IEEE Open Journal of the Communications Society最新文献

{"title":"An Ensemble Deep Reinforcement Learning-Based Asynchronous Scheduling Mechanism for 5G-TSN","authors":"Junjian Zhang;Sifan Long;Qing Zhang;Linfeng Luo;Ming Zhao;Fengxiao Tang;Nei Kato","doi":"10.1109/OJCOMS.2026.3659198","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3659198","url":null,"abstract":"Integrating Time-Sensitive Networking (TSN) with 5G enables Industrial IoT (IIoT) systems to achieve low latency and high reliability. However, deterministic cross-domain scheduling remains challenging due to strict synchronization demands and wireless channel variations. This paper proposes an asynchronous scheduling mechanism that consists of Ensemble Deep Reinforcement Learning and Online Asynchronous Flow Scheduling. The former integrates three reinforcement learning agents to generate a near-optimal Gate Control List (GCL) in complex asynchronous network environments, and the latter reallocates time-slot paths online based on the GCL to make fuller use of idle slot resources. Extensive OMNeT<inline-formula> <tex-math>$text {++}$ </tex-math></inline-formula> simulations show that the proposed asynchronous scheduling mechanism efficiently utilizes network resources, reduces delay from wireless fluctuations and asynchrony, and improves throughput.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1507-1522"},"PeriodicalIF":6.3,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11367707","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223674","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}

{"title":"Understanding the Role of Phase and Position Design in Fluid Reconfigurable Intelligent Surfaces","authors":"José David Vega Sánchez;Victor Hugo Garzón Pacheco;Nathaly Verónica Orozco Garzón;Henry Ramiro Carvajal Mora;F. Javier López-Martínez","doi":"10.1109/OJCOMS.2026.3658261","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3658261","url":null,"abstract":"Fluid Reconfigurable Intelligent Surfaces (FRISs) are gaining momentum as an improved alternative over classical RIS. However, it remains unclear whether their performance gains can be entirely attributed to spatial flexibility, or instead to differences in equivalent aperture or phase design. In this work, we shed light onto this problem by benchmarking FRIS vs. RIS performances in two practical scenarios: conventional RIS (same number of active elements and same overall aperture) and compact RIS (same number of active elements, and smaller aperture with sub-<inline-formula> <tex-math>$lambda $ </tex-math></inline-formula> inter-element spacing). Statistical analysis demonstrates that: 1) spatial position optimization in FRIS provides noticeable gains over conventional RIS in the absence of phase-shift (PS) design; 2) such benefits vanish when FRIS and conventional RIS employ optimal beamforming (BF) and PS design, making position optimization irrelevant; and 3) FRIS consistently outperforms compact RIS with optimized BF and PS design, owing to spatial correlation and smaller aperture.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1414-1425"},"PeriodicalIF":6.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11366925","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175636","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}

{"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}

{"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}

{"title":"A GenAI-Driven Framework for Context-Aware Semantic Communication in Digital Twin Networks","authors":"Peng Luo;Li Zhou;Haitao Zhao;Nan Li;Kuo Cao;Yueling Liu;Yichi Zhang;Jibo Wei","doi":"10.1109/OJCOMS.2026.3657769","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3657769","url":null,"abstract":"Semantic communication (SemCom) is expected to play a key role in 6G and digital twin network (DTN) environments, where reliability depends on preserving meaning and user intent rather than reconstructing bit sequences. This paper presents a GenAI-driven, contextual-knowledge-enhanced SemCom framework that strengthens semantic recovery by integrating relational, intra-sentential, inter-sentential, and historical knowledge. At the transmitter, a context-attentive (CA) mechanism combines sentence-level semantic features with relational knowledge generated by a relation-guided knowledge base. At the receiver, a knowledge enhancement (KE) mechanism employs Transformer-XL to incorporate historical semantic context and utilizes a relational knowledge modeling module to refine semantic inference. Experimental results demonstrate that the proposed framework achieves robust semantic recovery across a wide range of signal-to-noise ratios (SNRs), with significant improvements in low-SNR conditions. Ablation studies further highlight the complementary roles of contextual knowledge at the transmitter and receiver. These results confirm the effectiveness of contextual knowledge in enhancing deep learning-based SemCom and underscore its potential for deployment within future DTN-enabled 6G architectures.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1396-1413"},"PeriodicalIF":6.3,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11363363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175637","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}

{"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}

{"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}

{"title":"Anonymous Two-Party Authentication and Key Establishment With Trusted Server for Future-Proofing Security of Mobile Communication System","authors":"Dharminder Chaudhary;Uddeshaya Kumar;Saibal Kumar Pal;Gaurav Mittal;Narendra Khatri","doi":"10.1109/OJCOMS.2026.3653851","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3653851","url":null,"abstract":"In cellular systems, user devices (UEs) establish secure sessions with each other through a trusted server such as the Home Subscriber Server (HSS) or Authentication Center (AuC). The proposed two-party authenticated key establishment allows two users to authenticate and exchange encrypted data with minimal involvement of the server - improving efficiency and scalability in mobile communication. Moreover, this paper presents a cryptanalysis of two post-quantum authentication and key agreement protocols proposed by Dabra et al. and Kumar et al. Our analysis identifies specific weaknesses, a design flaw in the registration phase of Dabra et al.’s protocol, and vulnerabilities to identity guessing and impersonation attacks in Kumar et al.’s scheme. Motivated by these findings, we propose a new communication framework for a two-party authenticated key establishment protocol assisted by a server. In the proposed model, user <inline-formula> <tex-math>$U_{A}$ </tex-math></inline-formula> initiates communication by sending a request to a central server and receives a corresponding response to establish an authenticated session key with user <inline-formula> <tex-math>$U_{B}$ </tex-math></inline-formula>. Notably, user <inline-formula> <tex-math>$U_{B}$ </tex-math></inline-formula> does not need to interact with the server during session initiation or key derivation phases, thereby significantly reducing communication overhead and enhancing efficiency. Through extensive formal analysis and simulation, our protocol achieves 21.37% and 19.99% improvement in computational efficiency and 35.13% and 21.05% reduction in communication overhead compared to the lattice-based authenticated key agreement protocols of Dabra et al. and Kumar et al., respectively. Furthermore, the proposed scheme ensures key freshness, meaning that each session key is unique and has never been used in any prior communication session. This guarantees that every session remains protected by a distinct cryptographic key, even when the same users communicate repeatedly. Additionally, user <inline-formula> <tex-math>$U_{A}$ </tex-math></inline-formula> can establish a secure session with user <inline-formula> <tex-math>$U_{B}$ </tex-math></inline-formula> without revealing its identity to either <inline-formula> <tex-math>$U_{B}$ </tex-math></inline-formula> or any intermediate server, thereby preserving user anonymity. Overall, the proposed protocol offers strong privacy guarantees while enabling secure, authenticated communication with a minimal number of message exchanges.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"1491-1506"},"PeriodicalIF":6.3,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11347569","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223750","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}

{"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}

{"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}