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IEEE Annual Joint Conference: INFOCOM, IEEE computer and communications Societies

作者： Xiaoxi Zhang Haoran Xu Longhao Zou Jingpu Duan Chuan Wu Yali Xue Zuozhou Chen Xu Chen School of Computer Science and Engineering Sun Yat-sen University Department of Communications Peng Cheng Laboratory Department of Computer Science The University of Hong Kong

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Today’s video streaming service providers have exploited cloud-edge collaborative networks for geo-distributed video delivery. The existing content delivery network (CDN) scheduling and adaptive bitrate algorithms may not fully utilize edge resources or lack a global control to optimize resource sharing. The emerging super-resolution (SR) approach can unleash the potential of leveraging computation resources to compensate for bandwidth consumption, by producing high-quality videos from low-resolution contents. Yet the uncertain SR resource sensitivity and its interplay with bitrate adaptation are underexplored. In this work, we propose Rosevin, the first resource scheduler that jointly decides the bitrates and fine-grained resource allocation to perform SR at the edge, which can learn to optimize the long-term QoE for distributed end users. To handle the time-varying and complex space of decisions as well as a non-smooth objective function, Rosevin realizes a novel online combinatorial learning algorithm, which nicely integrates convex optimization theories and online learning techniques. In addition to theoretically analyzing its performance, we implement an SR-assisted video streaming prototype of Rosevin and demonstrate its advantages over several video delivery benchmarks.

关键词： Sensitivity Processor scheduling Federated learning Bit rate Superresolution Prototypes Streaming media

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Efficient multimodal cancelable biometric system based on steganography and cryptography

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Iran Journal of computer Science 2023年第2期6卷 109-121页

作者： Salama, Gerges M. El-Gazar, Safaa Nassar, Rana M. El-Shafai, Walid Khalaf, Ashraf A. M. El-Banby, Ghada M. Hamed, Hesham F. A. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia 61111 Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh 11586 Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 84428 Saudi Arabia

Due to the development of hacking programs, it has become easy to penetrate systems. Hence, there is a need for strong security mechanisms. The use of traditional passwords has become insufficient to secure systems. Biometric authentication is now widely used for security applications, and it has proven to be superior compared to traditional authentication methods. However, two issues need to be considered in biometric systems. The first is not to keep biometric data in its original form in the database. If biometric traits are hacked, they will no longer be of use. Biometric data should be kept in cancelable forms for reuse. The second issue is the reliance on a single biometric, which limits the verification accuracy. This can be solved by using a multimodal biometric system. Using steganography and cryptography, this paper introduces a cancelable multimodal biometric system. As voiceprints, facial images, and fingerprint images are used. In this paper, the verification is performed through the Mel frequency cepstral coefficients (MFCCs) of the voiceprints. Steganography is used as a tool to secure features extracted from voiceprints by embedding them into the facial image using block-based singular value decomposition (BSVD). Double random phase encoding (DRPE) is utilized as an encryption algorithm to generate the final cancelable templates. To increase the level of system security, fingerprint images are used as random phase masks (RPMs). Verification is performed by estimating the correlation between registered and test MFCCs. The correlation value is then compared with a threshold value, which is calculated using the distribution curves for the genuine and imposter correlations. Equal error rate (EER) values close to zero and an area under the receiver operator characteristic curve (AROC) that is close to one are obtained from the simulation results, demonstrating the outstanding performance of the suggested system. The proposed system achieves good performan

关键词： BSVD Cancelable biometrics DRPE MFCCs

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Toward Robust Channel Estimation in 5G With Atrous Pyramid Attention Networks

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IEEE communications Letters 2025年第6期29卷 1305-1309页

作者： Ngoc-Ha Truong Gia-Vuong Nguyen Ngoc Son Truong Thien Huynh-The Department of Computer and Communications Engineering Ho Chi Minh City University of Technology and Education Ho Chi Minh City Vietnam

Accurate channel estimation is crucial for reliable communication in modern wireless networks like fifth-generation (5G) and beyond 5G. However, current advanced methods face critical challenges in estimating channel coefficients accurately under noisy conditions and maintaining efficient model complexity. To overcome these challenges, this work proposes APANet, a deep convolutional network specially designed by incorporating atrous pyramid modules and synthesis attention modules to capture both local relationships and long-range dependencies in channel response matrices. Accordingly, APANet improves learning efficiency of complex channel patterns to increase the accuracy of channel estimation. Simulation results based on a synthesis channel dataset reveal that APANet outperforms state-of-the-art methods in estimation accuracy across various channel configurations while maintaining competitive computational efficiency. These results demonstrate the robustness and suitability of APANet for practical wireless networks, thus showcasing its potential to revolutionize channel estimation techniques.

关键词： Channel estimation Feature extraction Accuracy OFDM Convolutional neural networks 5G mobile communication Symbols Data mining Convolution Estimation

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Approximate EFX and Exact tEFX Allocations for Indivisible Chores: Improved Algorithms

arXiv

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arXiv 2024年

作者： Afshinmehr, Mahyar Ansaripour, Matin Danaei, Alireza Mehlhorn, Kurt Department of Computer Engineering Sharif University of Technology Iran Computer and Communications Sciences Department EPFL Switzerland Max Planck Institute for Informatics SIC Germany

We explore the fair distribution of a set of m indivisible chores among n agents, where each agent’s costs are evaluated using a monotone cost function. Our focus lies on two fairness criteria: envy-freeness up to any item (EFX) and a relaxed notion, namely envy-freeness up to the transfer of any item (tEFX). We demonstrate that a 2-approximate EFX allocation exists and is computable in_polynomial time for three agents with subadditive cost functions, improving upon the previous (2 + √6) approximation for additive cost functions. This result requires extensive case analysis. Christoforidis and Santorinaios (2024) independently claim the same approximation for additive cost functions;however, we provide a counter-example to their algorithm. We expand the number of agents to any number to get the same approximation guarantee with the assumption of partially identical ordering (IDO) for the cost functions. Additionally, we establish that a tEFX allocation is achievable for three agents if one has an additive 2-ratio bounded cost function, while the others may have general monotone cost functions. This is an improvement from the prior requirement of two agents with additive 2-ratio bounded cost functions. This allocation can also be extended to agent groups with identical valuations. Further, we show various analyses of EFX allocations for chores, such as the relaxations for additive α-ratio-bounded cost functions. © 2024, CC BY-NC-SA.

关键词： Cost functions

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 IEEE.

关键词： Resource allocation

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Supporting Bayesian modelling workflows with iterative filtering for multiverse analysis

arXiv

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arXiv 2024年

作者： Riha, Anna Elisabeth Siccha, Nikolas Oulasvirta, Antti Vehtari, Aki Department of Computer Science Aalto University Finland Department of Information and Communications Engineering Aalto University Finland

When building statistical models for Bayesian data analysis tasks, required and optional iterative adjustments and different modelling choices can give rise to numerous candidate models. In particular, checks and evaluations throughout the modelling process can motivate changes to an existing model or the consideration of alternative models to ultimately obtain models of sufficient quality for the problem at hand. Additionally, failing to consider alternative models can lead to overconfidence in the predictive or inferential ability of a chosen model. The search for suitable models requires modellers to work with multiple models without jeopardising the validity of their results. Multiverse analysis offers a framework for transparent creation of multiple models at once based on different sensible modelling choices, but the number of candidate models arising in the combination of iterations and possible modelling choices can become overwhelming in practice. Motivated by these challenges, this work proposes iterative filtering for multiverse analysis to support efficient and consistent assessment of multiple models and meaningful filtering towards fewer models of higher quality across different modelling contexts. Given that causal constraints have been taken into account, we show how multiverse analysis can be combined with recommendations from established Bayesian modelling workflows to identify promising candidate models by assessing predictive abilities and, if needed, tending to computational issues. We illustrate our suggested approach in different realistic modelling scenarios using real data examples. © 2024, CC BY.

关键词： Quality control

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Matrix Manifold Precoder Design for Massive MIMO High-Speed Railway communications with Channel Prediction 24

Matrix Manifold Precoder Design for Massive MIMO High-Speed ...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Sun, Rui Liu, Guan-Lin Sun, Chen Shi, Ding Lu, An-An Gao, Xiqi Xia, Xiang-Gen Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

ISBN: (纸本)9798350363760

In high-speed railway (HSR) communications, the channel suffers from severe channel aging effect caused by the high mobility. To address this issue, we investigate the precoder design against channel aging in massive multiple-input multiple-output (MIMO) systems with channel prediction. First of all, we introduce the concept of the quadruple beams (QBs), and establish a QB based channel model with sampled quadruple steering vectors. Then, the upcoming space domain channel can achieve a higher accuracy by channel prediction. We consider the precoder design on the Riemannian submanifold formed by the precoders satisfying the total power constraint (TPC). The Riemannian conjugate gradient (RCG) method is proposed to solve the problem on the manifold. The RCG method mainly involves the matrix multiplication and avoids the need of matrix inversion of the transmit antenna dimension. The simulation results demonstrate the effectiveness of the proposed channel model and the superiority of the RCG method for precoder design against channel aging. © 2024 IEEE.

关键词： MIMO systems

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Field Trial of an ML-Assisted Phase-Sensitive OTDR for Traffic Monitoring in Smart Cities

Field Trial of an ML-Assisted Phase-Sensitive OTDR for Traff...

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International Conference on Transparent Optical Networks

作者： Robson A. Colares Evandro Conforti Letícia Rittner Darli A. A. Mello Department of Communications School of Electrical and Computer Engineering Universidade de Campinas (UNICAMP) Campinas SP Brasil Department of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering Universidade de Campinas (UNICAMP) Campinas SP Brasil

ISBN: (数字)9798350377323

ISBN: (纸本)9798350377330

We present a field trial of cost-effective distributed optical fiber sensing (DOFS) for traffic intensity estimation. The field trial uses a buried single-mode fiber installed along the university campus intended for communications and research experimentation. Acquired $\phi$-OTDR traces are concatenated and transformed into a space-time domain to form images. The resulting images are then processed by a MobileNetv2 model for car counting. Training is carried out by a set of collected images labeled by traffic information gathered automatically from camera videos processed with the novel YOLOv8 model. The results indicate that the implemented setup successfully counts heavy vehicles with high accuracy. The setup is also applied to data sets collected over entire days, showcasing its successful application for traffic monitoring in smart cities.

关键词： Training Optical fibers Smart cities Urban planning Optical fiber cables Estimation Traffic control

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Particle Swarm Optimization for Efficient Brain Tumor Classification Using InceptionV3 Deep Learning Model 9

Particle Swarm Optimization for Efficient Brain Tumor Classi...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Akhila, P. Prabaharan, G. Pandiiyan, K. Swarna, S.L. Hussain, A. Sakthivelu, U. B V Raju Institute of Technology Department of Artificial Intelligence and Machine Learning Telangana Narsapur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Srm Arts and Science College Department of Computer Science with Artificial Intelligence Kattankulathur Chengalpattu India Sona College of Technology Department of Computer Science Science and Engineering Junction Main Road Tamil Nadu Salem India College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350377972

This research proposes a novel framework for brain tumor classification utilizing a combination of deep learning and optimization techniques. The framework employs an autoencoder network for image preprocessing, followed by SegNet for accurate tumor segmentation. The core classification is performed using the InceptionV3 model, further optimized through Particle Swarm Optimization (PSO) to enhance predictive performance. The proposed system achieved a classification accuracy of 98.75% on a dataset of 7,023 MRI images, demonstrating its effectiveness in accurately classifying brain tumors. This research contributes to the advancement of medical image analysis and provides a valuable tool for supporting clinicians in the early diagnosis and treatment of brain tumors. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Matrix Manifold Precoder Design for User-Centric Network Massive MIMO

Matrix Manifold Precoder Design for User-Centric Network Mas...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Sun, Rui You, Li Lu, An-An Sun, Chen Xiang, Ziyu Gao, Xiqi Xia, Xiang-Gen Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

ISBN: (纸本)9798350351255

In this paper, we investigate the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with matrix manifold optimization. In UCN mMIMO systems, each user terminal (UT) is served by a subset of the base stations (BSs) instead of all BSs, lowering the dimension of the precoders to be designed. Each BS in the system has a power constraint. By proving that the precoder set satisfying the constraints forms a Riemannian submanifold, we transform the constrained precoder design problem in Euclidean space as an unconstrained one on the Riemannian submanifold. Riemannian ingredients, including orthogonal projection, Riemannian gradient, retraction and vector transport, of the problem on the Riemannian submanifold are further derived, with which the Riemannian conjugate gradient (RCG) design method is proposed for solving the unconstrained problem. The proposed method avoids the inverses of large dimensional matrices. The complexity analyses show the high efficiency of RCG precoder design. Simulation results demonstrate the superiority of the proposed precoder design and the high efficiency of the UCN mMIMO system. © 2024 IEEE.

关键词： MIMO systems

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