Pub Date : 2026-01-23DOI: 10.1109/OJCOMS.2026.3656868
Ali Rasteh;Sundeep Rangan
Signal detection in environments with unknown signal bandwidth and time intervals is a fundamental problem in adversarial and spectrum-sharing scenarios. This paper addresses the problem of detecting signals occupying unknown degrees of freedom from non-coherent power measurements, where the signal is constrained to an interval in one dimension or a hyper-cube in multiple dimensions. A Generalized Likelihood Ratio Test (GLRT) is derived, resulting in a straightforward metric involving normalized average signal energy for each candidate signal set. We present bounds on false alarm and missed detection probabilities, demonstrating their dependence on signal-to-noise ratios (SNRs) and signal set sizes. To overcome the inherent computational complexity of exhaustive searches, we propose a computationally efficient binary search method, reducing the complexity from $O(N^{2})$ to $O(N)$ for one-dimensional cases. Simulations indicate that the method maintains performance near exhaustive searches and achieves asymptotic consistency, with interval-of-overlap converging to one under constant SNR as measurement size increases. The simulation studies also demonstrate superior performance and reduced complexity compared to contemporary neural network-based approaches, specifically outperforming custom-trained U-Net models in spectrum detection tasks.
{"title":"Computationally Efficient Signal Detection With Unknown Bandwidths","authors":"Ali Rasteh;Sundeep Rangan","doi":"10.1109/OJCOMS.2026.3656868","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3656868","url":null,"abstract":"Signal detection in environments with unknown signal bandwidth and time intervals is a fundamental problem in adversarial and spectrum-sharing scenarios. This paper addresses the problem of detecting signals occupying unknown degrees of freedom from non-coherent power measurements, where the signal is constrained to an interval in one dimension or a hyper-cube in multiple dimensions. A Generalized Likelihood Ratio Test (GLRT) is derived, resulting in a straightforward metric involving normalized average signal energy for each candidate signal set. We present bounds on false alarm and missed detection probabilities, demonstrating their dependence on signal-to-noise ratios (SNRs) and signal set sizes. To overcome the inherent computational complexity of exhaustive searches, we propose a computationally efficient binary search method, reducing the complexity from <inline-formula> <tex-math>$O(N^{2})$ </tex-math></inline-formula> to <inline-formula> <tex-math>$O(N)$ </tex-math></inline-formula> for one-dimensional cases. Simulations indicate that the method maintains performance near exhaustive searches and achieves asymptotic consistency, with interval-of-overlap converging to one under constant SNR as measurement size increases. The simulation studies also demonstrate superior performance and reduced complexity compared to contemporary neural network-based approaches, specifically outperforming custom-trained U-Net models in spectrum detection tasks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"997-1018"},"PeriodicalIF":6.3,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11362363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082275","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 : 2026-01-23DOI: 10.1109/OJCOMS.2026.3657332
Farid Yuli Martin Adiyatma;Panarat Cherntanomwong;Dwi Joko Suroso
Received Signal Strength (RSS)-based Wi-Fi localization offers a cost-effective solution for multi-floor indoor location estimation. However, its accuracy is often degraded by signal fading, multipath propagation, and device heterogeneity, posing major challenges to reliable localization. Recent studies have increasingly employed deep neural networks due to their ability to extract meaningful patterns from RSS data; however, these models require substantial computational resources and extensive parameter tuning, which limits their adaptability across diverse dynamic environments. To address these limitations, we propose DELLoc-RT, a localization framework integrating Dynamic Ensemble Learning (DELLoc) with RSS Transformation (RT) for accurate, efficient, and adaptable multi-floor indoor localization. The RT module applies Sigmoid-scaled normalization and confidence weighting to convert RSS values into compact, learnable features. DELLoc employs multiple base learners optimized via the Tree-structured Parzen Estimator with a pruning strategy (TPE-PS) that accelerates convergence by focusing on promising configurations. Additionally, Iterative Ensemble Optimization with Stepwise Selection (IEO-SS) selects complementary learners to enhance overall performance. Experimental results demonstrate that DELLoc-RT achieves floor classification accuracies of 93.32%, 94.38%, and 94.02% on the UJIIndoorLoc, UTSIndoorLoc, and Tampere datasets, respectively, with mean Euclidean errors (MEE) of 10.63 m, 7.87 m, and 8.19 m. These results highlight the model’s strong adaptability across diverse datasets. Evaluation on a self-constructed dataset further confirms that DELLoc-RT delivers high accuracy and efficiency while substantially reducing the need for manual tuning, enabling rapid deployment in practical scenarios.
{"title":"Dynamic Ensemble Learning With Received Signal Strength Transformation for Robust Multi-Floor Wi-Fi Indoor Localization","authors":"Farid Yuli Martin Adiyatma;Panarat Cherntanomwong;Dwi Joko Suroso","doi":"10.1109/OJCOMS.2026.3657332","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3657332","url":null,"abstract":"Received Signal Strength (RSS)-based Wi-Fi localization offers a cost-effective solution for multi-floor indoor location estimation. However, its accuracy is often degraded by signal fading, multipath propagation, and device heterogeneity, posing major challenges to reliable localization. Recent studies have increasingly employed deep neural networks due to their ability to extract meaningful patterns from RSS data; however, these models require substantial computational resources and extensive parameter tuning, which limits their adaptability across diverse dynamic environments. To address these limitations, we propose DELLoc-RT, a localization framework integrating Dynamic Ensemble Learning (DELLoc) with RSS Transformation (RT) for accurate, efficient, and adaptable multi-floor indoor localization. The RT module applies Sigmoid-scaled normalization and confidence weighting to convert RSS values into compact, learnable features. DELLoc employs multiple base learners optimized via the Tree-structured Parzen Estimator with a pruning strategy (TPE-PS) that accelerates convergence by focusing on promising configurations. Additionally, Iterative Ensemble Optimization with Stepwise Selection (IEO-SS) selects complementary learners to enhance overall performance. Experimental results demonstrate that DELLoc-RT achieves floor classification accuracies of 93.32%, 94.38%, and 94.02% on the UJIIndoorLoc, UTSIndoorLoc, and Tampere datasets, respectively, with mean Euclidean errors (MEE) of 10.63 m, 7.87 m, and 8.19 m. These results highlight the model’s strong adaptability across diverse datasets. Evaluation on a self-constructed dataset further confirms that DELLoc-RT delivers high accuracy and efficiency while substantially reducing the need for manual tuning, enabling rapid deployment in practical scenarios.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"978-996"},"PeriodicalIF":6.3,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11362365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082276","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 : 2026-01-19DOI: 10.1109/OJCOMS.2026.3655170
José David Vega-Sánchez;Victor Hugo Garzón Pacheco;Nathaly Verónica Orozco Garzón;Daniel A. Riofrío Almeida;Diana Pamela Moya Osorio
This paper examines the secrecy outage probability (SOP) in Fluid Reconfigurable Intelligent Surfaces (FRIS) and contrasts their performance against two alternative RIS architectures: a traditional planar RIS and a compact RIS layout. To characterize the end-to-end FRIS channel, a maximum likelihood estimation (MLE) approach is introduced, while a Q-learning algorithm is employed to adaptively select the spatial positions of FRIS elements. Numerical evaluations show that optimizing element placement in FRIS significantly improves SOP compared to conventional RIS without phase adaptation. However, these improvements become less evident once the conventional RIS implements optimized beamforming (BF) and phase-shift (PS) control. In addition, FRIS maintains a clear advantage over compact RIS designs with optimized BF and PS, mainly due to its lower spatial correlation. Results further indicate that reducing the inter-element distance negatively impacts SOP, highlighting the importance of spatial diversity. Moreover, the proposed MLE-based channel modeling and learning-driven optimization framework offer a scalable and data-efficient methodology for exploring secrecy performance. These findings establish FRIS as a promising architecture for improving physical layer security in spatially constrained and correlation-limited wireless environments.
{"title":"Exploring Spatial Flexibility and Phase Design in Fluid Reconfigurable Intelligent Surfaces: A Physical Layer Security Perspective","authors":"José David Vega-Sánchez;Victor Hugo Garzón Pacheco;Nathaly Verónica Orozco Garzón;Daniel A. Riofrío Almeida;Diana Pamela Moya Osorio","doi":"10.1109/OJCOMS.2026.3655170","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3655170","url":null,"abstract":"This paper examines the secrecy outage probability (SOP) in Fluid Reconfigurable Intelligent Surfaces (FRIS) and contrasts their performance against two alternative RIS architectures: a traditional planar RIS and a compact RIS layout. To characterize the end-to-end FRIS channel, a maximum likelihood estimation (MLE) approach is introduced, while a Q-learning algorithm is employed to adaptively select the spatial positions of FRIS elements. Numerical evaluations show that optimizing element placement in FRIS significantly improves SOP compared to conventional RIS without phase adaptation. However, these improvements become less evident once the conventional RIS implements optimized beamforming (BF) and phase-shift (PS) control. In addition, FRIS maintains a clear advantage over compact RIS designs with optimized BF and PS, mainly due to its lower spatial correlation. Results further indicate that reducing the inter-element distance negatively impacts SOP, highlighting the importance of spatial diversity. Moreover, the proposed MLE-based channel modeling and learning-driven optimization framework offer a scalable and data-efficient methodology for exploring secrecy performance. These findings establish FRIS as a promising architecture for improving physical layer security in spatially constrained and correlation-limited wireless environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"965-977"},"PeriodicalIF":6.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082253","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 : 2026-01-14DOI: 10.1109/OJCOMS.2026.3654538
Haythem A. Bany Salameh;Banan Abu Hammad;Ahmad Al-Ajlouni;Malik Mohamed Umar
The increasing demands for high spectral efficiency, low energy consumption, flexible deployment, and stringent reliability in beyond 5G/6G systems motivate integrating unmanned aerial vehicles (UAVs) with cognitive radio (CR) and multi-antenna (MIMO) technologies. In CR–MIMO UAV networks, CR improves spectrum efficiency by allowing secondary UAVs to opportunistically exploit underutilized licensed spectrum while protecting primary users (PUs). Furthermore, MIMO technology increases spectral and energy efficiency by using spatial multiplexing, diversity, and array/beamforming gains. Due to the UAVs’ limited battery capacity, a key challenge in enabling efficient CR MIMO UAV networking is to maximize the number of served UAVs while minimizing the required transmit power under a set of quality-of-service, power, and spectrum access constraints. To address this, we propose a reliability-aware, batch-based framework for power allocation and channel assignment in CR–MIMO UAV networks. Unlike traditional sequential methods, this batching paradigm assigns power/channels to multiple UAVs simultaneously, resulting in more power-efficient, concurrent UAV transmissions. Specifically, the joint power allocation and channel assignment problem for multiple contending UAVs is formulated as a mixed-integer nonlinear program, which is known to be NP-hard. For a scalable solution, we introduce a two-stage, polynomial-time, batch-based framework that decouples power allocation from channel assignment. First, the framework formulates and solves a convex per-antenna power minimization problem for each UAV–channel pair, enforcing rate, reliability, and power budget constraints, which leads to a closed-form per-antenna power solution. Based on the computed powers, the second stage performs batch-based channel assignment to minimize required transmit power under exclusive-assignment and maximum-matching constraints. This is achieved by formulating and solving a totally unimodular binary linear program that corresponds to a minimum-weight maximum matching problem, which can be solved optimally using the Hopcroft–Karp algorithm. The polynomial-time complexity of the proposed algorithm is established through analytical computational analysis. Simulations in realistic indoor scenarios demonstrate that the proposed approach consistently satisfies the imposed constraints under varying PU traffic, serves more UAVs with higher success probability, and reduces the total transmit power compared to baseline methods with comparable computational complexity for practical network conditions.
{"title":"Reliability-Aware Batch-Based Power-Efficient Spectrum Assignment in CR-MIMO UAV Networks","authors":"Haythem A. Bany Salameh;Banan Abu Hammad;Ahmad Al-Ajlouni;Malik Mohamed Umar","doi":"10.1109/OJCOMS.2026.3654538","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3654538","url":null,"abstract":"The increasing demands for high spectral efficiency, low energy consumption, flexible deployment, and stringent reliability in beyond 5G/6G systems motivate integrating unmanned aerial vehicles (UAVs) with cognitive radio (CR) and multi-antenna (MIMO) technologies. In CR–MIMO UAV networks, CR improves spectrum efficiency by allowing secondary UAVs to opportunistically exploit underutilized licensed spectrum while protecting primary users (PUs). Furthermore, MIMO technology increases spectral and energy efficiency by using spatial multiplexing, diversity, and array/beamforming gains. Due to the UAVs’ limited battery capacity, a key challenge in enabling efficient CR MIMO UAV networking is to maximize the number of served UAVs while minimizing the required transmit power under a set of quality-of-service, power, and spectrum access constraints. To address this, we propose a reliability-aware, batch-based framework for power allocation and channel assignment in CR–MIMO UAV networks. Unlike traditional sequential methods, this batching paradigm assigns power/channels to multiple UAVs simultaneously, resulting in more power-efficient, concurrent UAV transmissions. Specifically, the joint power allocation and channel assignment problem for multiple contending UAVs is formulated as a mixed-integer nonlinear program, which is known to be NP-hard. For a scalable solution, we introduce a two-stage, polynomial-time, batch-based framework that decouples power allocation from channel assignment. First, the framework formulates and solves a convex per-antenna power minimization problem for each UAV–channel pair, enforcing rate, reliability, and power budget constraints, which leads to a closed-form per-antenna power solution. Based on the computed powers, the second stage performs batch-based channel assignment to minimize required transmit power under exclusive-assignment and maximum-matching constraints. This is achieved by formulating and solving a totally unimodular binary linear program that corresponds to a minimum-weight maximum matching problem, which can be solved optimally using the Hopcroft–Karp algorithm. The polynomial-time complexity of the proposed algorithm is established through analytical computational analysis. Simulations in realistic indoor scenarios demonstrate that the proposed approach consistently satisfies the imposed constraints under varying PU traffic, serves more UAVs with higher success probability, and reduces the total transmit power compared to baseline methods with comparable computational complexity for practical network conditions.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1019-1032"},"PeriodicalIF":6.3,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11352532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082233","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 : 2026-01-12DOI: 10.1109/OJCOMS.2026.3653312
Laiba Tanveer;Zeeshan Kaleem;Aiman H. El-Maleh;Muhammad Afaq;Abdulaziz Barnawi;Huseyin Arslan
Recently, shortage of ground-based communication, transportation, and surveillance services has prompted the exploration of low-altitude airspace, that leads to Low-altitude economy (LAE) networks. Unlike traditional uncrewed aerial vehicle (UAV) systems, LAE envisions dense networks of flying platforms that serve both as mobile base stations and service nodes. However, the malicious deployment of UAVs in LAE networks can result in serious disasters. Therefore, robust and real-time UAV threat detection capabilities are required, particularly for low-signal-to-noise ratio (SNR) conditions. To address these challenges within LAE networks, we propose a Hybrid Quantum-Classical Convolutional Neural Network $(mathrm {HQC^{2}NN})$ for low-SNR RF drone signal classification. The model fuses classical feature extraction with quantum variational circuits to leverage quantum superposition and entanglement for improved representation learning. By providing an efficient and noise-resilient RF sensing mechanism, the proposed HQC2NN directly supports the sensing plane of LAE architectures, enabling reliable situational awareness in dense, interference-prone environments. Simulations demonstrate a classification accuracy of 97.3%, outperforming classical counterparts under noisy conditions. The results underscore the potential of quantum-enhanced deep learning models for robust RF signal analysis and real-time drone detection.
{"title":"HQC2NN: Hybrid Quantum-Classical Drone Detection for Low-SNR Conditions in Low-Altitude Economy Networks","authors":"Laiba Tanveer;Zeeshan Kaleem;Aiman H. El-Maleh;Muhammad Afaq;Abdulaziz Barnawi;Huseyin Arslan","doi":"10.1109/OJCOMS.2026.3653312","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3653312","url":null,"abstract":"Recently, shortage of ground-based communication, transportation, and surveillance services has prompted the exploration of low-altitude airspace, that leads to Low-altitude economy (LAE) networks. Unlike traditional uncrewed aerial vehicle (UAV) systems, LAE envisions dense networks of flying platforms that serve both as mobile base stations and service nodes. However, the malicious deployment of UAVs in LAE networks can result in serious disasters. Therefore, robust and real-time UAV threat detection capabilities are required, particularly for low-signal-to-noise ratio (SNR) conditions. To address these challenges within LAE networks, we propose a Hybrid Quantum-Classical Convolutional Neural Network <inline-formula> <tex-math>$(mathrm {HQC^{2}NN})$ </tex-math></inline-formula> for low-SNR RF drone signal classification. The model fuses classical feature extraction with quantum variational circuits to leverage quantum superposition and entanglement for improved representation learning. By providing an efficient and noise-resilient RF sensing mechanism, the proposed HQC2NN directly supports the sensing plane of LAE architectures, enabling reliable situational awareness in dense, interference-prone environments. Simulations demonstrate a classification accuracy of 97.3%, outperforming classical counterparts under noisy conditions. The results underscore the potential of quantum-enhanced deep learning models for robust RF signal analysis and real-time drone detection.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"614-629"},"PeriodicalIF":6.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11345628","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026568","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 : 2026-01-12DOI: 10.1109/OJCOMS.2026.3651975
Mohammadreza Mosahebfard;John S. Vardakas;Christos Verikoukis
The Sixth Generation (6G) networks are envisioned to support diverse and stringent Quality of Service (QoS) demands through advanced technologies such as network slicing and Network Function Virtualization (NFV). However, the online provisioning of Service Function Chains (SFCs) in such dynamic, multi-domain environments presents a complex optimization challenge. Existing approaches often fail to holistically balance power consumption across both computational and networking domains while adhering to strict, per-request QoS guarantees and slice-aware resource sharing policies. This paper addresses this gap by proposing HORIZON, a novel and holistic online heuristic for power and QoS-aware SFC Embedding (SFCE) in sliced 6G networks. The primary objective is to jointly minimize the total incremental network power consumption, encompassing both servers and Reconfigurable Optical Add-Drop Multiplexers (ROADMs), and the service blocking rate. The problem is first formulated as an Integer Linear Program (ILP), which includes a detailed linearization of the non-linear ROADM power model, to serve as an optimal benchmark. The core contribution, HORIZON, employs a proactive, backward placement strategy guided by a multi-metric server ranking function and a segmental, backward, QoS-aware routing algorithm. Extensive discrete-event simulations across various network topologies and dynamic traffic loads validate the proposed approach. Results demonstrate that HORIZON significantly outperforms state-of-the-art benchmark heuristics (ONE and Holu), consistently achieving performance closest to the optimal ILP benchmark. In resource-constrained, large-scale scenarios, HORIZON achieves relative power savings of up to 23.6% compared to Holu and 12.8% compared to ONE, while maintaining the lowest and most stable service blocking rates. Furthermore, HORIZON proves to be highly robust and computationally efficient, processing requests up to 67x faster than the ILP benchmark, establishing it as a practical solution for real-time service provisioning in 6G systems.
{"title":"HORIZON: Holistic Online Heuristic for Power and QoS-Aware Service Provisioning in Sliced 6G Networks","authors":"Mohammadreza Mosahebfard;John S. Vardakas;Christos Verikoukis","doi":"10.1109/OJCOMS.2026.3651975","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3651975","url":null,"abstract":"The Sixth Generation (6G) networks are envisioned to support diverse and stringent Quality of Service (QoS) demands through advanced technologies such as network slicing and Network Function Virtualization (NFV). However, the online provisioning of Service Function Chains (SFCs) in such dynamic, multi-domain environments presents a complex optimization challenge. Existing approaches often fail to holistically balance power consumption across both computational and networking domains while adhering to strict, per-request QoS guarantees and slice-aware resource sharing policies. This paper addresses this gap by proposing HORIZON, a novel and holistic online heuristic for power and QoS-aware SFC Embedding (SFCE) in sliced 6G networks. The primary objective is to jointly minimize the total incremental network power consumption, encompassing both servers and Reconfigurable Optical Add-Drop Multiplexers (ROADMs), and the service blocking rate. The problem is first formulated as an Integer Linear Program (ILP), which includes a detailed linearization of the non-linear ROADM power model, to serve as an optimal benchmark. The core contribution, HORIZON, employs a proactive, backward placement strategy guided by a multi-metric server ranking function and a segmental, backward, QoS-aware routing algorithm. Extensive discrete-event simulations across various network topologies and dynamic traffic loads validate the proposed approach. Results demonstrate that HORIZON significantly outperforms state-of-the-art benchmark heuristics (ONE and Holu), consistently achieving performance closest to the optimal ILP benchmark. In resource-constrained, large-scale scenarios, HORIZON achieves relative power savings of up to 23.6% compared to Holu and 12.8% compared to ONE, while maintaining the lowest and most stable service blocking rates. Furthermore, HORIZON proves to be highly robust and computationally efficient, processing requests up to 67x faster than the ILP benchmark, establishing it as a practical solution for real-time service provisioning in 6G systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"498-520"},"PeriodicalIF":6.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026605","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 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}
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