Pub Date : 2026-09-01Epub Date: 2026-10-14DOI: 10.23919/JCIN.2026.11737698
Xuanyu Wang;Zhiyu Yang;Xiong Wang;Jing Ren;Sheng Wang
Network telemetry is critical for low earth orbit (LEO) mega-constellation networks, where frequent topology changes and link disruptions make timely state collection challenging. Existing telemetry methods often suffer from limited coverage, high collection overhead, and poor observability resilience to link and node failures. This paper presents HyTel, a self-healing hybrid telemetry system designed for LEO mega-constellation networks. HyTel combines the controllability of active out-of-band probing with the fine-grained measurement capabilities of in-network telemetry and enables self-healing telemetry. In particular, HyTel proposes ring-based and tree-based telemetry mechanisms to balance probing cost, measurement coverage, and failure resilience. The ring-based mechanism provides low-overhead telemetry along orbital paths, while the tree-based mechanism improves observability resilience and failure localization through branch-based probing. We further design a lightweight telemetry protocol with a temporary-monitor-based recovery strategy for restoring observability after failures. Experiments on multi-plane, multi-shell dynamic constellations and analyses of representative failure scenarios compare HyTel with two-way active measurement protocol (TWAMP) and in-band network telemetry (INT) path-balance. Results show that HyTel-Tree achieves the lowest link-delay error and telemetry time per round while restoring telemetry availability through temporary-monitor election. HyTel-Ring incurs lower packet cost but provides reduced coverage when ring continuity is disrupted. Overall, HyTel offers a practical trade-off among accuracy, efficiency, overhead, and resilience.
{"title":"HyTel: A Self-Healing Hybrid Telemetry System for LEO Mega-Constellation Networks","authors":"Xuanyu Wang;Zhiyu Yang;Xiong Wang;Jing Ren;Sheng Wang","doi":"10.23919/JCIN.2026.11737698","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11737698","url":null,"abstract":"Network telemetry is critical for low earth orbit (LEO) mega-constellation networks, where frequent topology changes and link disruptions make timely state collection challenging. Existing telemetry methods often suffer from limited coverage, high collection overhead, and poor observability resilience to link and node failures. This paper presents HyTel, a self-healing hybrid telemetry system designed for LEO mega-constellation networks. HyTel combines the controllability of active out-of-band probing with the fine-grained measurement capabilities of in-network telemetry and enables self-healing telemetry. In particular, HyTel proposes ring-based and tree-based telemetry mechanisms to balance probing cost, measurement coverage, and failure resilience. The ring-based mechanism provides low-overhead telemetry along orbital paths, while the tree-based mechanism improves observability resilience and failure localization through branch-based probing. We further design a lightweight telemetry protocol with a temporary-monitor-based recovery strategy for restoring observability after failures. Experiments on multi-plane, multi-shell dynamic constellations and analyses of representative failure scenarios compare HyTel with two-way active measurement protocol (TWAMP) and in-band network telemetry (INT) path-balance. Results show that HyTel-Tree achieves the lowest link-delay error and telemetry time per round while restoring telemetry availability through temporary-monitor election. HyTel-Ring incurs lower packet cost but provides reduced coverage when ring continuity is disrupted. Overall, HyTel offers a practical trade-off among accuracy, efficiency, overhead, and resilience.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 3","pages":"401-415"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The hybrid satellite-terrestrial relay network (HSTRN) is a promising solution to enhance link reliability. However, its performance analysis is challenged by the geometric constraints introduced by relay coverage regions and elevation angle limitations, as well as terrestrial co-channel interference (CCI). This paper investigates the uplink successful transmission probability of HSTRN within a stochastic geometry framework. In our analysis, low earth orbit (LEO) satellite constellations are modeled as a binomial point process (BPP), while interfering terminals are described by a Poisson point process (PPP). Analytical expressions for the uplink successful transmission probability and the co-channel interference are derived, and their accuracy is validated through Monte Carlo simulations. Furthermore, key design insights are obtained, including the optimal relay coverage radius and the optimal satellite altitude, providing valuable guidelines for system design.
{"title":"Uplink Successful Transmission Probability Analysis for Hybrid Satellite-Terrestrial Relay Network: A Stochastic Geometry Framework","authors":"Guanjun Xu;Junjun Cui;Shuyuan Lu;Lina Zhu;Xinbo Xu;Dai Tian","doi":"10.23919/JCIN.2026.11604012","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11604012","url":null,"abstract":"The hybrid satellite-terrestrial relay network (HSTRN) is a promising solution to enhance link reliability. However, its performance analysis is challenged by the geometric constraints introduced by relay coverage regions and elevation angle limitations, as well as terrestrial co-channel interference (CCI). This paper investigates the uplink successful transmission probability of HSTRN within a stochastic geometry framework. In our analysis, low earth orbit (LEO) satellite constellations are modeled as a binomial point process (BPP), while interfering terminals are described by a Poisson point process (PPP). Analytical expressions for the uplink successful transmission probability and the co-channel interference are derived, and their accuracy is validated through Monte Carlo simulations. Furthermore, key design insights are obtained, including the optimal relay coverage radius and the optimal satellite altitude, providing valuable guidelines for system design.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 2","pages":"242-249"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11604012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443295","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}
Satellite-ground semantic communication is an important component of the forthcoming 6G era. Due to the strict bandwidth limitations of low earth orbit (LEO) satellites, efficient transmission of massive satellite remote sensing images is difficult. To this end, we propose a channel-aware adaptive semantic communication method for LEO satellite remote image transmission. The proposed method includes a feature extraction module, an important feature enhancement module, a rate adaptive module, and the corresponding decoding modules. The feature extraction module can extract global context information via lightweight mamba. The important feature enhancement module can enhance useful features via the involution layer and the signal to noise radio (SNR) adaptive block according to channel state SNRs. The rate adaptive module can further adaptively adjust the size of transmission features according to the transmission rate via the rate adaptive block and the rate mask block. The extensive experimental results on the WHU-RS19 dataset demonstrate that our method obtains higher peak signal to noise radio (PSNR), multi-scale structural similarity index measure (MS-SSIM) and learned perceptual image patch similarity (LPIPS) than state-of-the-art methods under low SNR and limited bandwidth conditions.
{"title":"A Channel-Aware Adaptive Semantic Communication Method for LEO Satellite Remote Image Transmission","authors":"Zhongqiang Zhang;Chiya Zhang;Qi Qiu;Chaofan He;Fanyang Meng","doi":"10.23919/JCIN.2026.11604013","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11604013","url":null,"abstract":"Satellite-ground semantic communication is an important component of the forthcoming 6G era. Due to the strict bandwidth limitations of low earth orbit (LEO) satellites, efficient transmission of massive satellite remote sensing images is difficult. To this end, we propose a channel-aware adaptive semantic communication method for LEO satellite remote image transmission. The proposed method includes a feature extraction module, an important feature enhancement module, a rate adaptive module, and the corresponding decoding modules. The feature extraction module can extract global context information via lightweight mamba. The important feature enhancement module can enhance useful features via the involution layer and the signal to noise radio (SNR) adaptive block according to channel state SNRs. The rate adaptive module can further adaptively adjust the size of transmission features according to the transmission rate via the rate adaptive block and the rate mask block. The extensive experimental results on the WHU-RS19 dataset demonstrate that our method obtains higher peak signal to noise radio (PSNR), multi-scale structural similarity index measure (MS-SSIM) and learned perceptual image patch similarity (LPIPS) than state-of-the-art methods under low SNR and limited bandwidth conditions.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 2","pages":"250-257"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11604013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443296","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 : 2026-06-01Epub Date: 2026-07-10DOI: 10.23919/JCIN.2026.11604007
Haoye Chai;Yaohua Sun;Mugen Peng
Low earth orbit (LEO) satellite networks are entering a critical phase of large-scale global deployment. However, their high dynamics pose unprecedented challenges to traditional terrestrial mobile communication and networking protocols. Although artificial intelligence (AI) technology provides new solution paths for high-efficiency and low-complexity operations, it still faces severe technical bottlenecks in practical application and deployment. This paper systematically analyzes three key challenges during the intelligent evolution of LEO satellites: model generalization issue, constrained payload capabilities, and decision latency bottleneck. In response to these challenges, this paper explores potential enabling AI technologies, including the use of meta-learning and graph neural networks to enhance model generalization, the implementation of model compression and lightweight strategies, and the application of generative AI and delay-tolerant reinforcement learning to improve resource management efficiency and decision robustness. On this basis, a further outlook on typical AI-enabled application scenarios is provided: at the communication transmission level, the paper highlights generative AI-driven channel estimation, delay-tolerant beam management, and interference suppression techniques based on diffusion models; at the networking level, graph-based routing strategies and meta-learning-based handover management schemes are discussed. The deep integration of AI technology and satellite communication regimes will serve as a critical support for constructing autonomous and ubiquitously intelligent integrated space information systems in the 6G era.
{"title":"AI-Empowered LEO Satellite Communications and Networking: Challenges, Enabling Technologies, and Future Applications","authors":"Haoye Chai;Yaohua Sun;Mugen Peng","doi":"10.23919/JCIN.2026.11604007","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11604007","url":null,"abstract":"Low earth orbit (LEO) satellite networks are entering a critical phase of large-scale global deployment. However, their high dynamics pose unprecedented challenges to traditional terrestrial mobile communication and networking protocols. Although artificial intelligence (AI) technology provides new solution paths for high-efficiency and low-complexity operations, it still faces severe technical bottlenecks in practical application and deployment. This paper systematically analyzes three key challenges during the intelligent evolution of LEO satellites: model generalization issue, constrained payload capabilities, and decision latency bottleneck. In response to these challenges, this paper explores potential enabling AI technologies, including the use of meta-learning and graph neural networks to enhance model generalization, the implementation of model compression and lightweight strategies, and the application of generative AI and delay-tolerant reinforcement learning to improve resource management efficiency and decision robustness. On this basis, a further outlook on typical AI-enabled application scenarios is provided: at the communication transmission level, the paper highlights generative AI-driven channel estimation, delay-tolerant beam management, and interference suppression techniques based on diffusion models; at the networking level, graph-based routing strategies and meta-learning-based handover management schemes are discussed. The deep integration of AI technology and satellite communication regimes will serve as a critical support for constructing autonomous and ubiquitously intelligent integrated space information systems in the 6G era.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 2","pages":"155-169"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11604007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443347","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 : 2026-06-01Epub Date: 2026-07-10DOI: 10.23919/JCIN.2026.11604006
Peng Yi;Ying-Chang Liang
Artificial intelligence (AI) has become a key enabler for next-generation wireless communication systems, offering powerful tools to cope with the increasing complexity, dynamics, and heterogeneity of modern wireless environments. To illustrate the role and the impact of AI in wireless communications, this paper takes collaborative spectrum sensing (CSS) in cognitive and intelligent wireless networks as a representative application and surveys recent advances from an AI perspective. We first introduce the fundamentals of CSS, including general framework, classical detector design, fusion strategies, and evaluation metrics. Then, we present an overview of the state-of-the-art research on AI-driven CSS, classified into three categories according to learning paradigms: discriminative deep learning (DL), generative DL models, and deep reinforcement learning (DRL). Building on this, we explore AI-empowered semantic communication (SemCom) as a paradigm-shifting solution for CSS. By extracting and transmitting task-relevant features, Sem-Com upgrades CSS from a computation-centric approach to a highly efficient joint communication and computation framework. Both single-user and multi-user SemCom scenarios are elaborated in detail. Finally, we discuss limitations, open challenges, and future research directions at the intersection of AI and wireless communication.
{"title":"Collaborative Spectrum Sensing in Cognitive and Intelligent Wireless Networks: An Artificial Intelligence Perspective","authors":"Peng Yi;Ying-Chang Liang","doi":"10.23919/JCIN.2026.11604006","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11604006","url":null,"abstract":"Artificial intelligence (AI) has become a key enabler for next-generation wireless communication systems, offering powerful tools to cope with the increasing complexity, dynamics, and heterogeneity of modern wireless environments. To illustrate the role and the impact of AI in wireless communications, this paper takes collaborative spectrum sensing (CSS) in cognitive and intelligent wireless networks as a representative application and surveys recent advances from an AI perspective. We first introduce the fundamentals of CSS, including general framework, classical detector design, fusion strategies, and evaluation metrics. Then, we present an overview of the state-of-the-art research on AI-driven CSS, classified into three categories according to learning paradigms: discriminative deep learning (DL), generative DL models, and deep reinforcement learning (DRL). Building on this, we explore AI-empowered semantic communication (SemCom) as a paradigm-shifting solution for CSS. By extracting and transmitting task-relevant features, Sem-Com upgrades CSS from a computation-centric approach to a highly efficient joint communication and computation framework. Both single-user and multi-user SemCom scenarios are elaborated in detail. Finally, we discuss limitations, open challenges, and future research directions at the intersection of AI and wireless communication.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 2","pages":"137-154"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11604006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443378","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}
To enable reliable and efficient communication under time-varying channel conditions, this paper proposes a feedback-based symbol rate adaptation scheme for ultraviolet (UV) communication. The receiver periodically reports channel state information, including the mean detected photon counts of signal and background components, to the transmitter after a fixed number of frames. Based on this feedback and predefined rate-switching thresholds obtained from off-line low-density parity-check (LDPC) performance simulations, the receiver selects appropriate symbol rates from a set of discrete levels and conveys the update decisions through a feedback channel. A lightweight decision logic is adopted to enable timely rate adjustments, and a feedback frame structure with frame indices is designed to ensure reliable symbol rate synchronization under frame loss. Indoor experiments with controlled link distance and noise variations verify the predictable adaptation behavior of the proposed scheme, while outdoor daylight experiments demonstrate stable communication and effective symbol rate tracking under time-varying channel conditions.
{"title":"A Symbol Rate Adaptation Method for Ultraviolet Communication Systems","authors":"Jingyang Chen;Yuexin Shen;Weijie Liu;Chen Gong;Zhengyuan Xu","doi":"10.23919/JCIN.2026.11604011","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11604011","url":null,"abstract":"To enable reliable and efficient communication under time-varying channel conditions, this paper proposes a feedback-based symbol rate adaptation scheme for ultraviolet (UV) communication. The receiver periodically reports channel state information, including the mean detected photon counts of signal and background components, to the transmitter after a fixed number of frames. Based on this feedback and predefined rate-switching thresholds obtained from off-line low-density parity-check (LDPC) performance simulations, the receiver selects appropriate symbol rates from a set of discrete levels and conveys the update decisions through a feedback channel. A lightweight decision logic is adopted to enable timely rate adjustments, and a feedback frame structure with frame indices is designed to ensure reliable symbol rate synchronization under frame loss. Indoor experiments with controlled link distance and noise variations verify the predictable adaptation behavior of the proposed scheme, while outdoor daylight experiments demonstrate stable communication and effective symbol rate tracking under time-varying channel conditions.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 2","pages":"231-241"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11604011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443294","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 : 2026-06-01Epub Date: 2026-07-10DOI: 10.23919/JCIN.2026.11604014
Haoyang Su;Wenhui Zhou;Lei Xie
With the widespread deployment of heterogeneous operating and computing devices in modern flexible manufacturing, stream processing applications are burdened by mismatches of computing capacity and demand, making it difficult to maintain service level objective (SLO) compliance in real-time manufacturing. To this end, we propose industry-EI, a cloud-edge collaborative scheduling algorithm inspired by embodied intelligence (EI). Industry-EI models the industrial system as a closed-loop embodied agent that continuously perceives the environment, makes scheduling decisions, and receives feedback on execution outcomes. To support real-time interaction with complex industrial environments, we design a perception-decision-control architecture that integrates deep reinforcement learning to adapt across heterogeneous production lines. To update the EI agent efficiently under dynamic workloads, we develop an iterative learning paradigm incorporating timestamp tracking and differential reward, which handles delayed and misaligned feedback phenomena and enables optimal decision-making. We implement a KubeEdge-based prototype and validate industry-EI in a real-world environment. Experiments show that industry-EI reduces the 95th percentile (P95) latency by 40.7% and achieves a SLO compliance rate of 93.7%.
{"title":"Industry-EI: Embodied Intelligence for Stream Scheduling in Industrial Flexible Manufacturing","authors":"Haoyang Su;Wenhui Zhou;Lei Xie","doi":"10.23919/JCIN.2026.11604014","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11604014","url":null,"abstract":"With the widespread deployment of heterogeneous operating and computing devices in modern flexible manufacturing, stream processing applications are burdened by mismatches of computing capacity and demand, making it difficult to maintain service level objective (SLO) compliance in real-time manufacturing. To this end, we propose industry-EI, a cloud-edge collaborative scheduling algorithm inspired by embodied intelligence (EI). Industry-EI models the industrial system as a closed-loop embodied agent that continuously perceives the environment, makes scheduling decisions, and receives feedback on execution outcomes. To support real-time interaction with complex industrial environments, we design a perception-decision-control architecture that integrates deep reinforcement learning to adapt across heterogeneous production lines. To update the EI agent efficiently under dynamic workloads, we develop an iterative learning paradigm incorporating timestamp tracking and differential reward, which handles delayed and misaligned feedback phenomena and enables optimal decision-making. We implement a KubeEdge-based prototype and validate industry-EI in a real-world environment. Experiments show that industry-EI reduces the 95th percentile (P95) latency by 40.7% and achieves a SLO compliance rate of 93.7%.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 2","pages":"258-269"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11604014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443297","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 : 2026-06-01Epub Date: 2026-07-10DOI: 10.23919/JCIN.2026.11604008
Wen Wang;Yongming Huang;Cheng Zhang
Spatially reconfigurable antenna arrays (SRAAs) have recently emerged as a promising paradigm for enhancing wireless system performance by treating antenna position and orientation as new spatial degrees of freedom (DoFs). Unlike conventional fixed-geometry antenna arrays, SRAAs enable geometry-aware adaptation of the physical aperture, thereby allowing wireless systems to actively exploit spatial channel variations beyond signal-domain processing. This capability is particularly attractive for future sixth-generation (6G) networks that operate in highly dynamic propagation environments and face stringent performance requirements. This review provides a comprehensive and system-oriented overview of SRAAs from both theoretical and practical perspectives. Firstly, we present a unified and geometry-aware channel modeling framework for spatial reconfiguration at different architectural granularities. Secondly, we analyze how position- and orientation-induced channel variations, along with their combined effects, and enable performance gains without relying solely on massive antenna scaling. Afterwards, we survey design and optimization methods for position-orientation reconfiguration, covering both model- and learning-based techniques. Practical considerations are also discussed through a systematic review of hardware implementation options and channel estimation techniques under spatial reconfiguration. To further illustrate the system-level benefits of SRAAs, representative applications are examined, including point-to-point and multiuser multiple-input multiple-output (MIMO), cell-free massive MIMO, as well as aerial and mobile communications. A dedicated case study on six-dimensional aerial rotatable antenna (6DARA)-enabled cell-free networks is provided to demonstrate how array-wise position and orientation control, combined with distributed optimization, can achieve substantial performance gains with manageable complexity. Finally, we outline key issues and future directions for the large-scale and practical deployment of SRAAs in 6G wireless networks.
{"title":"Spatially Reconfigurable Antenna Arrays for 6G Networks: Modeling, Methods, and Applications","authors":"Wen Wang;Yongming Huang;Cheng Zhang","doi":"10.23919/JCIN.2026.11604008","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11604008","url":null,"abstract":"Spatially reconfigurable antenna arrays (SRAAs) have recently emerged as a promising paradigm for enhancing wireless system performance by treating antenna position and orientation as new spatial degrees of freedom (DoFs). Unlike conventional fixed-geometry antenna arrays, SRAAs enable geometry-aware adaptation of the physical aperture, thereby allowing wireless systems to actively exploit spatial channel variations beyond signal-domain processing. This capability is particularly attractive for future sixth-generation (6G) networks that operate in highly dynamic propagation environments and face stringent performance requirements. This review provides a comprehensive and system-oriented overview of SRAAs from both theoretical and practical perspectives. Firstly, we present a unified and geometry-aware channel modeling framework for spatial reconfiguration at different architectural granularities. Secondly, we analyze how position- and orientation-induced channel variations, along with their combined effects, and enable performance gains without relying solely on massive antenna scaling. Afterwards, we survey design and optimization methods for position-orientation reconfiguration, covering both model- and learning-based techniques. Practical considerations are also discussed through a systematic review of hardware implementation options and channel estimation techniques under spatial reconfiguration. To further illustrate the system-level benefits of SRAAs, representative applications are examined, including point-to-point and multiuser multiple-input multiple-output (MIMO), cell-free massive MIMO, as well as aerial and mobile communications. A dedicated case study on six-dimensional aerial rotatable antenna (6DARA)-enabled cell-free networks is provided to demonstrate how array-wise position and orientation control, combined with distributed optimization, can achieve substantial performance gains with manageable complexity. Finally, we outline key issues and future directions for the large-scale and practical deployment of SRAAs in 6G wireless networks.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 2","pages":"170-202"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11604008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443293","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 : 2026-06-01Epub Date: 2026-07-10DOI: 10.23919/JCIN.2026.11604015
Yufan Xie;Hongguang Sun;Zhiming Lyu;Hongming Zhang;Shuqin Li
While millimeter wave (mmWave) communications offer vast bandwidth, their coverage is highly susceptible to blockages. Reconfigurable intelligent surface (RIS) have emerged as a promising solution to enhance mmWave connectivity. In this study, we analyze the coverage probability of RIS-assisted mmWave communications, which for the first time incorporates rectangular blockages, a two-step association strategy, and a far-field path loss model for RIS. Specifically, utilizing tools from stochastic geometry, we establish the system model, which includes the base station (BS), the blockage, the RIS, and the user distribution models. Subsequently, we define the association criterion and derive the conditional coverage probabilities for three distinct scenarios: line of sight (LOS) association, non-line-of-sight (NLOS) association, and RIS-assisted association. Finally, we combine the three cases to obtain the exact expressions for the coverage probability. Simulation results validate the theoretical analysis, showing that RIS provides up to 25% coverage gain and exhibits deployment-specific behavior under realistic blockage and interference. Notably, the benefits of RIS are most pronounced in heavily obstructed environments. Furthermore, as RIS deployment density and surface size increase, the optimal signal to interference plus noise ratio (SINR) threshold for maximizing area spectral efficiency (ASE) scales upward, shifting the network's peak performance toward higher operating regimes.
{"title":"Performance Analysis of RIS-Assisted mmWave Communications under Rectangular Blockages","authors":"Yufan Xie;Hongguang Sun;Zhiming Lyu;Hongming Zhang;Shuqin Li","doi":"10.23919/JCIN.2026.11604015","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11604015","url":null,"abstract":"While millimeter wave (mmWave) communications offer vast bandwidth, their coverage is highly susceptible to blockages. Reconfigurable intelligent surface (RIS) have emerged as a promising solution to enhance mmWave connectivity. In this study, we analyze the coverage probability of RIS-assisted mmWave communications, which for the first time incorporates rectangular blockages, a two-step association strategy, and a far-field path loss model for RIS. Specifically, utilizing tools from stochastic geometry, we establish the system model, which includes the base station (BS), the blockage, the RIS, and the user distribution models. Subsequently, we define the association criterion and derive the conditional coverage probabilities for three distinct scenarios: line of sight (LOS) association, non-line-of-sight (NLOS) association, and RIS-assisted association. Finally, we combine the three cases to obtain the exact expressions for the coverage probability. Simulation results validate the theoretical analysis, showing that RIS provides up to 25% coverage gain and exhibits deployment-specific behavior under realistic blockage and interference. Notably, the benefits of RIS are most pronounced in heavily obstructed environments. Furthermore, as RIS deployment density and surface size increase, the optimal signal to interference plus noise ratio (SINR) threshold for maximizing area spectral efficiency (ASE) scales upward, shifting the network's peak performance toward higher operating regimes.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 2","pages":"270-282"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11604015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443298","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 : 2026-06-01Epub Date: 2026-07-10DOI: 10.23919/JCIN.2026.11604009
Phuc Hao Do
Non-terrestrial networks (NTNs) are a cornerstone for 6G's global connectivity vision, but their hyper-dynamic nature presents unprecedented challenges in optimization, management, and security. This paper posits that the synergistic convergence of artificial intelligence (AI) and quantum technologies offers a transformative paradigm to address these issues. We conduct a comprehensive survey, presenting a novel taxonomy that classifies applications into three domains: quantum-enhanced AI, AI-powered quantum systems, and converged AI-quantum services. For each, we analyze formal problem formulations and hybrid solution architectures. To bridge theory and practice, we develop a detailed case study on handover optimization in a low earth orbit (LEO) satellite constellation under dynamic channel conditions. By formulating the problem as a quadratic unconstrained binary optimization (QUBO) model and solving it with a quantum-inspired annealer, our Monte Carlo simulations demonstrate a superior performance trade-off. The proposed approach reduces handovers by approximately 84% compared to a classical greedy strategy while maintaining high link quality and zero outage. Finally, we identify critical open research challenges and outline a future vision for autonomous, intelligent, and unconditionally secure global networks.
{"title":"Synergies of AI and Quantum Technologies in Next-Generation Non-Terrestrial Networks: A Comprehensive Survey","authors":"Phuc Hao Do","doi":"10.23919/JCIN.2026.11604009","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11604009","url":null,"abstract":"Non-terrestrial networks (NTNs) are a cornerstone for 6G's global connectivity vision, but their hyper-dynamic nature presents unprecedented challenges in optimization, management, and security. This paper posits that the synergistic convergence of artificial intelligence (AI) and quantum technologies offers a transformative paradigm to address these issues. We conduct a comprehensive survey, presenting a novel taxonomy that classifies applications into three domains: quantum-enhanced AI, AI-powered quantum systems, and converged AI-quantum services. For each, we analyze formal problem formulations and hybrid solution architectures. To bridge theory and practice, we develop a detailed case study on handover optimization in a low earth orbit (LEO) satellite constellation under dynamic channel conditions. By formulating the problem as a quadratic unconstrained binary optimization (QUBO) model and solving it with a quantum-inspired annealer, our Monte Carlo simulations demonstrate a superior performance trade-off. The proposed approach reduces handovers by approximately 84% compared to a classical greedy strategy while maintaining high link quality and zero outage. Finally, we identify critical open research challenges and outline a future vision for autonomous, intelligent, and unconditionally secure global networks.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 2","pages":"203-218"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11604009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443345","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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