSS) values and perform angle-of-arrival (AoA) detection without any need for phase detection or multi-element antenna. The DQN agent is trained using a 3-layer neural network and is rewarded based on RSS improvement. We also design a transfer learning framework by reloading and averaging pre-trained DQN weights across five distinct environmental scenarios. Our results demonstrate that the agent converges more quickly and achieves lower AoA detection error when using prior knowledge from transfer learning. They also reveal that categorizing the training scenarios based on line-of-sight (LoS) vs. non-LoS significantly improves the efficacy of the transfer learning for AoA detection.
{"title":"DQN With Transfer Learning for Sub-Microsecond AoA Detection in a Millimeter-Wave SDR Testbed","authors":"Marc Jean;Zaheen E. Muktadi Syed;Mustafa Tarik Sanic;Murat Yuksel;Elizabeth S. Bentley","doi":"10.1109/OJCOMS.2025.3639000","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3639000","url":null,"abstract":"This paper presents a novel framework that applies deep Q-learning (DQN) with transfer learning to millimeter-wave (mmWave) beam selection using a software-defined radio (SDR) testbed. We implement a three-thread software architecture integrating GNU Radio, ZeroMQ, and Python-based APIs to control beam steering. The testbed attains sub-microsecond timescales to perform beamforming and establish a control loop between SDR software and the underlying mmWave phased array. We design a DQN architecture to collect received signal strength (RSS) values and perform angle-of-arrival (AoA) detection without any need for phase detection or multi-element antenna. The DQN agent is trained using a 3-layer neural network and is rewarded based on RSS improvement. We also design a transfer learning framework by reloading and averaging pre-trained DQN weights across five distinct environmental scenarios. Our results demonstrate that the agent converges more quickly and achieves lower AoA detection error when using prior knowledge from transfer learning. They also reveal that categorizing the training scenarios based on line-of-sight (LoS) vs. non-LoS significantly improves the efficacy of the transfer learning for AoA detection.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"10079-10093"},"PeriodicalIF":6.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271585","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729346","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 : 2025-12-01DOI: 10.1109/OJCOMS.2025.3638890
Enes Koktas;Recep A. Tasci;Ibrahim Yildirim;Ertugrul Basar
Space exploration has witnessed a steady increase since the 1960s, with Mars playing a significant role in our quest for further knowledge. As the ambition to colonize Mars becomes a reality through the collaboration of private companies and space agencies, the need for reliable and robust communication infrastructures in the Martian environment becomes paramount. In this context, reconfigurable intelligent surface (RIS)-empowered communication emerges as a promising technology to enhance the coverage due to lack of multipath components in line-of-sight (LOS) dominated Martian environments. By considering various Martian scenarios such as canyons, craters, mountains, and plateaus, this article explores the potential of RISs in increasing the coverage in Martian environments. The article also provides an overview of RIS-assisted localization in both LOS and non-line-of-sight (NLOS) scenarios, presenting a general framework for accurate user angle estimation in challenging Martian conditions. The findings and presented framework of this article provide a promising research direction for integrating RISs in deep space communication as well as paving the way for future improvements in interplanetary communication networks.
{"title":"Reconfigurable Intelligent Surface Deployments for Mars: Communication and Localization Across Diverse Terrains","authors":"Enes Koktas;Recep A. Tasci;Ibrahim Yildirim;Ertugrul Basar","doi":"10.1109/OJCOMS.2025.3638890","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3638890","url":null,"abstract":"Space exploration has witnessed a steady increase since the 1960s, with Mars playing a significant role in our quest for further knowledge. As the ambition to colonize Mars becomes a reality through the collaboration of private companies and space agencies, the need for reliable and robust communication infrastructures in the Martian environment becomes paramount. In this context, reconfigurable intelligent surface (RIS)-empowered communication emerges as a promising technology to enhance the coverage due to lack of multipath components in line-of-sight (LOS) dominated Martian environments. By considering various Martian scenarios such as canyons, craters, mountains, and plateaus, this article explores the potential of RISs in increasing the coverage in Martian environments. The article also provides an overview of RIS-assisted localization in both LOS and non-line-of-sight (NLOS) scenarios, presenting a general framework for accurate user angle estimation in challenging Martian conditions. The findings and presented framework of this article provide a promising research direction for integrating RISs in deep space communication as well as paving the way for future improvements in interplanetary communication networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"10139-10149"},"PeriodicalIF":6.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729367","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 : 2025-12-01DOI: 10.1109/OJCOMS.2025.3638849
Luhui Wang;Xiao Ma;Jianyong Fan;Fulin Li;Ying Li
Satellite-Terrestrial Integrated Network (STIN), with its wider coverage and more flexible network structure over its counterparts, is a crucial application scenario for the Sixth Generation (6G) mobile communications. However, the depolyment of network infrastructure and verification of related technologies are challenging. Therefore, a novel test framework is urgently needed to advance research outputs on STINs. Motivated by the recent proliferation of Digital Twin (DT) technology, the DT empowered STIN platform has been considered as a key enabling technology for the future 6G-based Internet of Everything (IoE). Nevertheless, there is still a lack of effective cases of applying DT technology in STINs. In this work, we discuss the importance of DT technology for the research and development of STINs. The prototype of the Multilayer Modular Architecture (MMA) for the DT in STIN is proposed. Through a hierarchical and modular design, the model is made more versatile and extensible. Meanwhile, aiming at the difficulties in multiple access caused by the high dynamics and large-scale delays in STINs, we propose a lightweight multiple-access protocol and take it as an application case of MMA. Furthermore, the test platform and the deployment scheme that we suggest are presented. Finally, the test results show that MMA can correctly verify the working logic of the proposed protocol in STIN, which provides an important reference for future research in IoE technology.
{"title":"Digital Twin Architecture Design and Testbed Deployment for Satellite-Terrestrial Integrated Networks","authors":"Luhui Wang;Xiao Ma;Jianyong Fan;Fulin Li;Ying Li","doi":"10.1109/OJCOMS.2025.3638849","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3638849","url":null,"abstract":"Satellite-Terrestrial Integrated Network (STIN), with its wider coverage and more flexible network structure over its counterparts, is a crucial application scenario for the Sixth Generation (6G) mobile communications. However, the depolyment of network infrastructure and verification of related technologies are challenging. Therefore, a novel test framework is urgently needed to advance research outputs on STINs. Motivated by the recent proliferation of Digital Twin (DT) technology, the DT empowered STIN platform has been considered as a key enabling technology for the future 6G-based Internet of Everything (IoE). Nevertheless, there is still a lack of effective cases of applying DT technology in STINs. In this work, we discuss the importance of DT technology for the research and development of STINs. The prototype of the Multilayer Modular Architecture (MMA) for the DT in STIN is proposed. Through a hierarchical and modular design, the model is made more versatile and extensible. Meanwhile, aiming at the difficulties in multiple access caused by the high dynamics and large-scale delays in STINs, we propose a lightweight multiple-access protocol and take it as an application case of MMA. Furthermore, the test platform and the deployment scheme that we suggest are presented. Finally, the test results show that MMA can correctly verify the working logic of the proposed protocol in STIN, which provides an important reference for future research in IoE technology.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"153-168"},"PeriodicalIF":6.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929544","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 this paper, we propose a coexistence scheme based on orthogonal frequency division multiplexing (OFDM) and affine frequency division multiplexing (AFDM) to enable efficient joint sensing and communication (JSAC) under different channel conditions. Specifically, the distinct signal representations of both waveforms in the frequency and affine domains are exploited to ensure, by construction, the orthogonality and flexibility of the proposed mechanism within a shared resource grid. Furthermore, a novel pilot design is introduced to enable joint channel estimation for both communication and sensing tasks in a unified and coherent manner, using a single pilot structure for both functionalities. To further enhance the scheme’s adaptability, the proposed approach dynamically adapts to channel mobility and time variation, ensuring seamless and stable operation in JSAC networks across practical scenarios. Numerical results validate the effectiveness of the design, demonstrating accurate, low-complexity radar parameter estimation while simultaneously maintaining high data rates, and reducing the peak-to-average-power ratio (PAPR) when compared with the conventional OFDM and AFDM baselines. Taken together, these elements indicate that the proposed coexistence strategy achieves sensing capability and communication efficiency within a single design while preserving the intended orthogonality and flexibility.
{"title":"On the Orthogonal Coexistence of AFDM and OFDM for Joint Sensing and Communication","authors":"Rania Yasmine Bir;Ayoub Ammar Boudjelal;Hüseyin Arslan","doi":"10.1109/OJCOMS.2025.3638461","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3638461","url":null,"abstract":"In this paper, we propose a coexistence scheme based on orthogonal frequency division multiplexing (OFDM) and affine frequency division multiplexing (AFDM) to enable efficient joint sensing and communication (JSAC) under different channel conditions. Specifically, the distinct signal representations of both waveforms in the frequency and affine domains are exploited to ensure, by construction, the orthogonality and flexibility of the proposed mechanism within a shared resource grid. Furthermore, a novel pilot design is introduced to enable joint channel estimation for both communication and sensing tasks in a unified and coherent manner, using a single pilot structure for both functionalities. To further enhance the scheme’s adaptability, the proposed approach dynamically adapts to channel mobility and time variation, ensuring seamless and stable operation in JSAC networks across practical scenarios. Numerical results validate the effectiveness of the design, demonstrating accurate, low-complexity radar parameter estimation while simultaneously maintaining high data rates, and reducing the peak-to-average-power ratio (PAPR) when compared with the conventional OFDM and AFDM baselines. Taken together, these elements indicate that the proposed coexistence strategy achieves sensing capability and communication efficiency within a single design while preserving the intended orthogonality and flexibility.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"10010-10022"},"PeriodicalIF":6.3,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729372","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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