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Optimizing AoI in RIS-aided Wireless Powered Backscatter-Active Communication Networks

IEEE Transactions on Vehicular Technology 2025年

作者： Hu, Huimin Zhang, Ruichen Ding, Zhiguo Jiang, Fan Jiang, Ruihong Niyato, Dusit Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an710121 China Nanyang Technological University College of Computing and Data Science Singapore Singapore Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

In this paper, we investigate the average age of information (AoI) in wireless powered backscatter-active communication networks (WPBACN) assisted by a reconfigurable intelligent surface (RIS). To minimize the average AoI, we jointly optimize the phase shift of the RIS, the backscatter-active communication time allocation factor, and the transmitted power of IoT devices. Since the optimization problem is composed of coupled variables, the optimization variables are implicit in the objective function and the problem is non-convex, which makes it challenging to solve. To address this challenge, we decompose the problem into an RIS phase shift optimization subproblem and a joint power and time allocation subproblem. Simulation results indicate that the proposed scheme has a significant performance advantage in reducing the average AoI compared to the scheme using random RIS phase shifts and the scheme without RIS and supports the challenging quality of service required by real-time information update applications. © 1967-2012 IEEE.

关键词： age of information Reconfigurable intelligent surface WPBACN

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UAV-Aided Covert Federated Learning Networks

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IEEE Network 2025年

作者： Wang, Chao Guo, Shaoyong Xiong, Zehui Xing, Chengwen Zhao, Nan Niyato, Dusit Karagiannidis, George Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Beijing Institute of Technology School of Information and Electronics Beijing100081 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece

With the increasing emphasis on data privacy, federated learning (FL) networks show great potential through distributed training without directly sharing the raw data. However, the coverage of FL terrestrial servers is usually limited. Therefore, we propose leveraging the unmanned aerial vehicle (UAV) as a mobile flying server to further improve the wireless coverage and training efficiency of FL. Nevertheless, frequent exchanges of model parameters in UAV-assisted FL can result in serious security risks. In order to achieve this, we propose a covert FL scheme assisted by the UAV, which can protect the transmission key features of local models from being detected by wardens. Furthermore, we discuss the FL network and covert communications, as well as the exceptional features of UAV-assisted covert FL. Finally, we present two case studies, one of which focuses on minimising the network latency, while the other concentrates on reducing the energy consumption of covert FL. The simulation results demonstrate the efficacy of the proposed schemes in addressing future challenges. © 1986-2012 IEEE.

关键词： Data privacy

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Clustering-based adaptive low-power subframe configuration with load-aware offsetting in dense heterogeneous networks

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Digital Communications and Networks 2022年第5期8卷 843-852页

作者： Dinkisa A.Bulti Jyri Hämäläinen Beneyam B.Haile Yihenew Wondie School of Electrical and Computer Engineering Addis Ababa Institute of Technology(AAiT)Ethiopia Department of Communication and Networking Aalto UniversityFinland

Heterogeneous Networks(HetNets)and cell densification represent promising solutions for the surging data traffic demand in wireless *** dense HetNets,user traffic is steered toward the Low-Power Node(LPN)when possible to enhance the user throughput and system capacity by increasing the area spectral ***,because of the transmit power differences in different tiers of HetNets and irregular service demand,a load imbalance typically exists among different serving *** offload more traffic to LPNs and coordinate the Inter-Cell Interference(ICI),Third-Generation Partnership Project(3GPP)has facilitated the development of the Cell Range Expansion(CRE),enhanced Inter-Cell Interference Coordination(eICIC)and Further enhanced ICIC(FeICIC).In this paper,we develop a cell clustering-based load-aware offsetting and an adaptive Low-Power Subframe(LPS)*** solution allows the separation of User Association(UA)functions at the User Equipment(UE)and network server such that users can make a simple cell-selection decision similar to that in the maximum Received Signal Strength(max-RSS)based UA scheme,where the network server computes the load-aware offsetting and required LPS periods based on the load conditions of the *** proposed solution is evaluated using system-level simulations wherein the results correspond to performance changes in different service *** show that our method effectively solves the offloading and interference coordination problems in dense HetNets.

关键词： Heterogeneous networks(HetNets) 5G LTE-AUser association Interference coordination Load balancing Cell range expansion Almost blank subframe Further enhanced ICIC Low-power subframe

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TrustChain-VANETs: Blockchain and IPFS Integration for Reliable and Secure Vehicular Communication in Intelligent Transportation Systems (ITS)

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IET Intelligent Transport Systems 2025年第1期19卷

作者： Zia Ullah Ghassan Husnain Abid Iqbal Ibrar Ali Shah Ali Saeed Alzahrani Ramasamy Srinivasaga Naidu Yazeed Yasin Ghadi Mohammed S. Al-Zahrani Department of Computer Software Engineering University of Engineering and Technology Mardan Mardan Pakistan Department of Computer Science CECOS University of IT and Emerging Sciences Peshawar Pakistan Department of Computer Engineering College of Computer Sciences and Information Technology King Faisal University AlAhsa Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi UAE Department of Computer Networking College of Computer Sciences and Information Technology King Faisal University AlAhsa Saudi Arabia

Vehicular ad-hoc networks (VANETs) are pivotal in intelligent transportation systems (ITS), enabling enhanced traffic efficiency and safety. However, VANETs within ITS face critical challenges related to trust, privacy, and data reliability. To address these issues, this paper proposes a comprehensive solution that integrates blockchain and InterPlanetary file system (IPFS) technologies for ITS applications. We introduce a blockchain-based trust management system, TrustChain-VANETs, designed to ensure message credibility, privacy, and data reliability in ITS environments. Our model safeguards vehicle privacy while enabling credible messages to be shared through anonymous aggregate vehicular announcements, an essential feature for ITS. Reputation values, stored in the blockchain, allow roadside units (RSUs) to assess message reliability, achieving a 15% higher malicious vehicle detection rate compared to traditional methods at low probabilities of false reporting, crucial for trust in ITS. Additionally, conditional privacy is maintained by tracking malicious entities through public addresses, ensuring accountability in ITS. The system leverages IPFS on RSUs for secure, reliable data storage, with aggregated event data from vehicles stored in IPFS and vehicle reputation values maintained on the blockchain, addressing storage and cost challenges in ITS. This approach reduces transaction costs by 20% and decreases storage overhead by 30%, enhancing the efficiency of ITS data sharing. An incentive mechanism encourages honest data sharing among vehicles, with monetary rewards for aligning with verified event information, transparently recorded on the blockchain. Performance analysis demonstrates that TrustChain-VANETs reduces message verification time by an average of 25% compared to traditional proof-of-work blockchain models, making it suitable for the dynamic and demanding nature of ITS. This innovative framework addresses critical challenges in VANETs, delivering robu

关键词： data privacy security of data vehicular ad hoc networks

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Over-the-air Wireless Federated Learning Model for Generative AI

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IEEE Network 2025年

作者： Zheng, Jie Niyato, Dusit Zhang, Haijun Du, Hongyang Wang, Jiacheng Kang, Jiawen Xiong, Zehui Northwest University State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Shaanxi 710127 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore University of Science and Technology Beijing Institute of Artificial Intelligence Beijing100083 China University of Hong Kong Department of electrical and electronic engineering Hong Kong Hong Kong Guangdong University of Technology Automation of School Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

Generative artificial intelligence (GenAI) technologies represent an important advancement in the field of AI, particularly for their capabilities in text and image generation. Over-the-air wireless federated learning (OA-WFL) can utilize the wireless waveform superposition property to achieve efficient model aggregation, thereby providing support for GenAI training and deployment. Therefore, this article investigates the support of OA-WFL for GenAI, focusing on potential applications and specific examples. We first discuss the OA-WFL and GenAI models, emphasizing their functionalities and the potential benefits arising from their interaction. We then explore its application in various GenAI scenarios, including large-scale edge device content generation, efficient distributed training of GenAI models, and reduction of bandwidth requirements and device load. Next, a framework is proposed to apply OA-WFL to diffusion models, such as those used in image generation, and validate its effectiveness through simulation results. Finally, we discuss prospective research directions for the application of OA-WFL for GenAI. © 2025 IEEE.

关键词： Federated learning

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Modeling of miss-probability in content-centric networking

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Science China(Information Sciences) 2015年第7期58卷 187-199页

作者： HUANG Tao WANG GuoQing FANG Chao YU F.Richard LIU YunJie State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Department of Systems and Computer Engineering Carleton University

Content-Centric networking(CCN), one of the most promising Future Internet architectures, is a hot research topic because of its advantages of content delivery over the Internet. Based on the Least Recently Used(LRU) replacement policy in CCN, the closed-form expression of the miss-probability is first derived by considering the sequence of arriving requests under single cache scenario, and then extended to linear and binary tree topologies. Consequently, the virtual round trip time(VRTT) is determined to be influenced by cache capacity and link bandwidth. The simulation results show that the proposed miss-probability model accords with realistic CCN network conditions, being much better than the existing model.

关键词： in-network caching miss-probability least recently used request sequence content-centric net-working

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Data Transmission with the Battery Utilization Maximization

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Journal of computer Science & Technology 2011年第3期26卷 392-404页

作者：张哲维章铁飞杨川岳陈应正洪士灏郭大维陈天洲 Department of Computer Science and Information Engineering "National Taiwan University" College of Computer Science Zhejiang University Graduate Institute of Networking and Multimedia "National Taiwan University"

With the growing popularity of 3G-powered devices, there are growing demands on energy-efficient data trans- mission strategies for various embedded systems. Different from the past work in energy-efficient real-time task scheduling, we explore strategies to maximize the amount of data transmitted by a 3G module under a given battery capacity. In particular, we present algorithms under different workload configurations with and without timing constraint considerations. Experiments were then conducted to verify the validity of the strategies and develop insights in energy-efficient data transmission.

关键词： data transmission battery utilization 3G devices bandwidth scheduling

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Wireless Hallucination in Generative AI-enabled Communications: Concepts, Issues, and Solutions

arXiv

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arXiv 2025年

作者： Wang, Xudong Wang, Jiacheng Feng, Lei Niyato, Dusit Zhang, Ruichen Kang, Jiawen Xiong, Zehui Du, Hongyang Mao, Shiwen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn United States

Generative AI (GenAI) is driving the intelligence of wireless communications. Due to data limitations, random generation, and dynamic environments, GenAI may generate channel information or optimization strategies that violate physical laws or deviate from actual real-world requirements. We refer to this phenomenon as wireless hallucination, which results in invalid channel information, spectrum wastage, and low communication reliability but remains underexplored. To address this gap, this article provides a comprehensive concept of wireless hallucinations in GenAI-driven communications, focusing on hallucination mitigation. Specifically, we first introduce the fundamental, analyze its causes based on the GenAI workflow, and propose mitigation solutions at the data, model, and post-generation levels. Then, we systematically examines representative hallucination scenarios in GenAI-enabled communications and their corresponding solutions. Finally, we propose a novel integrated mitigation solution for GenAI-based channel estimation. At the data level, we establish a channel estimation hallucination dataset and employ generative adversarial networks (GANs)-based data augmentation. Additionally, we incorporate attention mechanisms and large language models (LLMs) to enhance both training and inference performance. Experimental results demonstrate that the proposed hybrid solutions reduce the normalized mean square error (NMSE) by 0.19, effectively reducing wireless hallucinations. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Multiple additively constrained path selection.

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IEE PROCEEDINGS-COMMUNICATIONS 2002年第5-6期149卷 237-241页

作者： Cheng, G Ansari, N Advanced Networking Lab Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark USA

Finding a feasible path subject to multiple constraints in a network is an NP-complete problem and has been extensively studied. However, current algorithms suffer either high computational complexity or low success ratio in finding feasible paths. The authors propose a novel extended Bellman–Ford algorithm (EB), from which they present a high-performance algorithm with low computational complexity in finding feasible paths with multiple additive constraints. Through analysis and simulations, it is shown that the algorithm outperforms its contenders in the success rate of finding a feasible path as well as in terms of scalability; the proposed algorithm can achieve almost 100% success ratio as long as a feasible path exists. Furthermore, the worst case computational complexity is only twice that of the Bellman–Ford algorithm.

关键词： telecommunication network routing NP-complete problem multiple additively constrained path selection quality of service computational complexity feasible path packet switching Communication network design, planning and routing high-performance algorithm EB Communication switching extended Bellman-Ford algorithm

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QoS-Aware Joint Massive Random Access Control and Resource Allocation With Colliding Preamble Reuse for Prioritized IoT

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IEEE Transactions on Vehicular Technology 2025年

作者： Guo, Ziming Zhu, Xu Wei, Zhongxiang Cao, Jie Jiang, Yufei Lau, Vincent K.N. Sun, Sumei Harbin Institute of Technology School of Information Science and Technology Shenzhen518055 China Shenzhen Municipal Key Laboratory of AIoT Communication Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China TongJi University College of Electronic and Information Engineering Shanghai Institute of Intelligent Science and Technology China Southeast University National Mobile Communications Research Laboratory Nanjing China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering 999077 Hong Kong Institute for Infocomm Research Agency for Science 138632 Singapore

Enabling the current Internet-of-Things (IoT) and the future Internet-of-Everything (IoE) paradigms in the existing networks is challenging due to the low access efficiency amidst massive random access (RA) requests and heterogeneous applications. To address this, we investigate a novel prioritized RA control technique for massive IoT networks that cooperatively identifies adaptive access class barring (ACB) factors and preamble allocations, taking into account IoT devices' heterogeneity and limited RA resources. We derived the closed-form expression for the preamble utilization ratio (PAUR), incorporating the reusable colliding preambles and the early-stage collision detection (es-CD) technique. Two priority-guided RA request transition models are established, precisely translating RA priorities into a flexible backoff window size. Our Markov-Chain-based analytical model enables a comprehensive RA performance evaluation, demonstrating the superior performance of the proposed QoS-Aware Joint Access Control and Resource Allocation (QA-JACRA) in terms of PAUR, access delay, and throughput. This is achieved at a lower computational complexity compared to existing RA control schemes, providing a promising design approach for the activation of massive IoT devices. © 2025 IEEE.

关键词： Markov chains

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