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Successive Interference Cancellation-Enabled Timely Status Update in Linear Multi-hop Wireless networks

IEEE Transactions on Mobile Computing 2025年第6期24卷 5298-5311页

作者： Han, Xinhui Pan, Haoyuan Wang, Zhaorui Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Future Network of Intelligence Institute The School of Science and Engineering Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China

We investigate the timely status update in linear multi-hop wireless networks, where a source tries to deliver status update packets to a destination through a sequence of half-duplex relays. Timeliness is measured by the age of information (AoI) metric. Maintaining a low AoI at the destination typically necessitates frequent transmission of update packets from the source. However, high packet transmission frequency in multi-hop scenarios can result in mutual wireless interference at intermediate relays. Specifically, when an intermediate relay receives wireless signals of a new packet from its previous node, simultaneous transmission of an old packet by its subsequent node to the next hop may cause wireless signals to interfere at the intermediate relay, conventionally leading to packet collision. A key motivation to solve this issue is that the intermediate relay has previously received the old packet (which can thus be forwarded to the subsequent node for further relaying). Hence, successive interference cancellation (SIC) can be employed to mitigate interference of the old packet and recover the new packet. This paper designs an SIC-enabled packet relaying scheme tailored to low AoI. Initially focusing on a three-hop network, we subsequently extend our approach to general multi-hop networks. We model the multi-hop relaying scheme using a Markov chain to derive the theoretical average AoI. Theoretical and simulation results indicate that the SIC-enabled packet relaying scheme significantly reduces the average AoI compared to the non-SIC approaches, owing to an increased packet transmission frequency at the source and the effectiveness of SIC techniques at the relays. © 2002-2012 IEEE.

关键词： Markov chains

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Toward a Time-Bounded Solution for Real-Time Latency Prediction in Dynamic 5G Communication

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 3853-3867页

作者： Nota, Andrea Maile, Lisa Saidi, Selma TU Dortmund Faculty of Electrical Engineering and Information Technology Dortmund 44227 Germany Institute of Computer and Network Engineering TU Braunschweig Braunschweig 38106 Germany

The growing adoption of safety-critical applications in domains such as Industry 4.0 and autonomous driving places strict latency requirements on Fifth Generation (5G) wireless networks. Existing approaches typically rely on latency measurements, detecting violations only after they occur, or on pre-planned network configurations, which are impractical in highly dynamic environments such as mobile device communication. In this work, we propose a proactive analytical approach for predicting Worst-Case Latencies (WCLs) in 5G networks before violations occur. Our method derives upper bounds on latency analytically, ensuring both explainability and computational efficiency. Additionally, we introduce the concept of a Validity Interval (VI), which quantifies how long a latency prediction remains valid. We extensively evaluate our solution in synthetic and realistic simulations to define the impact of key parameters of our new model on global pessimism, computational overhead, and accuracy. Our results show that with complete knowledge of future channel conditions, our analytical model can consistently upper-bound the simulated worst-case latencies. Even with limited knowledge about future channel conditions, using state-of-the-art forecasting methods, our approach still bounds 99.8% of actual latencies, demonstrating its robustness. With this, our works demonstrate that future 5G networks can enable safe and reliable real-time applications even in highly dynamic environments. © 2020 IEEE.

关键词： channel quality prediction Dynamic 5G wireless communication real-time latency prediction time-bounded procedure

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Service-Oriented Resource Allocation and Task Scheduling for Wi-Fi and Bluetooth Coexistence in Smart Home IoT Systems

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ACM Transactions on Internet of Things 2025年第2期6卷 1-24页

作者： Zhao, Di Niu, Tianxu Song, Bin Du, Xiaojiang Xidian University State Key Laboratory of Integrated Services Network Xi'an China Xidian University Hangzhou Institute of Technology Hangzhou China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken United States

In IoT-enabled smart home scenarios, heterogeneous communication devices such as Bluetooth (BT) and Wi-Fi are widely used in applications such as home automation, remote monitoring, and intelligent device interconnection. However, in such a multi-device coexistence environment, efficiently allocating limited time-frequency resources to mitigate communication interference and enhance system performance has become a critical challenge. To address these issues, this article proposes a comprehensive solution that integrates master selection, resource allocation, and task scheduling to optimize resource utilization and service quality in smart home IoT systems. For device management, we propose a hierarchical entropy weight method (HEWM), considering factors such as device parameters, sensing capabilities, communication performance, and device interoperability. This method ensures efficient and stable selection of the primary device, optimizing network topology and communication efficiency. For resource allocation, we introduce a proximal policy optimization (PPO) algorithm that dynamically adjusts time-frequency resource allocation based on the varying device usage, network load, and communication condition. This adaptive strategy reduces interference between devices and improves system throughput. For task scheduling, we develop a task urgency-based queueing (TUQ) mechanism that prioritizes tasks based on urgency. A task preemption mechanism ensures that high-urgency tasks are processed with minimal delay, enhancing scheduling efficiency and service responsiveness. Simulation results show that the proposed approach significantly outperforms traditional methods in smart home IoT scenarios, achieving higher primary device scores, a 3%-22% improvement in system throughput, and a 5%-36% reduction in task delay. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Wi-Fi

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Resource Allocation for SLIPT-enabled Hybrid OFDMA-NOMA VLC System

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IEEE Photonics Technology Letters 2025年第12期37卷 667-670页

作者： Chen, Danyang Ren, Zhiyu Wang, Qingxuan Hao, Rui Wang, Jianping University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China China Telecom Research Institute Cloud-Network Integration Laboratory Beijing102209 China

This letter investigates a simultaneous lightwave information and power transfer (SLIPT)-enabled hybrid orthogonal frequency division multiple access and non-orthogonal multiple access (OFDMA-NOMA) technology for visible light communication (VLC) systems operating in a high-frequency fading channel. An optimization problem considering information receiving and energy harvesting performance is formulated by jointly adjusting the inter-pair power allocation factor, direct current bias, and power splitting coefficient. A resource allocation scheme based on a modified arithmetic optimization algorithm (m-AOA) is adopted. The results demonstrate that the proposed resource allocation scheme improves energy efficiency (EE) of the SLIPT-enabled hybrid multiple access VLC system by 15%, while the user fairness is also enhanced compared to the conventional fixed power allocation scheme. © 1989-2012 IEEE.

关键词： Energy efficiency

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Coded Beam Training for RIS Assisted Wireless Communications

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3854-3867页

作者： Chen, Yuhao Dai, Linglong Department of Electronic Engineering China Tsinghua University State Key laboratory of Space Network and Communications Beijing100084 China Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States

Reconfigurable intelligent surface (RIS) is considered as one of the key technologies for future 6G communications. To fully unleash the performance of RIS, accurate channel state information (CSI) is crucial. Beam training is widely utilized to acquire the CSI. However, before aligning the beam correctly to establish stable connections, the signal-to-noise ratio (SNR) at UE is inevitably low, which reduces the beam training accuracy. To deal with this problem, we exploit the coded beam training framework for RIS systems, which leverages the error correction capability of channel coding to improve the beam training accuracy under low SNR. Specifically, we first extend the coded beam training framework to RIS systems by decoupling the base station-RIS channel and the RIS-user channel. For this framework, codewords that accurately steer to multiple angles is essential for fully unleashing the error correction capability. In order to realize effective codeword design in RIS systems, we then propose a new codeword design criterion, based on which we propose a relaxed Gerchberg-Saxton (GS) based codeword design scheme by considering the constant modulus constraints of RIS elements. In addition, considering the two dimensional structure of RIS, we further propose a dimension reduced encoder design scheme, which can not only guarentee a better beam shape, but also enable a stronger error correction capability. Simulation results reveal that the proposed scheme can realize effective and accurate beam training in low SNR scenarios. © 2002-2012 IEEE.

关键词： Error correction

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Resource Allocation in 6G network for High-Speed Train Using D2D Outband Communication 3rd

Resource Allocation in 6G Network for High-Speed Train Using...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Garg, Aakansha Arya, Rajeev Singh, Maheshwari Prasad Wireless Sensor Network Lab Department of Electronics and Communication Engineering Patna India Department of Computer Science and Engineering National Institute of Technology Patna India

ISBN: (纸本)9789819743582

The growth of 6G results in rising data transmission rate in static scenario. To achieve fast communication and computation with quality service in high-speed train is a challenge. This is due to fast handover and Doppler shift. To effectively communicate in high-speed train, the placement of a mobile relay (MR) with small server at the rooftop of train coach is proposed. The base station sends channel state information to mobile relay which stores it in the stack. The resource assignment has been conducted by an improved grey wolf optimization algorithm. The focus is at improving throughput with reduction in latency and energy consumption. The simulation outcomes manifests that the presented algorithm carry-out finer in comparison of the Hungarian algorithm. The accomplished results are refined almost three times in respect of energy consumption, latency, and throughput. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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A New Data-Free Backdoor Removal Method via Adversarial Self-Knowledge Distillation

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IEEE Internet of Things Journal 2025年第9期12卷 12267-12277页

作者： Li, Xuexiang Gao, Yafei Liu, Minglin Zhou, Xu Chen, Xianfu Wu, Celimuge Li, Jie Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China Michigan Technological University Department of Computer Science HoughtonMI49931 United States Shenzhen CyberAray Network Technology Company Ltd Shenzhen518038 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201204 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

In the context of Internet of Things edge devices, pretrained models are often sourced directly from cloud computing platforms due to the unavailability of training data. This lack of access during the training phase makes these models susceptible to backdoor attacks. To address this challenge, we introduce a novel data-free backdoor removal method that operates effectively even when only the poisoned model is accessible. Our innovative approach employs two end-to-end generators with identical architectures to create both clean and poisoned samples. These samples are crucial for transferring knowledge from the teacher model—the fixed poisoned model—to the student model, which is initialized with the poisoned model. Our method utilizes a channel shuffling technique during the distillation process to disrupt and eliminate the backdoor knowledge embedded in the teacher model. This process involves iterative updates of the generators and meticulous distillation of the student model, leading to efficient backdoor removal. We conducted extensive experiments on five sophisticated backdoor attacks across two benchmark datasets. The results demonstrate that our method not only significantly bolsters the model’s resistance to backdoor attacks but also maintains high recognition accuracy for clean samples, thereby outperforming existing methods. © 2014 IEEE.

关键词： Cloud platforms

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Towards Robust Cyber Attack Taxonomies: A Survey with Requirements, Structures, and Assessment

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ACM Computing Surveys 2025年第8期57卷 1-36页

作者： Santos, Paulo Roberto Da Paz Ferraz Resende, Paulo Angelo Alves Gondim, João José Costa Drummond, André Costa Department of Computer Science University of Brasilia Brasilia Brazil C4I and Cyber Center George Mason University Fairfax United States GigaCandanga Association Brasilia Brazil Department of Electrical Engineering University of Brasilia Brasilia Brazil Institute of Computer and Network Engineering Technische Universität Braunschweig Braunschweig Germany

Cyber attacks have become a growing threat in today's interconnected society, and taxonomies play a crucial role in understanding and preventing these attacks. However, the lack of comprehensive assessment methods for evaluating attack taxonomies represents a significant gap in the literature, hindering their development and applicability. This article aims to address this gap by conducting a survey of 20 attack taxonomies published between 2011 and 2022 and evaluating them with a novel set of qualitative and quantitative assessment criteria, grounded in fundamental taxonomy requirements and key structural attributes. In pursuit of clear and objective assessment criteria, the authors investigated the main taxonomy properties in the literature, identifying dependencies and relationships. This investigation extracted the fundamental requirements for a relevant and widely accepted attack taxonomy in the cybersecurity community. Noteworthy structural aspects, such as organization, scheme, labeling, and approach, are also addressed, considering their impact on taxonomy effectiveness and applicability constraints. Finally, the article poses some open questions and challenges, along with suggestions for future research directions. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cyber attacks

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Dysarthric speech recognition: an investigation on using depthwise separable convolutions and residual connections

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Neural Computing and Applications 2025年第12期37卷 7991-8005页

作者： Shahamiri, Seyed Reza Mandal, Krishnendu Sarkar, Sudeshna DeepNet Discovery Network Faculty of Engineering and Design University of Auckland Private Bag 92019 Building 405 Level 6 Room 669 3 Garfton Road Auckland1142 New Zealand Computer Science and Engineering Department Indian Institute of Technology Kharagpur India

Dysarthria is a neurological condition resulting from impairments affecting muscle control involved in speech articulation, leading to reduced intelligibility or unintelligible speech, which affects communication abilities. Although Automatic Speech Recognition (ASR) technologies hold the potential to improve the lives of people with dysarthria significantly, ASR systems designed for normal speech have shown limited effectiveness when presented with impaired speech. Consequently, researchers have focused on developing ASR systems specifically tailored for dysarthria. However, progress in this area has been gradual due to the scarcity of dysarthric speech for training and the increased variability of speech among dysarthric individuals, necessitating a larger dataset of dysarthric utterances. One potential solution to enhance the robustness of dysarthric ASR is to deepen the architecture of the acoustic model, which maps the speech signal to words or phonetic units. However, deeper architectures require more training data and pose challenges in dealing with issues such as the vanishing gradient problem and representational bottlenecks in deep learning models. In this study, we expanded on our previous findings and investigated the applications of Depthwise Separable Convolution neurons and the inclusion of Residual Connections to propose a deep dysarthric acoustic model, tackling both vanishing gradients and representational bottleneck issues in dysarthric ASR. Multiple speaker-adaptive dysarthric ASRs were trained and evaluated for 15 UA-Speech dysarthric subjects, then benchmarked against the state-of-the-art and our previous dysarthric ASRs. Our proposed architectures have delivered up to 22.58% word recognition rate (WRR) improvements over the reference models. We observed an average of 10.81% better WRRs over the base traditional dysarthric ASR for all speakers. Likewise, the proposed acoustic model outperformed the state-of-the-art Transformer-based dysarthric

关键词： Convolution

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A novel coarse-grained knowledge graph embedding framework for platform risk identification from relational data

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Neural Computing and Applications 2025年 1-21页

作者： Zhang, Qi Wang, Shicheng Wang, Lihong Sheng, Jiawei Guo, Shu Li, Chen He, Min Zhang, Renqiang School of Economics and Management Beihang University Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China School of Computer Science and Engineering Beihang University Beijing China

Platform risk identification from relational data aims to determine whether the target platforms are risky or not. However, in such a scenario, the relation data involve massive types of relations and numerous numerical-aware relations composed of attributes and infinite continuous values are challenges in this field. It is paramount to capture semantic information of relations and precise control power, drastically affecting the power of learning and the performance of identifying risky platforms. Recent financial risk studies often employed graph neural networks (GNNs) to model relational data, which fall short of capturing the intricacies of such complex relationships resulting in incorrect identification of platforms. To solve this problem, we propose a novel coarse-grained knowledge graph embedding (CKGE) framework that integrates coarse-grained knowledge graph construction and adopts five well-known knowledge graph embedding methods and ensemble learning. The extensive experimental results based on a real-world peer-to-peer lending platform dataset demonstrate that the CKGE framework achieves state of the art over GNNs for platform risk identification. Our study highlights the importance of utilizing the relational data of multi-type and multi-numerical relations for revealing platform risk situations and provides an alternative solution for identifying platform risk. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Knowledge graph

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