Low-Power Wide-Area Networks (LPWANs) have become fundamental to the Internet of Things (IoT), with NB-IoT (Narrowband Internet of Things) standing out due to its seamless integration with cellular infrastructure, enhanced coverage, and support for dense deployments. Despite its commercial proliferation, SDR-based physical layer (PHY) exploration for NB-IoT remains limited, particularly in addressing unique complexities such as narrowband signal processing, cellular-specific synchronization sequences, and stringent link budget requirements. This paper bridges this gap by presenting a comprehensive tutorial on SDR-based NB-IoT PHY implementation, focusing on three pillars: robust time-frequency synchronization under severe fading and interference, efficient channel estimation for coherent detection, and experimental performance validation in real-world scenarios. We introduce a first-of-its-kind end-to-end SDR implementation supporting both single-tone and multi-tone transmissions, leveraging commercial off-the-shelf (COTS) platforms. Our novel signal processing workflow achieves synchronization through NPSS-based auto-correlation and NSSS-driven cell-ID detection while incorporating CFO estimation and compensation to mitigate oscillator mismatches. For uplink processing, we detail preamble detection and demodulation, addressing coverage enhancement (CE) levels and adaptive subcarrier spacing configurations. Extensive experiments conducted in both indoor (LOS/NLOS) and outdoor environments demonstrate reliable performance, with Bit Error Rate (BER) and Block Error Rate (BLER) metrics validating resilience under varying repetition counts and propagation conditions. The tutorial offers actionable insights for optimizing PHY-layer design, validated against 3GPP specifications, and lays the foundation for next-generation NB-IoT systems in emerging applications, such as smart cities and industrial automation.
{"title":"A Tutorial on SDR-Based NB-IoT PHY: Synchronization, Demodulation, and Validation","authors":"Jingze Zheng;Zhiguo Shi;Xiuzhen Guo;Shibo He;Chaojie Gu;Jiming Chen","doi":"10.1109/COMST.2026.3654924","DOIUrl":"10.1109/COMST.2026.3654924","url":null,"abstract":"Low-Power Wide-Area Networks (LPWANs) have become fundamental to the Internet of Things (IoT), with NB-IoT (Narrowband Internet of Things) standing out due to its seamless integration with cellular infrastructure, enhanced coverage, and support for dense deployments. Despite its commercial proliferation, SDR-based physical layer (PHY) exploration for NB-IoT remains limited, particularly in addressing unique complexities such as narrowband signal processing, cellular-specific synchronization sequences, and stringent link budget requirements. This paper bridges this gap by presenting a comprehensive tutorial on SDR-based NB-IoT PHY implementation, focusing on three pillars: robust time-frequency synchronization under severe fading and interference, efficient channel estimation for coherent detection, and experimental performance validation in real-world scenarios. We introduce a first-of-its-kind end-to-end SDR implementation supporting both single-tone and multi-tone transmissions, leveraging commercial off-the-shelf (COTS) platforms. Our novel signal processing workflow achieves synchronization through NPSS-based auto-correlation and NSSS-driven cell-ID detection while incorporating CFO estimation and compensation to mitigate oscillator mismatches. For uplink processing, we detail preamble detection and demodulation, addressing coverage enhancement (CE) levels and adaptive subcarrier spacing configurations. Extensive experiments conducted in both indoor (LOS/NLOS) and outdoor environments demonstrate reliable performance, with Bit Error Rate (BER) and Block Error Rate (BLER) metrics validating resilience under varying repetition counts and propagation conditions. The tutorial offers actionable insights for optimizing PHY-layer design, validated against 3GPP specifications, and lays the foundation for next-generation NB-IoT systems in emerging applications, such as smart cities and industrial automation.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4458-4484"},"PeriodicalIF":34.4,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/COMST.2026.3651702
Ruichen Zhang;Guangyuan Liu;Yinqiu Liu;Changyuan Zhao;Jiacheng Wang;Yunting Xu;Dusit Niyato;Jiawen Kang;Yonghui Li;Shiwen Mao;Sumei Sun;Xuemin Shen;Dong In Kim
The rapid expansion of sixth-generation (6G) wireless networks and the Internet of Things (IoT) has catalyzed the evolution from centralized cloud intelligence towards decentralized edge general intelligence. However, traditional edge intelligence methods, characterized by static models and limited cognitive autonomy, fail to address the dynamic, heterogeneous, and resource-constrained scenarios inherent to emerging edge networks. Agentic artificial intelligence (Agentic AI) emerges as a transformative solution, enabling edge systems to autonomously perceive multi-modal environments, reason contextually, and adapt proactively through continuous perception–reasoning–action loops. In this context, the agentification of edge intelligence serves as a key paradigm shift, where distributed entities evolve into autonomous agents capable of collaboration and continual adaptation. This paper presents a comprehensive survey dedicated to Agentic AI and agentification frameworks tailored explicitly for edge general intelligence. First, we systematically introduce foundational concepts and clarify distinctions from traditional edge intelligence paradigms. Second, we analyze important enabling technologies, including compact model compression, energy-aware computing strategies, robust connectivity frameworks, and advanced knowledge representation and reasoning mechanisms. Third, we provide representative case studies demonstrating Agentic AI’s capabilities in low-altitude economy networks, intent-driven networking, vehicular networks, and human-centric service provisioning, supported by numerical evaluations. Furthermore, we identify current research challenges, review emerging open-source platforms, and highlight promising future research directions to guide robust, scalable, and trustworthy Agentic AI deployments for next-generation edge environments.
{"title":"Toward Edge General Intelligence With Agentic AI and Agentification: Concepts, Technologies, and Future Directions","authors":"Ruichen Zhang;Guangyuan Liu;Yinqiu Liu;Changyuan Zhao;Jiacheng Wang;Yunting Xu;Dusit Niyato;Jiawen Kang;Yonghui Li;Shiwen Mao;Sumei Sun;Xuemin Shen;Dong In Kim","doi":"10.1109/COMST.2026.3651702","DOIUrl":"10.1109/COMST.2026.3651702","url":null,"abstract":"The rapid expansion of sixth-generation (6G) wireless networks and the Internet of Things (IoT) has catalyzed the evolution from centralized cloud intelligence towards decentralized edge general intelligence. However, traditional edge intelligence methods, characterized by static models and limited cognitive autonomy, fail to address the dynamic, heterogeneous, and resource-constrained scenarios inherent to emerging edge networks. Agentic artificial intelligence (Agentic AI) emerges as a transformative solution, enabling edge systems to autonomously perceive multi-modal environments, reason contextually, and adapt proactively through continuous perception–reasoning–action loops. In this context, the agentification of edge intelligence serves as a key paradigm shift, where distributed entities evolve into autonomous agents capable of collaboration and continual adaptation. This paper presents a comprehensive survey dedicated to Agentic AI and agentification frameworks tailored explicitly for edge general intelligence. First, we systematically introduce foundational concepts and clarify distinctions from traditional edge intelligence paradigms. Second, we analyze important enabling technologies, including compact model compression, energy-aware computing strategies, robust connectivity frameworks, and advanced knowledge representation and reasoning mechanisms. Third, we provide representative case studies demonstrating Agentic AI’s capabilities in low-altitude economy networks, intent-driven networking, vehicular networks, and human-centric service provisioning, supported by numerical evaluations. Furthermore, we identify current research challenges, review emerging open-source platforms, and highlight promising future research directions to guide robust, scalable, and trustworthy Agentic AI deployments for next-generation edge environments.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4285-4318"},"PeriodicalIF":34.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The acquisition of channel state information (CSI) plays a vital role in enhancing the performance of sixth-generation (6G) wireless communication systems. Conventional channel estimation approaches encounter significant scalability limitations in emerging scenarios, such as high-mobility environments, extremely large-scale multiple-input multiple-output (XL-MIMO) configurations, and multi-band operations, where pilot overhead grows dramatically. CSI extrapolation offers an effective solution to these issues by leveraging limited or partial CSI measurements to reconstruct or predict the full CSI, thereby substantially lowering the required overhead without compromising accuracy. Artificial intelligence (AI) has emerged as a powerful tool to advance CSI extrapolation, enabling more accurate and efficient inference across diverse channel conditions. Although research in this area is expanding rapidly, the literature still lacks a thorough and unified survey that synthesizes the latest developments in AI-based CSI extrapolation methods. This paper aims to bride this gap by providing the first comprehensive review of AI-driven CSI extrapolation techniques, covering their current state, key limitations, and promising research avenues. We begin by outlining the foundational aspects of AI-driven CSI extrapolation. This includes essential wireless channel properties that influence extrapolation performance and an overview of the most commonly employed AI architectures suited to this task. Building on these basics, we systematically examine the major categories of extrapolation approaches, both traditional model-based and modern AI-enhanced ones, across the primary domains: time, frequency, antenna, and multi-domain scenarios. For each category, we highlight representative techniques, their underlying principles, strengths, and limitations, along with distilled insights from comparative studies. Recognizing the strong potential of AI-based methods to satisfy the demanding performance targets of future systems, we also review publicly available open channel datasets and channel simulators that support the development and benchmarking of robust AI-driven extrapolation models. Finally, we identify persistent challenges in the field, and outline forward-looking research directions to guide future progress toward practical deployment in 6G networks.
{"title":"AI-Driven Channel State Information (CSI) Extrapolation for 6G: Current Situations, Challenges, and Future Research","authors":"Yuan Gao;Zichen Lu;Xinyi Wu;Wenjun Yu;Shengli Liu;Jianbo Du;Yanliang Jin;Shunqing Zhang;Xiaoli Chu;Shugong Xu","doi":"10.1109/COMST.2026.3652799","DOIUrl":"10.1109/COMST.2026.3652799","url":null,"abstract":"The acquisition of channel state information (CSI) plays a vital role in enhancing the performance of sixth-generation (6G) wireless communication systems. Conventional channel estimation approaches encounter significant scalability limitations in emerging scenarios, such as high-mobility environments, extremely large-scale multiple-input multiple-output (XL-MIMO) configurations, and multi-band operations, where pilot overhead grows dramatically. CSI extrapolation offers an effective solution to these issues by leveraging limited or partial CSI measurements to reconstruct or predict the full CSI, thereby substantially lowering the required overhead without compromising accuracy. Artificial intelligence (AI) has emerged as a powerful tool to advance CSI extrapolation, enabling more accurate and efficient inference across diverse channel conditions. Although research in this area is expanding rapidly, the literature still lacks a thorough and unified survey that synthesizes the latest developments in AI-based CSI extrapolation methods. This paper aims to bride this gap by providing the first comprehensive review of AI-driven CSI extrapolation techniques, covering their current state, key limitations, and promising research avenues. We begin by outlining the foundational aspects of AI-driven CSI extrapolation. This includes essential wireless channel properties that influence extrapolation performance and an overview of the most commonly employed AI architectures suited to this task. Building on these basics, we systematically examine the major categories of extrapolation approaches, both traditional model-based and modern AI-enhanced ones, across the primary domains: time, frequency, antenna, and multi-domain scenarios. For each category, we highlight representative techniques, their underlying principles, strengths, and limitations, along with distilled insights from comparative studies. Recognizing the strong potential of AI-based methods to satisfy the demanding performance targets of future systems, we also review publicly available open channel datasets and channel simulators that support the development and benchmarking of robust AI-driven extrapolation models. Finally, we identify persistent challenges in the field, and outline forward-looking research directions to guide future progress toward practical deployment in 6G networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4485-4518"},"PeriodicalIF":34.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/COMST.2026.3651990
Yang Lu;Shengli Zhang;Chang Liu;Ruichen Zhang;Bo Ai;Dusit Niyato;Wei Ni;Xianbin Wang;Abbas Jamalipour
The rapid advancement of communication technologies has driven the evolution of communication networks toward both high-dimensional resource utilization and multifunctional integration. This evolving complexity poses significant challenges in designing communication networks to satisfy the growing quality-of-service and time sensitivity of mobile applications in dynamic environments. Graph neural networks (GNNs) have emerged as fundamental deep learning (DL) models for complex communication networks. Most existing GNNs are task-specific, whereas end-to-end communication performance hinges on multi-step inference. To address this gap, this article proposes to leverage agentic artificial intelligence (AI) to orchestrate and integrate diverse GNNs, thereby forming a novel framework termed agentic GNNs. This framework enables application-aware implementations, facilitating the advancement of edge general intelligence. Regarding the core roles of GNNs in the framework, we comprehensively review recent advances in GNN-based applications for wireless communications and networking, aiming to fully understand the comprehensive capabilities of GNNs. Specifically, we focus on the alignment between graph representations and network topologies, as well as between neural architectures and communication tasks. We first provide an overview of GNNs based on prominent neural architectures, followed by the concept of agentic GNNs. Then, we summarize and compare GNN applications for conventional systems and emerging technologies, including physical, MAC, and network layer designs, integrated sensing and communication (ISAC), reconfigurable intelligent surface (RIS) and cell-free network architecture. We further propose a large language model (LLM) framework as an intelligent question-answering agent, leveraging this survey as a local knowledge base to enable GNN-related responses tailored to wireless communication research. Moreover, we present several experimental results to quantify the effectiveness of GNNs across various scenarios. Finally, we highlight the critical challenges, open issues, and future research directions for GNN-empowered wireless communication designs.
{"title":"Agentic Graph Neural Networks for Wireless Communications and Networking Toward Edge General Intelligence: A Survey","authors":"Yang Lu;Shengli Zhang;Chang Liu;Ruichen Zhang;Bo Ai;Dusit Niyato;Wei Ni;Xianbin Wang;Abbas Jamalipour","doi":"10.1109/COMST.2026.3651990","DOIUrl":"10.1109/COMST.2026.3651990","url":null,"abstract":"The rapid advancement of communication technologies has driven the evolution of communication networks toward both high-dimensional resource utilization and multifunctional integration. This evolving complexity poses significant challenges in designing communication networks to satisfy the growing quality-of-service and time sensitivity of mobile applications in dynamic environments. Graph neural networks (GNNs) have emerged as fundamental deep learning (DL) models for complex communication networks. Most existing GNNs are task-specific, whereas end-to-end communication performance hinges on multi-step inference. To address this gap, this article proposes to leverage agentic artificial intelligence (AI) to orchestrate and integrate diverse GNNs, thereby forming a novel framework termed agentic GNNs. This framework enables application-aware implementations, facilitating the advancement of edge general intelligence. Regarding the core roles of GNNs in the framework, we comprehensively review recent advances in GNN-based applications for wireless communications and networking, aiming to fully understand the comprehensive capabilities of GNNs. Specifically, we focus on the alignment between graph representations and network topologies, as well as between neural architectures and communication tasks. We first provide an overview of GNNs based on prominent neural architectures, followed by the concept of agentic GNNs. Then, we summarize and compare GNN applications for conventional systems and emerging technologies, including physical, MAC, and network layer designs, integrated sensing and communication (ISAC), reconfigurable intelligent surface (RIS) and cell-free network architecture. We further propose a large language model (LLM) framework as an intelligent question-answering agent, leveraging this survey as a local knowledge base to enable GNN-related responses tailored to wireless communication research. Moreover, we present several experimental results to quantify the effectiveness of GNNs across various scenarios. Finally, we highlight the critical challenges, open issues, and future research directions for GNN-empowered wireless communication designs.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4519-4554"},"PeriodicalIF":34.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-05DOI: 10.1109/COMST.2025.3650568
Qianglong Dai;Yong Zeng;Huizhi Wang;Changsheng You;Chao Zhou;Hongqiang Cheng;Xiaoli Xu;Shi Jin;A. Lee Swindlehurst;Yonina C. Eldar;Robert Schober;Rui Zhang;Xiaohu You
Integrated sensing and communication (ISAC) is one of the key usage scenarios for future sixth-generation (6G) mobile communication networks, where communication and sensing (C&S) services are simultaneously provided through shared wireless spectrum, signal processing modules, hardware, and network infrastructure. Such an integration is strengthened by the technology trends in 6G, such as denser network nodes, larger antenna arrays, wider bandwidths, higher frequency bands, and more efficient utilization of spectrum and hardware resources, which incentivize and empower enhanced sensing capabilities. Moreover, emerging applications such as Internet-of-Everything (IoE), autonomous ground and aerial vehicles, virtual reality/augmented reality (VR/AR), and connected intelligence have intensified the demands for both high-quality C&S services, accelerating the development and implementation of ISAC in wireless networks. As in contemporary communication systems, orthogonal frequency-division multiplexing (OFDM) is expected to be the dominant waveform for ISAC, motivating the need for study of both the potential benefits and challenges of OFDM ISAC. Thus, this paper aims to provide a comprehensive tutorial overview of ISAC systems enabled by large-scale multi-input multi-output (MIMO) and OFDM technologies and discuss their fundamental principles, advantages, and enabling signal processing methods. To this end, a unified MIMO-OFDM ISAC system model is first introduced, followed by four frameworks for estimating parameters across the spatial, delay, and Doppler domains, including parallel one-domain, sequential one-domain, joint two-domain, and joint three-domain parameter estimation. Next, sensing algorithms and performance analysis are presented in detail for far-field scenarios where uniform plane wave (UPW) propagation is valid, followed by extensions to near-field scenarios where uniform spherical wave (USW) characteristics must be considered. Finally, the paper presents open challenges and outlines promising avenues for future research on MIMO-OFDM ISAC.
{"title":"A Tutorial on MIMO-OFDM ISAC: From Far-Field to Near-Field","authors":"Qianglong Dai;Yong Zeng;Huizhi Wang;Changsheng You;Chao Zhou;Hongqiang Cheng;Xiaoli Xu;Shi Jin;A. Lee Swindlehurst;Yonina C. Eldar;Robert Schober;Rui Zhang;Xiaohu You","doi":"10.1109/COMST.2025.3650568","DOIUrl":"10.1109/COMST.2025.3650568","url":null,"abstract":"Integrated sensing and communication (ISAC) is one of the key usage scenarios for future sixth-generation (6G) mobile communication networks, where communication and sensing (C&S) services are simultaneously provided through shared wireless spectrum, signal processing modules, hardware, and network infrastructure. Such an integration is strengthened by the technology trends in 6G, such as denser network nodes, larger antenna arrays, wider bandwidths, higher frequency bands, and more efficient utilization of spectrum and hardware resources, which incentivize and empower enhanced sensing capabilities. Moreover, emerging applications such as Internet-of-Everything (IoE), autonomous ground and aerial vehicles, virtual reality/augmented reality (VR/AR), and connected intelligence have intensified the demands for both high-quality C&S services, accelerating the development and implementation of ISAC in wireless networks. As in contemporary communication systems, orthogonal frequency-division multiplexing (OFDM) is expected to be the dominant waveform for ISAC, motivating the need for study of both the potential benefits and challenges of OFDM ISAC. Thus, this paper aims to provide a comprehensive tutorial overview of ISAC systems enabled by large-scale multi-input multi-output (MIMO) and OFDM technologies and discuss their fundamental principles, advantages, and enabling signal processing methods. To this end, a unified MIMO-OFDM ISAC system model is first introduced, followed by four frameworks for estimating parameters across the spatial, delay, and Doppler domains, including parallel one-domain, sequential one-domain, joint two-domain, and joint three-domain parameter estimation. Next, sensing algorithms and performance analysis are presented in detail for far-field scenarios where uniform plane wave (UPW) propagation is valid, followed by extensions to near-field scenarios where uniform spherical wave (USW) characteristics must be considered. Finally, the paper presents open challenges and outlines promising avenues for future research on MIMO-OFDM ISAC.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4319-4358"},"PeriodicalIF":34.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-30DOI: 10.1109/COMST.2025.3649707
Chengyang Liang;Dong Li
The rapid advancement of generative artificial intelligence (GenAI) has introduced novel opportunities for semantic communication (SemCom) systems. This survey offers a comprehensive overview of GenAI-enabled SemCom, connecting theoretical foundations with practical applications. Initially, we introduce the fundamental concepts of SemCom and explore how generative models augment traditional communication paradigms. The paper systematically reviews state-of-the-art methodologies, including variational autoencoders, generative adversarial networks, diffusion models, and other GenAI frameworks within SemCom contexts. We classify GenAI in SemCom based on its GenAI architecture, communication modality, and application tasks. Additionally, we present detailed case studies that demonstrate real-world applications in smart healthcare, intelligent transportation systems, and smart agriculture. These case studies exemplify how generative SemCom can fulfill semantic tasks while preserving the communication fidelity. Finally, we identify emerging research directions and discuss open challenges that merit further investigation. This survey constitutes a valuable resource for researchers and practitioners aiming to comprehend and implement GenAI techniques in next-generation communication systems.
{"title":"Generative AI-Enabled Semantic Communication: State-of-the-Art, Applications, and the Way Ahead","authors":"Chengyang Liang;Dong Li","doi":"10.1109/COMST.2025.3649707","DOIUrl":"10.1109/COMST.2025.3649707","url":null,"abstract":"The rapid advancement of generative artificial intelligence (GenAI) has introduced novel opportunities for semantic communication (SemCom) systems. This survey offers a comprehensive overview of GenAI-enabled SemCom, connecting theoretical foundations with practical applications. Initially, we introduce the fundamental concepts of SemCom and explore how generative models augment traditional communication paradigms. The paper systematically reviews state-of-the-art methodologies, including variational autoencoders, generative adversarial networks, diffusion models, and other GenAI frameworks within SemCom contexts. We classify GenAI in SemCom based on its GenAI architecture, communication modality, and application tasks. Additionally, we present detailed case studies that demonstrate real-world applications in smart healthcare, intelligent transportation systems, and smart agriculture. These case studies exemplify how generative SemCom can fulfill semantic tasks while preserving the communication fidelity. Finally, we identify emerging research directions and discuss open challenges that merit further investigation. This survey constitutes a valuable resource for researchers and practitioners aiming to comprehend and implement GenAI techniques in next-generation communication systems.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"3976-4015"},"PeriodicalIF":34.4,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-30DOI: 10.1109/COMST.2025.3649735
Yalçın Ata;Farah Mahdi Al-Sallami;Muhsin Caner Gökçe;Anna Maria Vegni;Sujan Rajbhandari;Yahya Baykal
Optical Wireless Communication Systems (OWCSs) are becoming more popular each day, especially after numerous mobile applications are being employed within the concept of Internet of Things (IoT). OWCSs are largely used in both terrestrial and non-terrestrial environments, like underwater, air, and space scenarios. Due to the large applicability of OWCS, it represents one of the main candidate technologies for the future 6G wireless communication systems. Naturally, this market trend forces the system designers to reach the best performance in their designs, as well as optimize the cost. In this survey paper, we intend to provide information to the researchers working in this field on the statistical models adopted in OWCS, the methods and techniques used to improve their performances, mainly in outdoor environment like air, space, and underwater. In this respect, the background on theoretical aspects of OWCS, together with their benefits, limitations and challenges are presented. Performance improvement techniques employed in OWCSs, such as power increase, partial coherence, beamforming, aperture averaging, spatial diversity, and intelligent reflecting surfaces, are also introduced. Finally, we discuss the open challenges that researchers are still facing, together with future directions on next steps for a large-scale adoption of OWCS.
{"title":"Optical Wireless Communication in Atmosphere and Underwater: Statistical Models, Improvement Techniques, and Recent Applications","authors":"Yalçın Ata;Farah Mahdi Al-Sallami;Muhsin Caner Gökçe;Anna Maria Vegni;Sujan Rajbhandari;Yahya Baykal","doi":"10.1109/COMST.2025.3649735","DOIUrl":"10.1109/COMST.2025.3649735","url":null,"abstract":"Optical Wireless Communication Systems (OWCSs) are becoming more popular each day, especially after numerous mobile applications are being employed within the concept of Internet of Things (IoT). OWCSs are largely used in both terrestrial and non-terrestrial environments, like underwater, air, and space scenarios. Due to the large applicability of OWCS, it represents one of the main candidate technologies for the future 6G wireless communication systems. Naturally, this market trend forces the system designers to reach the best performance in their designs, as well as optimize the cost. In this survey paper, we intend to provide information to the researchers working in this field on the statistical models adopted in OWCS, the methods and techniques used to improve their performances, mainly in outdoor environment like air, space, and underwater. In this respect, the background on theoretical aspects of OWCS, together with their benefits, limitations and challenges are presented. Performance improvement techniques employed in OWCSs, such as power increase, partial coherence, beamforming, aperture averaging, spatial diversity, and intelligent reflecting surfaces, are also introduced. Finally, we discuss the open challenges that researchers are still facing, together with future directions on next steps for a large-scale adoption of OWCS.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4248-4284"},"PeriodicalIF":34.4,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318578","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1109/COMST.2025.3647980
Seid Koudia;Leonardo Oleynik;Mert Bayraktar;Junaid Ur Rehman;Symeon Chatzinotas
Quantum communication systems support unique applications in the form of distributed quantum computing, distributed quantum sensing, and several cryptographic protocols. The main enabler in these communication systems is an efficient infrastructure that is capable to transport unknown quantum states with high rate and fidelity. This feat requires a new approach to communication system design which efficiently exploits the available physical layer resources, while respecting the limitations and principles of quantum information. Despite the fundamental differences between the classic and quantum worlds, there exist universal communication concepts that may proven beneficial in quantum communication systems as well. In this survey, the distinctive aspects of physical layer quantum communications are highlighted in a attempt to draw commonalities and divergences between classic and quantum communications. More specifically, we begin by overviewing the quantum channels and use cases over diverse optical propagation media, shedding light on the concepts of crosstalk and interference. Subsequently, we survey quantum sources, detectors, channels and modulation techniques. More importantly, we discuss and analyze spatial multiplexing techniques, such as coherent control, multiplexing, diversity and MIMO. Finally, we identify synergies between the two communication technologies and grand open challenges that can be pivotal in the development of next-generation quantum communication systems.
{"title":"Physical-Layer Aspects of Quantum Communications: A Survey","authors":"Seid Koudia;Leonardo Oleynik;Mert Bayraktar;Junaid Ur Rehman;Symeon Chatzinotas","doi":"10.1109/COMST.2025.3647980","DOIUrl":"10.1109/COMST.2025.3647980","url":null,"abstract":"Quantum communication systems support unique applications in the form of distributed quantum computing, distributed quantum sensing, and several cryptographic protocols. The main enabler in these communication systems is an efficient infrastructure that is capable to transport unknown quantum states with high rate and fidelity. This feat requires a new approach to communication system design which efficiently exploits the available physical layer resources, while respecting the limitations and principles of quantum information. Despite the fundamental differences between the classic and quantum worlds, there exist universal communication concepts that may proven beneficial in quantum communication systems as well. In this survey, the distinctive aspects of physical layer quantum communications are highlighted in a attempt to draw commonalities and divergences between classic and quantum communications. More specifically, we begin by overviewing the quantum channels and use cases over diverse optical propagation media, shedding light on the concepts of crosstalk and interference. Subsequently, we survey quantum sources, detectors, channels and modulation techniques. More importantly, we discuss and analyze spatial multiplexing techniques, such as coherent control, multiplexing, diversity and MIMO. Finally, we identify synergies between the two communication technologies and grand open challenges that can be pivotal in the development of next-generation quantum communication systems.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4429-4457"},"PeriodicalIF":34.4,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11317988","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1109/COMST.2025.3648785
Han Zhang;Mohammad Farzanullah;Mohammad Ghassemi;Akram Bin Sediq;Ali Afana;Melike Erol-Kantarci
Foundation models (FMs) are recognized as a transformative breakthrough that has started to reshape the future of artificial intelligence (AI) across both academia and industry. The integration of FMs into wireless networks is expected to enable the development of general-purpose AI agents capable of handling diverse network management requests and highly complex wireless-related tasks involving multi-modal data. Inspired by these ideas, this work discusses the utilization of FMs, especially multi-modal FMs in wireless networks. We focus on two important types of tasks in wireless network management: prediction tasks and control tasks. In particular, we first discuss FMs-enabled multi-modal contextual information understanding in wireless networks. Then, we explain how FMs can be applied to prediction and control tasks, respectively. Following this, we introduce the development of wireless-specific FMs from two perspectives: available datasets for development and the methodologies used. Finally, we conclude with a discussion of the challenges and future directions for FM-enhanced wireless networks.
{"title":"Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey","authors":"Han Zhang;Mohammad Farzanullah;Mohammad Ghassemi;Akram Bin Sediq;Ali Afana;Melike Erol-Kantarci","doi":"10.1109/COMST.2025.3648785","DOIUrl":"10.1109/COMST.2025.3648785","url":null,"abstract":"Foundation models (FMs) are recognized as a transformative breakthrough that has started to reshape the future of artificial intelligence (AI) across both academia and industry. The integration of FMs into wireless networks is expected to enable the development of general-purpose AI agents capable of handling diverse network management requests and highly complex wireless-related tasks involving multi-modal data. Inspired by these ideas, this work discusses the utilization of FMs, especially multi-modal FMs in wireless networks. We focus on two important types of tasks in wireless network management: prediction tasks and control tasks. In particular, we first discuss FMs-enabled multi-modal contextual information understanding in wireless networks. Then, we explain how FMs can be applied to prediction and control tasks, respectively. Following this, we introduce the development of wireless-specific FMs from two perspectives: available datasets for development and the methodologies used. Finally, we conclude with a discussion of the challenges and future directions for FM-enhanced wireless networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4359-4393"},"PeriodicalIF":34.4,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wireless positioning technologies hold significant value for applications in autonomous driving, extended reality (XR), uncrewed aerial vehicles (UAVs), and more. With the advancement of artificial intelligence (AI), leveraging AI to enhance positioning accuracy and robustness has emerged as a field full of potential. Driven by the requirements and functionalities defined in the 3rd Generation Partnership Project (3GPP) standards, AI/machine learning (ML)-based cellular positioning is becoming a key technology to overcome the limitations of traditional methods. This paper presents a comprehensive survey of AI-driven cellular positioning. We begin by reviewing the fundamentals of wireless positioning and AI models, analyzing their respective challenges and synergies. We provide a comprehensive review of the evolution of 3GPP positioning standards, with a focus on the integration of AI/ML in current and upcoming standard releases. Guided by the 3GPP-defined taxonomy, we categorize and summarize state-of-the-art (SOTA) research into two major classes: AI/ML-assisted positioning and direct AI/ML-based positioning. The former includes line-of-sight (LOS)/non-line-of-sight (NLOS) detection, time of arrival (TOA)/time difference of arrival (TDOA) estimation, and angle prediction; the latter encompasses fingerprinting, knowledge-assisted learning, and channel charting. Furthermore, we review representative public datasets and conduct performance evaluations of AI-based positioning algorithms using these datasets. Finally, we conclude by summarizing the challenges and opportunities of AI-driven wireless positioning.
{"title":"AI-Driven Wireless Positioning: Fundamentals, Standards, State-of-the-Art, and Challenges","authors":"Guangjin Pan;Yuan Gao;Yilin Gao;Wenjun Yu;Zhiyong Zhong;Xiaoyu Yang;Xinyu Guo;Shugong Xu","doi":"10.1109/COMST.2025.3648577","DOIUrl":"10.1109/COMST.2025.3648577","url":null,"abstract":"Wireless positioning technologies hold significant value for applications in autonomous driving, extended reality (XR), uncrewed aerial vehicles (UAVs), and more. With the advancement of artificial intelligence (AI), leveraging AI to enhance positioning accuracy and robustness has emerged as a field full of potential. Driven by the requirements and functionalities defined in the 3rd Generation Partnership Project (3GPP) standards, AI/machine learning (ML)-based cellular positioning is becoming a key technology to overcome the limitations of traditional methods. This paper presents a comprehensive survey of AI-driven cellular positioning. We begin by reviewing the fundamentals of wireless positioning and AI models, analyzing their respective challenges and synergies. We provide a comprehensive review of the evolution of 3GPP positioning standards, with a focus on the integration of AI/ML in current and upcoming standard releases. Guided by the 3GPP-defined taxonomy, we categorize and summarize state-of-the-art (SOTA) research into two major classes: AI/ML-assisted positioning and direct AI/ML-based positioning. The former includes line-of-sight (LOS)/non-line-of-sight (NLOS) detection, time of arrival (TOA)/time difference of arrival (TDOA) estimation, and angle prediction; the latter encompasses fingerprinting, knowledge-assisted learning, and channel charting. Furthermore, we review representative public datasets and conduct performance evaluations of AI-based positioning algorithms using these datasets. Finally, we conclude by summarizing the challenges and opportunities of AI-driven wireless positioning.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4394-4428"},"PeriodicalIF":34.4,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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