Pub Date : 2026-06-01Epub Date: 2026-07-10DOI: 10.23919/JCIN.2026.11604010
Jintao Wang;Pingping Zhang;Chengzhi Ma;Chengwang Ji;Zheng Shi;Guanghua Yang;Shaodan Ma
Reconfigurable distributed antennas and reflecting surface (RDARS) has emerged as a transformative solution to address the stringent requirements of future wireless networks. By combining distributed active antennas with reconfigurable passive reflecting surfaces, RDARS integrates the advantages of both active transmission and passive wave control in a cost-effective and energy-efficient manner. This hybrid architecture enables enhanced coverage, improved spectral efficiency, and seamless support for integrated communication and sensing. In this article, we first introduce the fundamental architecture and working principles of RDARS, followed by practical benefits and comparisons with recently proposed intelligent surface variants. We then highlight the signal to noise ratio (SNR) gains in representative applications of RDARS-aided communication and sensing scenarios, where RDARS demonstrates clear advantages over conventional reconfigurable intelligent surfaces. Finally, we outline key challenges related to practical implementation and resource allocation, and discuss potential research directions. With its unique hybrid mode synergy, RDARS is envisioned to play a pivotal role in shaping the evolution of next-generation intelligent communication systems.
{"title":"A New Paradigm towards Reconfigurable Environment: Reconfigurable Distributed Antennas and Reflecting Surface","authors":"Jintao Wang;Pingping Zhang;Chengzhi Ma;Chengwang Ji;Zheng Shi;Guanghua Yang;Shaodan Ma","doi":"10.23919/JCIN.2026.11604010","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11604010","url":null,"abstract":"Reconfigurable distributed antennas and reflecting surface (RDARS) has emerged as a transformative solution to address the stringent requirements of future wireless networks. By combining distributed active antennas with reconfigurable passive reflecting surfaces, RDARS integrates the advantages of both active transmission and passive wave control in a cost-effective and energy-efficient manner. This hybrid architecture enables enhanced coverage, improved spectral efficiency, and seamless support for integrated communication and sensing. In this article, we first introduce the fundamental architecture and working principles of RDARS, followed by practical benefits and comparisons with recently proposed intelligent surface variants. We then highlight the signal to noise ratio (SNR) gains in representative applications of RDARS-aided communication and sensing scenarios, where RDARS demonstrates clear advantages over conventional reconfigurable intelligent surfaces. Finally, we outline key challenges related to practical implementation and resource allocation, and discuss potential research directions. With its unique hybrid mode synergy, RDARS is envisioned to play a pivotal role in shaping the evolution of next-generation intelligent communication systems.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 2","pages":"219-230"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11604010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443346","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 : 2026-03-01Epub Date: 2026-04-02DOI: 10.23919/JCIN.2026.11474260
Shuqi Liu;Jiawei Ji;Yulong Shang;Cuimei Cui;Shuyan Xiao;Lei Zhang;Jianjie Tian;Zhigang Sun
This paper investigates the maximization problem of secrecy rate in underlay cognitive radio networks (CRNs) with the aid of reconfigurable intelligent surface (RIS). The secure communication method is studied, which takes into account the underlay spectrum access mode, the characteristics of RIS, and the threat of eavesdropping user to cognitive network communication. Using convex optimization theory, the secrecy rate of cognitive networks is maximized by jointly optimizing cognitive base station (CBS) beamforming and RIS phase shifts. Based on the proposed optimization and analyzing method, the impact of network parameters on the secrecy rate is analyzed in depth, and the security of cognitive networks is discussed. Theoretical analysis and simulation have shown that the proposed method can significantly improve the security of cognitive networks. With the aid of RIS, the secrecy rate is greatly improved compared to the case without RIS. Further, the secrecy rate is improved even more through optimizing RIS phase shifts and CBS beamforming.
{"title":"Optimization of Secrecy Rate Aided by RIS in Underlay Cognitive Networks","authors":"Shuqi Liu;Jiawei Ji;Yulong Shang;Cuimei Cui;Shuyan Xiao;Lei Zhang;Jianjie Tian;Zhigang Sun","doi":"10.23919/JCIN.2026.11474260","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11474260","url":null,"abstract":"This paper investigates the maximization problem of secrecy rate in underlay cognitive radio networks (CRNs) with the aid of reconfigurable intelligent surface (RIS). The secure communication method is studied, which takes into account the underlay spectrum access mode, the characteristics of RIS, and the threat of eavesdropping user to cognitive network communication. Using convex optimization theory, the secrecy rate of cognitive networks is maximized by jointly optimizing cognitive base station (CBS) beamforming and RIS phase shifts. Based on the proposed optimization and analyzing method, the impact of network parameters on the secrecy rate is analyzed in depth, and the security of cognitive networks is discussed. Theoretical analysis and simulation have shown that the proposed method can significantly improve the security of cognitive networks. With the aid of RIS, the secrecy rate is greatly improved compared to the case without RIS. Further, the secrecy rate is improved even more through optimizing RIS phase shifts and CBS beamforming.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 1","pages":"110-119"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588267","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}
Pub Date : 2026-03-01Epub Date: 2026-04-02DOI: 10.23919/JCIN.2026.11474252
Peizhe Li;Sumei Sun;Gary C. F. Lee;Cailian Chen;Shanying Zhu;Xinping Guan;Gerhard P. Fettweis
The integration of wireless communication and state estimation has become a fundamental enabler for large-scale industrial Internet of things (IloT) systems, where estimation, transmission, and control are tightly coupled across heterogeneous networks. This survey provides an overview of recent advances in state estimation and communication co-design, highlighting the evolution from isolated subsystem optimization toward unified frameworks that jointly address estimation accuracy, communication efficiency, and scalability. We first review theoretical foundations that characterize how data rate and packet loss constrain estimation stability, introducing the key results such as data-rate theorem and critical loss rate for estimation convergence. Next, we discuss the scalability perspective, addressing the horizontal expansion through multihop relaying to overcome transmission distance limitations, and the vertical expansion through multi-sensor fusion to address the limited observation range of individual sensors. At the network level, we discuss layered design methodologies across the application, transport, medium access control (MAC), and physical layers to ensure estimation performance. Subsequently, we examine control-oriented co-design paradigms, including separation principle-based designs, joint optimization without separation, and learning-based frameworks that integrate estimation, communication, and control in a unified manner. Finally, we discuss several inspiring future research directions.
{"title":"State Estimation and Communication Co-Design in IIoT Systems: A Brief Survey","authors":"Peizhe Li;Sumei Sun;Gary C. F. Lee;Cailian Chen;Shanying Zhu;Xinping Guan;Gerhard P. Fettweis","doi":"10.23919/JCIN.2026.11474252","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11474252","url":null,"abstract":"The integration of wireless communication and state estimation has become a fundamental enabler for large-scale industrial Internet of things (IloT) systems, where estimation, transmission, and control are tightly coupled across heterogeneous networks. This survey provides an overview of recent advances in state estimation and communication co-design, highlighting the evolution from isolated subsystem optimization toward unified frameworks that jointly address estimation accuracy, communication efficiency, and scalability. We first review theoretical foundations that characterize how data rate and packet loss constrain estimation stability, introducing the key results such as data-rate theorem and critical loss rate for estimation convergence. Next, we discuss the scalability perspective, addressing the horizontal expansion through multihop relaying to overcome transmission distance limitations, and the vertical expansion through multi-sensor fusion to address the limited observation range of individual sensors. At the network level, we discuss layered design methodologies across the application, transport, medium access control (MAC), and physical layers to ensure estimation performance. Subsequently, we examine control-oriented co-design paradigms, including separation principle-based designs, joint optimization without separation, and learning-based frameworks that integrate estimation, communication, and control in a unified manner. Finally, we discuss several inspiring future research directions.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 1","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588281","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}
Pub Date : 2026-03-01Epub Date: 2026-04-02DOI: 10.23919/JCIN.2026.11474256
Nan Huang;Yuan Ma;Xingjian Zhang;Chunlong He;Chiya Zhang
The advent of extremely large-scale arrays technology is the cornerstone of the sixth-generation wireless networks, promising significant advances in spectrum efficiency. However, compared with traditional far-field beam training methods, near-field beam training faces greater challenges as the spherical wavefront propagation characteristic in the near-field environments necessitates beam search in both the angle and distance dimensions. To reduce the training overhead of the two-dimensional search, we propose a data-driven on-grid beam training scheme that can simultaneously search the angle and distance domains to find the optimal codewords through real-time adaptive alignments. To further address the grid-based non-uniform sampling, we propose a datadriven off-grid adaptive optimization scheme to further improve near-field beam training accuracy with fast convergence. By establishing equivalent dynamic linearization data models, the proposed approach adaptively adjusts the angle-distance domain estimation based on real-time measurements. Numerical results show that the proposed approach can achieve enhanced beamforming performance with reduced training overhead.
{"title":"Data-Driven Near-Field Beam Training and Off-Grid Optimization Scheme","authors":"Nan Huang;Yuan Ma;Xingjian Zhang;Chunlong He;Chiya Zhang","doi":"10.23919/JCIN.2026.11474256","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11474256","url":null,"abstract":"The advent of extremely large-scale arrays technology is the cornerstone of the sixth-generation wireless networks, promising significant advances in spectrum efficiency. However, compared with traditional far-field beam training methods, near-field beam training faces greater challenges as the spherical wavefront propagation characteristic in the near-field environments necessitates beam search in both the angle and distance dimensions. To reduce the training overhead of the two-dimensional search, we propose a data-driven on-grid beam training scheme that can simultaneously search the angle and distance domains to find the optimal codewords through real-time adaptive alignments. To further address the grid-based non-uniform sampling, we propose a datadriven off-grid adaptive optimization scheme to further improve near-field beam training accuracy with fast convergence. By establishing equivalent dynamic linearization data models, the proposed approach adaptively adjusts the angle-distance domain estimation based on real-time measurements. Numerical results show that the proposed approach can achieve enhanced beamforming performance with reduced training overhead.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 1","pages":"62-71"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588290","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}
With the advantages of low cost and easy deployment, ambient backscatter has attracted significant attention in the Internet of things (IoT) community and experienced rapid growth over the past decade. As the number of deployed backscatter devices increases, enabling concurrent transmission among multiple tags has become critical for achieving high throughput and large-scale network coverage. To this end, this article presents a comprehensive survey of concurrent transmission techniques. To provide a structured overview, we categorize existing studies on backscatter concurrency into five implementation-oriented domains, including time, frequency, spatial, code, and energy, according to their technical characteristics. For each domain, we comprehensively review representative works, emphasizing their underlying principles and key designs. Furthermore, we investigate the growing trend of multi-domain integration, demonstrating how cross-layer synergies enhance performance and facilitate system evolution. Finally, we outline open challenges and future research directions for concurrent backscatter communication.
{"title":"A Comprehensive Survey on Concurrent Transmission in Backscatter Communication","authors":"Wei Wang;Shuo Ding;Zhiqing Luo;Huixin Dong;Yijie Wu;Qian Zhang","doi":"10.23919/JCIN.2026.11474253","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11474253","url":null,"abstract":"With the advantages of low cost and easy deployment, ambient backscatter has attracted significant attention in the Internet of things (IoT) community and experienced rapid growth over the past decade. As the number of deployed backscatter devices increases, enabling concurrent transmission among multiple tags has become critical for achieving high throughput and large-scale network coverage. To this end, this article presents a comprehensive survey of concurrent transmission techniques. To provide a structured overview, we categorize existing studies on backscatter concurrency into five implementation-oriented domains, including time, frequency, spatial, code, and energy, according to their technical characteristics. For each domain, we comprehensively review representative works, emphasizing their underlying principles and key designs. Furthermore, we investigate the growing trend of multi-domain integration, demonstrating how cross-layer synergies enhance performance and facilitate system evolution. Finally, we outline open challenges and future research directions for concurrent backscatter communication.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 1","pages":"22-38"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588241","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}
Pub Date : 2026-03-01Epub Date: 2026-04-02DOI: 10.23919/JCIN.2026.11474257
Yifan Qin;Fengyuan Zhu;Xiaohua Tian
Conventional transmissive metasurface design for Internet of things (IoT) communications relies heavily on expert knowledge and iterative full-wave simulations, resulting in high computational cost and limited efficiency. To address this challenge, we propose an end-to-end deep-learning-based design framework for sub-6 GHz transmissive communication metasurfaces, which enables automatic mapping from target transmission responses to manufacturable physical structures. We first develop a prediction model, Img2S, to accurately estimate the S-parameters of metasurfaces, significantly reducing the need for full-wave simulations. Based on this model, two variational generative networks, strictly constrained-conditional variational autoencoder (SC-CVAE) and loosely constrained-conditional variational autoencoder (LC-CVAE), are proposed to synthesize physically realizable metasurface structures by incorporating geometric priors and electromagnetic consistency constraints. Experimental results show that Img2S achieves a mean squared error (MSE) of 9.76 × 10−4 in predicting the simulated S-parameters of metasurfaces over the operating frequency band. Both simulation and measurement results confirm that the generated metasurfaces closely match the target electromagnetic responses, with single-state mean absolute errors (MAEs) below 0.16 in simulation and below 0.31 in measurement, respectively, outperforming conventional design approaches in terms of accuracy and frequency stability while significantly improving the overall design efficiency.
{"title":"Neural-Network-Assisted Design Approach for Transmissive Metasurfaces in IoT Communications","authors":"Yifan Qin;Fengyuan Zhu;Xiaohua Tian","doi":"10.23919/JCIN.2026.11474257","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11474257","url":null,"abstract":"Conventional transmissive metasurface design for Internet of things (IoT) communications relies heavily on expert knowledge and iterative full-wave simulations, resulting in high computational cost and limited efficiency. To address this challenge, we propose an end-to-end deep-learning-based design framework for sub-6 GHz transmissive communication metasurfaces, which enables automatic mapping from target transmission responses to manufacturable physical structures. We first develop a prediction model, Img2S, to accurately estimate the S-parameters of metasurfaces, significantly reducing the need for full-wave simulations. Based on this model, two variational generative networks, strictly constrained-conditional variational autoencoder (SC-CVAE) and loosely constrained-conditional variational autoencoder (LC-CVAE), are proposed to synthesize physically realizable metasurface structures by incorporating geometric priors and electromagnetic consistency constraints. Experimental results show that Img2S achieves a mean squared error (MSE) of 9.76 × 10<sup>−4</sup> in predicting the simulated S-parameters of metasurfaces over the operating frequency band. Both simulation and measurement results confirm that the generated metasurfaces closely match the target electromagnetic responses, with single-state mean absolute errors (MAEs) below 0.16 in simulation and below 0.31 in measurement, respectively, outperforming conventional design approaches in terms of accuracy and frequency stability while significantly improving the overall design efficiency.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 1","pages":"72-85"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588260","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}
Pub Date : 2026-03-01Epub Date: 2026-04-02DOI: 10.23919/JCIN.2026.11474255
Jinlu Liu;Xin Xie;Rui Han;Jiaxing Wang;Lin Bai;Guowei Shi
Frequency hopping (FH) plays a significant role in wireless ad hoc networks (WANETs) by enhancing communication security, reliability, and resistance to jamming. However, conventional frequency hopping schemes rely on pre-arranged hopping sequences and strict time synchronization among participating nodes, which limits their adaptability in dynamic and decentralized environments. Motivated by these limitations, we propose a random frequency hopping framework for multi-hop broadcasts in WANETs in combination with carrier sense multiple access with collision avoidance (CSMA/CA). To analyze the performance of the proposed framework, we leverage the analogy between information dissemination and epidemic spreading to develop an analytical model, which characterizes message propagation as an annular infection process, incorporating the effects of frequency matching probability, contention-based access control, and equivalent communication radius under Rician fading. Simulation results demonstrate that the proposed analytical model can effectively predict the process of message propagation, achieving a mean absolute percentage error (MAPE) of less than 5%. Across diverse network configurations, the analytical and simulation curves remain closely aligned, confirming the robustness and validity of the proposed model.
{"title":"Epidemic Modeling for Multi-Hop Broadcasts in Random Frequency Hopping Ad Hoc Networks","authors":"Jinlu Liu;Xin Xie;Rui Han;Jiaxing Wang;Lin Bai;Guowei Shi","doi":"10.23919/JCIN.2026.11474255","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11474255","url":null,"abstract":"Frequency hopping (FH) plays a significant role in wireless ad hoc networks (WANETs) by enhancing communication security, reliability, and resistance to jamming. However, conventional frequency hopping schemes rely on pre-arranged hopping sequences and strict time synchronization among participating nodes, which limits their adaptability in dynamic and decentralized environments. Motivated by these limitations, we propose a random frequency hopping framework for multi-hop broadcasts in WANETs in combination with carrier sense multiple access with collision avoidance (CSMA/CA). To analyze the performance of the proposed framework, we leverage the analogy between information dissemination and epidemic spreading to develop an analytical model, which characterizes message propagation as an annular infection process, incorporating the effects of frequency matching probability, contention-based access control, and equivalent communication radius under Rician fading. Simulation results demonstrate that the proposed analytical model can effectively predict the process of message propagation, achieving a mean absolute percentage error (MAPE) of less than 5%. Across diverse network configurations, the analytical and simulation curves remain closely aligned, confirming the robustness and validity of the proposed model.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 1","pages":"51-61"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588293","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}
Pub Date : 2026-03-01Epub Date: 2026-04-02DOI: 10.23919/JCIN.2026.11474254
Guangming Shi;Ning Lan;Xuemei Xie;Jinjian Wu
In the information age, individuals and organizations have accumulated massive volumes of digital data. As society enters the intelligence era, the primary demand has shifted toward effectively utilizing these data through intelligent models. However, prevailing large language models (LLMs) typically require data to be uploaded to centralized cloud servers for processing, making it difficult to ensure data privacy. Consequently, users increasingly seek small, locally deployable models that can operate directly on private data. To realize this expectation, this paper explores intelligent systems built upon small models rather than monolithic large models. On this basis, we first revisit the essence of intelligence as the capability to utilize knowledge to solve problems and accomplish tasks, and further propose the first principle of intelligence, which models intelligence as a closed-loop, goal-oriented process. Within this process, an agent, under external constraints, leverages knowledge to evaluate discrepancies between its internal state and task objectives, formulates strategies to reduce these discrepancies, and iteratively executes actions to converge toward goal completion. From this perspective, intelligence is viewed as a collaborative system of interdependent cognitive functions. Grounded in this theoretical principle, we propose the six-capability network, a knowledge-driven cognitive architecture that decomposes intelligence into six fundamental capabilities: observation, attention, understanding, discrimination, memory, and execution. These capabilities constitute the core cognitive model of the proposed framework and are each realized by lightweight, deployable small models operating over structured knowledge representations. Finally, to demonstrate the executability of the network, we consider a networked intelligence system. In this system, agents exchange semantic symbols that encode knowledge rather than raw data, enabling each agent to operate within its functional scope while acquiring missing knowledge from others when needed. This approach offers a new intelligence paradigm, enabling deployment in private, local, and resource-constrained environments.
{"title":"Rethinking Intelligence in the Era of Large Language Models","authors":"Guangming Shi;Ning Lan;Xuemei Xie;Jinjian Wu","doi":"10.23919/JCIN.2026.11474254","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11474254","url":null,"abstract":"In the information age, individuals and organizations have accumulated massive volumes of digital data. As society enters the intelligence era, the primary demand has shifted toward effectively utilizing these data through intelligent models. However, prevailing large language models (LLMs) typically require data to be uploaded to centralized cloud servers for processing, making it difficult to ensure data privacy. Consequently, users increasingly seek small, locally deployable models that can operate directly on private data. To realize this expectation, this paper explores intelligent systems built upon small models rather than monolithic large models. On this basis, we first revisit the essence of intelligence as the capability to utilize knowledge to solve problems and accomplish tasks, and further propose the first principle of intelligence, which models intelligence as a closed-loop, goal-oriented process. Within this process, an agent, under external constraints, leverages knowledge to evaluate discrepancies between its internal state and task objectives, formulates strategies to reduce these discrepancies, and iteratively executes actions to converge toward goal completion. From this perspective, intelligence is viewed as a collaborative system of interdependent cognitive functions. Grounded in this theoretical principle, we propose the six-capability network, a knowledge-driven cognitive architecture that decomposes intelligence into six fundamental capabilities: observation, attention, understanding, discrimination, memory, and execution. These capabilities constitute the core cognitive model of the proposed framework and are each realized by lightweight, deployable small models operating over structured knowledge representations. Finally, to demonstrate the executability of the network, we consider a networked intelligence system. In this system, agents exchange semantic symbols that encode knowledge rather than raw data, enabling each agent to operate within its functional scope while acquiring missing knowledge from others when needed. This approach offers a new intelligence paradigm, enabling deployment in private, local, and resource-constrained environments.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 1","pages":"39-50"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588296","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}
Pub Date : 2026-03-01Epub Date: 2026-04-02DOI: 10.23919/JCIN.2026.11474258
Lei Wang;Tiankui Zhang;Xiaoxia Xu;Tianyi Shi;Yapeng Wang
Unmanned aerial vehicle (UAV) edge computing effectively reduces task latency and mitigates computing pressure for ground terminals (GTs), particularly in scenarios lacking fixed terrestrial infrastructure. This paper constructs a novel framework for a multi-UAV edge computing system with cross-terminal dependent subtasks, in which the task offloading decision, communication bandwidth allocation, and UAV trajectory planning are jointly optimized. Unlike traditional task offloading schemes, the internal dependency relationships of subtasks impose complex temporal constraints on task offloading decision. Firstly, a directed acyclic graph (DAG) is employed to describe the structure of dependent subtasks. Accounting for computing timeliness requirements and UAV energy constraints, a system cost based on weighted delay and energy consumption is defined. Subsequently, a long-term optimization problem with the objective of minimizing system cost is formulated. In order to solve this complex non-convex mixed-integer programming problem, an algorithm combined with a pre-trained graph attention network (GAT) and the proximal policy optimization (PPO) is proposed. GAT utilizes its specialized graph-processing capabilities to extract high-level subtask features from the DAG. Then PPO integrates these high-dimensional features with environmental state information for global reasoning to obtain the task offloading decision and the UAV trajectory planning. Comprehensive simulations demonstrate that the proposed algorithm effectively reduces system cost under varying system parameters and successfully addresses the unique challenges of a multi-UAV edge computing system with dependent tasks.
{"title":"Optimization of Multi-UAV Edge Computing Systems with Dependent Tasks","authors":"Lei Wang;Tiankui Zhang;Xiaoxia Xu;Tianyi Shi;Yapeng Wang","doi":"10.23919/JCIN.2026.11474258","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11474258","url":null,"abstract":"Unmanned aerial vehicle (UAV) edge computing effectively reduces task latency and mitigates computing pressure for ground terminals (GTs), particularly in scenarios lacking fixed terrestrial infrastructure. This paper constructs a novel framework for a multi-UAV edge computing system with cross-terminal dependent subtasks, in which the task offloading decision, communication bandwidth allocation, and UAV trajectory planning are jointly optimized. Unlike traditional task offloading schemes, the internal dependency relationships of subtasks impose complex temporal constraints on task offloading decision. Firstly, a directed acyclic graph (DAG) is employed to describe the structure of dependent subtasks. Accounting for computing timeliness requirements and UAV energy constraints, a system cost based on weighted delay and energy consumption is defined. Subsequently, a long-term optimization problem with the objective of minimizing system cost is formulated. In order to solve this complex non-convex mixed-integer programming problem, an algorithm combined with a pre-trained graph attention network (GAT) and the proximal policy optimization (PPO) is proposed. GAT utilizes its specialized graph-processing capabilities to extract high-level subtask features from the DAG. Then PPO integrates these high-dimensional features with environmental state information for global reasoning to obtain the task offloading decision and the UAV trajectory planning. Comprehensive simulations demonstrate that the proposed algorithm effectively reduces system cost under varying system parameters and successfully addresses the unique challenges of a multi-UAV edge computing system with dependent tasks.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 1","pages":"86-99"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588285","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}
Contactless human-computer interaction (HCI) based on radio frequency (RF) signals is critical for next-generation intelligent environments. However, this field lacks a standardized benchmark dataset for practical interaction commands and faces challenges in heterogeneous signal fusion. We address this by constructing and releasing WRF-G20, a novel Wi-Fi and radio frequency identification (RFID) multimodal gesture dataset. It encompasses 20 refined gestures for cutting-edge applications and adheres to the standardized XRF55 protocol for reproducibility. To address the fusion challenge, we propose the mixture of experts-contrastive learning network (MoE-CLNet) framework. It first employs cross-modal contrastive learning to map heterogeneous signals into a shared semantic space, then utilizes a Top-K sparse MoE architecture to perform adaptive dynamic weighted fusion based on the signal quality characteristics of each input sample. On the WRF-G20 benchmark, MoE-CLNet achieves a 92.78% F1-score, validating the dataset's high quality and learnability while outperforming existing baseline models. The framework also demonstrates robust generalization on the public XRF55 benchmark, attaining 91.81% accuracy. This work provides the heterogeneous RF sensing field with critical research infrastructure and a high-performance technical reference.
{"title":"WRF-G20 and MoE-CLNet: A Wi-Fi + RFID HCI Benchmark and Contrastive-Expert Fusion Framework","authors":"Yaohua Guo;Xiangguo Li;Xuehong Sun;Liping Liu;Guoche Qin","doi":"10.23919/JCIN.2026.11474261","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11474261","url":null,"abstract":"Contactless human-computer interaction (HCI) based on radio frequency (RF) signals is critical for next-generation intelligent environments. However, this field lacks a standardized benchmark dataset for practical interaction commands and faces challenges in heterogeneous signal fusion. We address this by constructing and releasing WRF-G20, a novel Wi-Fi and radio frequency identification (RFID) multimodal gesture dataset. It encompasses 20 refined gestures for cutting-edge applications and adheres to the standardized XRF55 protocol for reproducibility. To address the fusion challenge, we propose the mixture of experts-contrastive learning network (MoE-CLNet) framework. It first employs cross-modal contrastive learning to map heterogeneous signals into a shared semantic space, then utilizes a Top-K sparse MoE architecture to perform adaptive dynamic weighted fusion based on the signal quality characteristics of each input sample. On the WRF-G20 benchmark, MoE-CLNet achieves a 92.78% F1-score, validating the dataset's high quality and learnability while outperforming existing baseline models. The framework also demonstrates robust generalization on the public XRF55 benchmark, attaining 91.81% accuracy. This work provides the heterogeneous RF sensing field with critical research infrastructure and a high-performance technical reference.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 1","pages":"120-136"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588288","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}
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