IEEE Open Journal of the Communications Society最新文献

{"title":"Secure Agentic AI and Multimodal Intelligence in 6G Communication Systems","authors":"Satyam Gaba;Devanshu Motwani;Siva Sai;Vineet Suri;Vikas Ranjan;Vinay Chamola","doi":"10.1109/OJCOMS.2026.3717323","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3717323","url":null,"abstract":"Sixth-generation (6G) communication systems are expected to support immersive, intent-driven, and latency-critical services through tight integration of multimodal intelligence, edge computing, and autonomous network control. In this setting, agentic AI systems can reason over heterogeneous inputs, use tools and network functions, coordinate across distributed entities, and adapt to dynamic environments. These capabilities make agentic multimodal intelligence promising for 6G applications such as semantic communications, digital twins, and zero-touch network management, but they also introduce security and privacy risks that extend beyond conventional AI and network security models. Vulnerabilities can propagate across multimodal data pipelines, memory, tools, communication interfaces, and inter-agent coordination loops. We survey multimodal intelligence and agentic AI for 6G cross-layered communication systems from the perspectives of privacy and security. We present a 6G-oriented system view spanning perception, memory, tool use, and multi-agent orchestration; analyze threat models and attack surfaces across sensing, cognition, action, memory, and communication layers; organize existing attacks into a structured taxonomy; and review mitigation strategies including prompt and memory defenses, inter-agent trust mechanisms, secure orchestration, and privacy-preserving learning. We conclude by outlining open research directions for trustworthy multimodal multi-agent 6G systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"8926-8944"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11626059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747486","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":"Spectrum-Aligned DFT for THz Massive MIMO: A Fully Digital Transmit Precoding Solution to the Bandwidth Disaster","authors":"He Liao;Kaiyuan Wang;Yi Wan","doi":"10.1109/OJCOMS.2026.3723692","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3723692","url":null,"abstract":"The transition to terahertz (THz) and millimeter-wave (mmWave) frequencies is essential for meeting the extreme data-rate demands of 6G wireless networks. However, the associated ultra-wide bandwidth induces severe beam squint—a frequency-dependent beam misalignment that drastically reduces array gain and spectral efficiency. Conventional approaches face an irreconcilable trade-off: fully digital processing offers high accuracy but with prohibitive computational complexity, while hardware-based true-time-delay (TTD) networks incur excessive power consumption and insertion loss. In this paper, we propose a novel fully-digital architecture termed Spectrum-Aligned DFT (SADFT), which eliminates beam squint purely in the digital domain via mathematical reconstruction, designed for a fully-digital transmit architecture. The SADFT organizes the computation as a single-path pruned radix-2 recursive signal flow graph—a computational structure that enables regular in-place memory access, pipeline-friendly stage-wise multiplier reuse, and direct mapping to existing FFT hardware accelerators by reconfiguring only the twiddle factor generation logic—that directly targets the frequency-variant spatial frequencies, achieving mathematically exact alignment (gain loss bounded by <inline-formula> <tex-math>$10^{-4}$ </tex-math></inline-formula> dB for the simulated configurations) through virtual dense-grid mapping and path pruning. We rigorously extend the framework to two-dimensional uniform planar arrays (UPAs), proving mathematically exact separability into cascaded 1D operations that achieve the theoretical lower bound of <inline-formula> <tex-math>$mathcal {O}(M)$ </tex-math></inline-formula> complexity for an <inline-formula> <tex-math>$M$ </tex-math></inline-formula>-element array. Extensive simulations using THz-specific Saleh–Valenzuela multipath models and the 3GPP CDL-A clustered channel model at 300 GHz demonstrate that SADFT closely approaches the Shannon capacity, delivering over <inline-formula> <tex-math>$3times $ </tex-math></inline-formula> capacity improvement over standard FFT-based beamforming in ultra-wideband scenarios and effectively removing the baseband computational barrier that has historically prevented fully-digital wideband beamforming from being computationally feasible. The proposed architecture offers a scalable, low-complexity solution for future extremely large-scale antenna arrays (ELAAs) in 6G systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"10229-10246"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11655228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836795","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":"Enricher: An Edge-Based QoS Optimization Framework for Vertical Apps Over 5G via Network-Exposed APIs","authors":"Vincent Charpentier;Alessandro Calvio;Andreas Gavrielides;Miguel Camelo;Johann M. Marquez-Barja;Nina Slamnik-Kriještorac","doi":"10.1109/OJCOMS.2026.3711428","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3711428","url":null,"abstract":"Emerging fifth-generation (5G) capabilities such as network slicing and network-exposed application programming interfaces (APIs) aim to support vertical industries with stringent Quality of Service (QoS) requirements. However, current solutions lack per-user observability and real-time adaptation, limiting their effectiveness in dynamic, safety-critical environments. This paper introduces Enricher, a modular edge-based framework that autonomously optimizes application-level QoS by leveraging standardized APIs such as CAMARA. Enricher operates as middleware between vertical applications and 5G infrastructure, combining localized telemetry, artificial intelligence (AI)-based forecasting, and intent-driven control, without requiring telecom-specific logic from vertical developers. We validated Enricher’s performance in a real-world 5G standalone (SA) deployment for teleoperated vessel control at the Port of Antwerp-Bruges. Compared to baseline approaches, Enricher reduces Service-Level Agreement (SLA) violations by 84% and resource overprovisioning by 36%, achieving 97.3% SLA compliance. These results position Enricher as a deployable and domain-agnostic solution for per-application QoS adaptation in 5G and beyond.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"10150-10169"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11599668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837107","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":"Quantization-Aware Learning for Secure and Resilient UAV-STAR-RIS-Enabled 6G Systems","authors":"Yaser Almasri;Khaled M. Rabie;Mahmoud M. Salim;Ashraf S. Mahmoud;Ali Arshad Nasir","doi":"10.1109/OJCOMS.2026.3725175","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3725175","url":null,"abstract":"Secure and reliable wireless communication is a major requirement for upcoming 6G mission-critical applications, such as emergency response and public safety networks. In this respect, unmanned aerial vehicle (UAV)-mounted, simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems are expected to play an important role in enabling secure, mission-critical 6G communications. However, practical implementations require discrete phase shifts, which can introduce performance degradation due to phase quantization effects. In this paper, we investigate phase quantization in a UAV-mounted STAR-RIS-assisted downlink non-orthogonal multiple access (NOMA) network to maximize secrecy rate. Two approaches are considered, namely, post-training quantization (PTQ) and quantization-aware training (QAT). A Twin Delayed Deep Deterministic Policy Gradient (TD3) agent is developed to jointly optimize STAR-RIS phase shifts and UAV positioning while enforcing minimum quality-of-service (QoS) requirements for users. Simulation results show that QAT consistently outperforms PTQ, particularly at low resolutions. While PTQ suffers significant performance degradation under coarse quantization, QAT maintains near-continuous performance at 3-bit resolution with less than 1% secrecy rate loss, and significantly reduces degradation at 2 bits. These results demonstrate that QAT enables efficient low-bit STAR-RIS operation, reducing hardware complexity and signaling overhead while preserving secrecy performance, thereby enhancing the practical viability of the proposed framework.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"10095-10110"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11658898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837165","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 Dynamic Priority-Aware Task Offloading and Resource Allocation Strategy Assisted by RSU-Relay in Vehicular Edge Computing","authors":"Dun Cao;Yuan Su;Wenqian Wang;R. Simon Sherratt;Jin Wang","doi":"10.1109/OJCOMS.2025.3649533","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3649533","url":null,"abstract":"The rapid advancement of the Internet of Vehicular has enabled a wide range of vehicular applications, intensifying the demand for low-latency and high-efficiency computation. However, the limited onboard computing capabilities and the highly dynamic nature of vehicular networks present significant challenges for task offloading and resource allocation. To tackle these challenges, Vehicular Edge Computing (VEC) incorporates Roadside Units (RSU) not only as computing nodes but also as relay nodes capable of multi-hop task forwarding, effectively extending the offloading scope and mitigating transmission constraints in sparse or high-mobility scenarios. Moreover, due to the diversity of task demands and fluctuating resource availability, it becomes essential to dynamically adjust task priorities to ensure timely task completion and efficient resource utilization. In this paper, we propose a novel Computing Offloading and Resource Allocation strategy under Relay Collaboration (CORA-RC). CORA-RC exploits the dual roles of RSU as computing and relay nodes to extend the offloading range and improve flexibility, while jointly optimizing task offloading, dynamic priority adjustment, and resource allocation. Simulation results demonstrate that CORA-RC ensures the timely completion of security-related tasks while effectively reducing the average task delay, thereby achieving the lowest average weighted cost among all compared strategies.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"353-369"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318631","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148485459","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":"Scaling Trust: A Domain-Specific Relational Extraction for Formal Verification With Limited Data Annotation in NextG Security Protocols","authors":"Jingda Yang;Paul Ratazzi;Ying Wang","doi":"10.1109/OJCOMS.2026.3702385","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3702385","url":null,"abstract":"Formal modeling and verification are essential for ensuring the reliability and security of domain-specific protocols, including 5G RRC and Department of Defense (DoD) network specifications. These tasks, however, face significant challenges due to the complexity and jargon of technical language, the limited availability of annotated data, and the difficulty of adapting machine learning models to handle out-of-vocabulary (OOV) scenarios effectively. To address these challenges, this research proposes a hybrid framework that integrates Large Language Models (LLMs) with domain-specific models <inline-formula> <tex-math>$M_{phi }$ </tex-math></inline-formula>, leveraging a Monte Carlo Tree Search (MCTS) approach to dynamically balance generative and classification-based strategies for relation extraction and formal property generation. The proposed framework systematically analyzes the performance of generative and classification models under diverse conditions. Generative models excel in structured and context-aware environments but suffer from error propagation in OOV scenarios due to their sequential dependencies. Classification models, on the other hand, demonstrate robustness to OOV data but lack the ability to leverage contextual dependencies effectively in structured datasets. The MCTS-based model unifies these approaches, dynamically adjusting its reliance on prior information and independence to achieve superior robustness and adaptability. Experimental evaluations highlight the MCTS model’s ability to outperform traditional models, maintaining high accuracy even in challenging OOV conditions. By combining insights from information theory with experimental validation, this research introduces a scalable and resilient solution for relation extraction and formal modeling in scenarios with limited annotated data. Future work will focus on optimizing the MCTS framework with advanced reward mechanisms, extending its application to diverse domain-specific protocols, and integrating explainability features to enhance trust and transparency. This work contributes to automating formal verification in complex systems, offering a significant step forward for both academia and industry in advancing the reliability of critical systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"6856-6870"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11558363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148486098","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":"Mobility-Aware Fog-Aided Mobile Crowdsensing: From Spatiotemporal and Continuous-Time Perspective","authors":"Lenong Chu;Young Jin Chun;Hing Cheung So","doi":"10.1109/OJCOMS.2026.3707162","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3707162","url":null,"abstract":"Mobile crowdsensing (MCS) leverages distributed devices to collect and share real-time data for location-driven applications, yet its performance is constrained by the absence of continuous-time theoretical frameworks addressing worker mobility and energy efficiency. To our knowledge, there is no continuous-time framework yet that addresses both worker mobility patterns and power control strategies for MCS performance. This paper proposes a novel analytical framework for fog-assisted MCS in location-driven scenarios, adopting a continuous-time and spatiotemporal perspective. Two mobility models are employed: two-dimensional Brownian motion and constrained Brownian motion, to characterize the physical locations of MCS workers under realistic pathway constraints. Assuming time-uncorrelated Rayleigh fading, we evaluate key performance indicators, including departure time, instantaneous capacity, fog-aware temporal average transmission success probability (TATSP), cumulative sensing data and region of interest (ROI)-capacity. Furthermore, we assess the power consumption and energy efficiency of the path-loss inversion power control strategy in fog-aided MCS system. Our findings offer a comprehensive perspective on the spatiotemporal evolution of worker performance, establishing foundational principles for task allocation in MCS with unpredictable mobility patterns. Theoretical results suggest that long-term sensing tasks may be preferentially assigned to low-mobility workers to optimize fog-aware TATSP. Moreover, the energy efficiency decreases monotonically with respect to the initial link distance. While numerical results, validated by Monte Carlo simulations and real-world GPS trajectory data from the Geolife dataset, illustrate how mobility, initial link distance, and path-loss coefficient influence instantaneous capacity, ROI-capacity, cumulative sensing data and energy efficiency. It is also revealed that, assigning the tasks to the nearest low-mobility workers presents a simple yet effective scheme.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"7136-7158"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11579392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148486352","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":"Orthogonal Chirp-Frequency-Division Multiple Access: System, Channel Estimation, and Pilot Optimization","authors":"Túlio F. Moreira;Wallace A. Martins;Moisés V. Ribeiro","doi":"10.1109/OJCOMS.2026.3694134","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3694134","url":null,"abstract":"This paper introduces the orthogonal chirp-frequency-division multiple access (OCFDMA), a technique based on orthogonal chirp-frequency-division multiplexing that partitions the frequency band into independent chirp-based subchannels, referred to as voices. By leveraging the block-circulant property of the discrete modular chirp transform, OCFDMA assigns each user one or more voices to achieve orthogonal, interference-free, multiple-access transmissions, thereby enabling per-user processing and reducing transceiver complexity. User-oriented geometries are developed to allow flexible trade-offs between bit-error rate (BER) and data rate by adjusting the number and size of voices. Per-voice linear models are derived, and both minimum-variance unbiased and minimum mean-squared-error channel estimators are presented, along with optimal pilot designs. The proposed technique is compared with state-of-the-art chirp-based multiple-access techniques, such as orthogonal chirp-division multiple access (OCDMA), as well as orthogonal frequency-division multiple access (OFDMA)-related techniques widely adopted in current wireless standards. Numerical experiments show that OCFDMA achieves superior per-user BER, strong robustness to cyclic-prefix-length violation and carrier-frequency offset, competitive data rates, and a peak-to-average power ratio comparable to OFDMA for both downlink and uplink connections.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"5602-5620"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11523042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148486358","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":"DATURA: A Deep Learning-Based Adaptive Traffic-Aware Unified Resource Autoscaling Framework for VNFs in 5G/B5G Networks","authors":"Kanchan Kumar Tiwari;Krishna M. Sivalingam","doi":"10.1109/OJCOMS.2026.3668155","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3668155","url":null,"abstract":"This paper introduces DATURA, a Deep Learning(DL)–based Adaptive Traffic-aware Unified Resource Autoscaling framework for virtual network functions (VNFs) in 5G and beyond networks. A predictive GRU–MLP based scaling architecture is proposed and integrated with a deep reinforcement learning (DRL)-based traffic generator. The GRU–MLP scaling controller forecasts per-VNF and per-slice latencies from streaming telemetry. Temporal dependencies are captured through the GRU layer, while two multitask MLP heads estimate local (per-VNF) and global (slice-level) latency trends. Once deployed, DATURA operates fully online, executing inference-driven scaling decisions in real time. The DRL traffic generator employs Proximal Policy Optimisation (PPO) agents trained to learn transition probability matrices (TPM) that capture realistic transitions between low, medium, high, and very-high-load regimes. The underlying models are periodically retrained with updated telemetry, enabling the framework to adapt to long-term traffic evolution without service disruption. The predicted latencies are then translated by a regression layer into proactive scaling decisions and resource allocations that maintain SLA compliance while avoiding unnecessary over-provisioning. The framework is implemented in a <monospace>SimPy</monospace>-based 5G Core model that supports UE registration and PDU session establishment message exchanges. Detailed experiments show that DATURA consistently outperforms other existing schemes based on Long Short-Term Memory (LSTM) and GRU. The proposed framework achieves up to 73% latency reduction, 55% throughput gain, and 47% higher provisioning efficiency.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"2197-2221"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11411821","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148486360","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":"RIS-Enabled Joint Communications and Sensing in 6G NTNs: Opportunities and Challenges","authors":"Wali Ullah Khan;Chandan Kumar Sheemar;Muhammad Ali Jamshed;Eva Lagunas;Jorge Querol;Aryan Kaushik;Symeon Chatzinotas","doi":"10.1109/OJCOMS.2026.3655070","DOIUrl":"https://doi.org/10.1109/OJCOMS.2026.3655070","url":null,"abstract":"The integration of Joint Communications and Sensing (JCAS) and Reconfigurable Intelligent Surfaces (RIS) is poised to be a cornerstone of Sixth-Generation (6G) Non-Terrestrial Networks (NTNs), enabling unprecedented capabilities for ubiquitous connectivity and high-resolution environmental monitoring. While the synergy between RIS and JCAS in terrestrial networks has been explored, its application in the unique and stratified environment of NTNs presents distinct opportunities and challenges across different propagation domains. This paper provides a comprehensive analysis of RIS-enabled JCAS for 6G NTNs, with a specific focus on differentiating the operational landscape between the space layer and the atmospheric layer. We identify novel key opportunities in the atmospheric layer, including uncrewed aerial vehicles (UAVs) and high-altitude platforms (HAPSs), and the space layer, including, low-Earth orbit, medium Earth orbit, and Geostationary orbit (GEO), and present and discuss the future challenges to be addressed to realize the integration of RIS a reality for the next-generation JCAS for NTNs.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"7 ","pages":"821-843"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148486425","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}