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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Omar A. Saad Ahmed A. Etman Mai A. Abdel-Malek Mohamed Azab Department of Computer and Communications Engineering Faculty of Engineering Alexandria University Alexandria Egypt Department of Electrical and Computer Engineering The University of Arizona AZ USA Department of Computer and Information Sciences Virginia Military Institute VA USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

With next-generation communication technologies becoming more integrated into critical infrastructure, securing these networks against sophisticated threats has become paramount. One of the most persistent threats to this communication is the growing presence of fake base stations (FBS). These FBSs are malicious entities that pose as legitimate base stations to intercept, manipulate, or exploit mobile communications, leading to serious privacy breaches. While some existing literature addresses the detection of such malicious entities, detecting FBSs has traditionally been the responsibility of network operators, who rely on tools like signal strength analysis and radio frequency (RF) monitoring to identify unusual patterns or rogue signals. However, these centralized methods often struggle to detect FBSs in complex environments characterized by high mobility, dense urban areas, and sophisticated spoofing techniques. This paper proposes a scalable and pervasive blockchain-based crowd-sourcing mechanism to enable extensive device participation in identifying FBSs. In a fully automated and distributed manner, the proposed platform uses vetted and verified information recorded on the blockchain to identify and report suspicious FBSs in the area. This allows other users to dissociate from these malicious entities automatically. A reputation management system is also employed to ensure the reliability and accountability of participating users. Using qualitative and quantitative evaluations, the proposed approach demonstrated effectiveness and efficiency in achieving all targeted goals.

关键词： Crowdsourcing Radio frequency Base stations Accuracy Urban areas Privacy breach Blockchains Security Reliability Radiofrequency identification

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Achievable Rate Analysis and Optimization of Double-RIS Assisted Spatially Correlated MIMO with Statistical CSI

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China communications 2025年第1期22卷 139-157页

作者： Xu Kaizhe Guo Jiajia Zhang Jun Jin Shi Ma Shaodan Department of Communications and Networking Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China State Key Laboratory of Internet of Things for Smart City University of MacaoMacao SARChina Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao SARChina

Reconfigurable intelligent surface(RIS)is a novel meta-material which can form a smart radio environment by dynamically altering reflection directions of the impinging electromagnetic *** the prior literature,the inter-RIS links which also contribute to the performance of the whole system are usually neglected when multiple RISs are *** this paper we investigate a general double-RIS assisted multiple-input multiple-output(MIMO)wireless communication system under spatially correlated non line-of-sight propagation channels,where the cooperation of the double RISs is also *** design objective is to maximize the achievable ergodic rate based on full statistical channel state information(CSI).Specifically,we firstly present a closedform asymptotic expression for the achievable ergodic rate by utilizing replica method from statistical *** a full statistical CSI-enabled optimal design is proposed which avoids high pilot training overhead compared to instantaneous CSI-enabled *** further reduce the signal processing overhead and lower the complexity for practical realization,a common-phase scheme is proposed to design the double *** results show that the derived asymptotic ergodic rate is quite accurate even for small-sized antenna *** the proposed optimization algorithm can achieve substantial gain at the expense of a low overhead and ***,the cooperative double-RIS assisted MIMO framework is proven to achieve superior ergodic rate performance and high communication reliability under harsh propagation environment.

关键词： common reflection pattern double RISs large system analysis MIMO replica method

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Radio resource management and path planning in intelligent transportation systems via reinforcement learning for environmental sustainability

Radio resource management and path planning in intelligent t...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Norouzi, S. Azarasa, N. Abedi, M.R. Mokari, N. Seyedabrishami, S.E. Saeedi, H. Jorswieck, E.A. Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Civil and Environmental Engineering Tarbiat Modares University Tehran Iran College of Engineering Doha University for Science and Technology Doha Qatar Institute of Communications Technology TU Braunschweig Lower Saxony Germany

ISBN: (纸本)9781032803722

Efficient and dynamic path planning has become an important topic for urban areas with larger density of connected vehicles (CV) which results in reduction of travel time and directly contributes to environmental sustainability through reducing energy consumption. CVs exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the vehicle-to-infrastructure (V2I) messages to the Base-station (BS) to improve situation awareness on urban roads. In this paper, we investigate radio resource management (RRM) in such a framework to minimize the age of information (AoI) so as to enhance path planning results. We use the fact that V2I messages with lower AoI value result in less error in estimating the road capacity and more accurate path planning. Through simulations, we compare road travel times and volume over capacity (V/C) against different levels of AoI and demonstrate the promising performance of the proposed framework. © 2025 The Author(s).

关键词： Resource allocation

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Universal Joint Source-Channel Coding for Modulation-Agnostic Semantic Communication

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IEEE Journal on Selected Areas in communications 2025年

作者： Huh, Yoon Seo, Hyowoon Choi, Wan Departement of Electrical and Computer Engineering Institute of New Media and Communications Seoul08826 Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

From the perspective of joint source-channel coding (JSCC), there has been significant research on utilizing semantic communication, which inherently possesses analog characteristics, within digital device environments. However, a single-model approach that operates modulation-agnostically across various digital modulation orders has not yet been established. This article presents the first attempt at such an approach by proposing a universal joint source-channel coding (uJSCC) system that utilizes a single-model encoder-decoder pair and trained vector quantization (VQ) codebooks. To support various modulation orders within a single model, the operation of every neural network (NN)-based module in the uJSCC system requires the selection of modulation orders according to signal-to-noise ratio (SNR) boundaries. To address the challenge of unequal output statistics from shared parameters across NN layers, we integrate multiple batch normalization (BN) layers, selected based on modulation order, after each NN layer. This integration occurs with minimal impact on the overall model size. Through a comprehensive series of experiments, we validate that the modulation-agnostic semantic communication framework demonstrates superiority over existing digital semantic communication approaches in terms of model complexity, communication efficiency, and task effectiveness. © 1983-2012 IEEE.

关键词： Vector quantization

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Counterfactual brain amyloid pathology image synthesis

Counterfactual brain amyloid pathology image synthesis

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Medical Imaging 2025: Image Processing

作者： Bilgel, Murat Zuo, Lianrui Resnick, Susan M. Carass, Aaron Laboratory of Behavioral Neuroscience National Institute on Aging BaltimoreMD21224 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781510685901

We propose a U-Net-based MRI-to-PET image translation approach to synthesize distribution volume ratio (DVR) images for the amyloid PET radiotracer 11C-Pittsburgh compound B (PiB). Given a structural MRI and a binary map indicating the spatial presence of amyloid pathology, our network can generate PiB DVR images to reflect the specified spatial distribution-based on the binary map-of amyloid pathology. This approach allows for the synthesis of counterfactual PiB DVR images. Compared to using a separate U-Net per PiB status, our method enables the synthesis of PiB DVR images with improved SSIM (among PiB+, 0.818 vs. 0.783, p −6) and PSNR (among PiB- & PiB+, 32.475 vs. 31.729, p = 5.83 × 10−6). It also generates counterfactual PiB- DVR images that are more difficult to identify as counterfactual vs. synthetic "factual" (classification accuracy = 0.657 vs. 0.714). We show that the counterfactual PiB- images can be used for personalized monitoring of global brain amyloid burden and demonstrate that our proposed measure correlates highly with mean cortical DVR, an established index that requires anatomical labeling (Pearson correlation = 0.856 for testing set PiB+ scans). © 2025 SPIE.

关键词： 3D reconstruction

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Natural Language Generation in AI: Developing Human-Like Text Through Deep Learning 1

Natural Language Generation in AI: Developing Human-Like Tex...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Arabelli, Rajeshwarrao Gupta, Suneet Prakash, Nilima Ali, Zainab School of Engineering SR University Department of Electronics and Communications Engineerin Warangal India School of Computer Science and Engineering Galgotias University Greater Noida India Northcap University Gurugram India Vivekananda Global University Department of Journalism & Mass Communication Jaipur India

ISBN: (纸本)9798331518578

Natural Language Generation (NLG) is a subset of artificial intelligence that has as its goal to develop techniques to synthesize human language. This paper focuses on the finding of NLG as a field of study as well as how it benefitted from ideas such as deep learning. Next, we consider the development of NLG models, from standard Recurrent Neural Networks to Long Short-Term Memory networks and from Transformers like GPT. Transformer models have proven to be magnificent for the NLG as they remove the drawbacks of previous models and have improved the long-range dependencies and have synthesized appropriate topic-based content. This paper assesses the merits and the demerit of these models and the areas where that they are used such as healthcare, content creation and customer care. Transformer models are particularly good at producing high-quality text, for which it comes with severe difficulties though. The research for this paper aims to consider future developments of NLG with regards to scalability and the potential for change across fields. © 2025 IEEE.

关键词： Contrastive Learning

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MIRA: A Method of Federated MultI-Task Learning for LaRge LAnguage Models

IEEE Networking Letters

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IEEE Networking Letters 2025年

作者： Elbakary, Ahmed Ben Issaid, Chaouki Elbatt, Tamer Seddik, Karim Bennis, Mehdi University of Oulu Finland Department of Electronics and Communications Engineering American University in Cairo Egypt Department of Computer Science and Engineering American University in Cairo Egypt Department of Electronics and Communications Engineering Cairo University Egypt

In this paper, we introduce a method for fine-tuning Large Language Models (LLMs), inspired by Multi-Task learning in a federated manner. Our approach leverages the structure of each client's model and enables a learning scheme that considers other clients' tasks and data distribution. To mitigate the extensive computational and communication overhead often associated with LLMs, we utilize a parameter-efficient fine-tuning method, specifically Low-Rank Adaptation (LoRA), to reduce the number of trainable parameters. Experimental results, with different datasets and models, demonstrate the proposed method's effectiveness compared to existing frameworks for federated fine-tuning of LLMs in terms of global and local performances. The proposed scheme outperforms existing baselines by achieving lower local loss for each client, while maintaining comparable global performance. © 2019 IEEE.

关键词： Federated learning

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Multi-Representation Spatial-Temporal Graph Convolutional Networks for Network Traffic Prediction

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IEEE Internet of Things Journal 2025年

作者： Yang, Yang He, Yechen Zhao, Binnan Wu, Celimuge Gao, Zhipeng Rui, Lanlan Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan

With the rapid proliferation of the Internet of Things, network traffic prediction has become crucial for intelligent network management, enabling more reliable and flexible services for a vast array of IoT devices and applications. The heterogeneous and dynamic nature of IoT networks introduces complex spatial relations and underlying periodic dependencies in spatial-temporal graphs that existing methods struggle to model effectively. In this paper, we propose Multi-Representation Spatial-Temporal Graph Convolutional Networks (MRSTGCN), a novel unified framework specifically designed to address these challenges. MRSTGCN integrates a Multi-Representation Graph Convolutional Network (MRGCN) module to model node heterogeneity and complex traffic propagation, and two complementary embedding modules - Historical Embedding and Temporal Embedding - to capture and fuse periodic dependencies across different fine-grained temporal cycles. Extensive experiments are conducted on two network traffic datasets, and the results demonstrate that MRSTGCN achieves state-of-the-art performance with obvious improvements in MAE, RMSE and MAPE on three prediction horizons. © 2025 IEEE.

关键词： Convolutional neural networks

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Novel Distributed Approach for Next Best Offer using XGboost

Novel Distributed Approach for Next Best Offer using XGboost

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Shatakshi Chaudhari Prerana Malladi Sai Ashwini D Bundele Department of Computer Engineering Pune Institute of Computer Technology Pune India Department of Electronics and Communications Engineering Sardar Vallabhbhai National Institute of Technology Surat India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Product recommendations have become integral to business development and customer engagement strategies. While traditional techniques like market basket analysis are effective in retail, they are insufficient for the financial sector due to the diversity of banking products and the lack of direct dependency among them. This research paper proposes a novel distributed framework for developing an automated recommendation system leveraging XGBoost to predict banking product purchases based on categorized customer profiles. The framework integrates distributed computing technologies such as Hadoop for data storage and PySpark for scalable data processing. The system classifies customers into distinct categories based on their transaction history, demographic attributes, and banking behaviors to deliver personalized product suggestions. Model performance is evaluated using metrics such as the ROC curve, precision, recall, and F1-score. Empirical results demonstrate the system’s effectiveness in enhancing lead generation, targeting high-potential customers, and improving customer satisfaction in real-world financial markets.

关键词： Measurement Technological innovation Memory Banking Lead History Ensemble learning Informatics Recommender systems Business

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Adaptive Stabilization Control by Deep Reinforcement Learning for Hovering Drone Surveillance

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IEEE Transactions on Mobile Computing 2025年

作者： Lee, Chao-Yang Tsai, Ang-Hsun Wang, Li-Chun National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Taiwan Feng Chia University Department of Communications Engineering Taiwan National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Taiwan

This paper proposes an adaptive stabilization control mechanism by using deep reinforcement learning (DRL) for hovering drones that have to execute a surveillance task for a long time. For long-endurance flights, we design and implement a buoyancy-aided unmanned aerial vehicle (UAV) that can use buoyancy lift to decrease the weight and increase the battery capacity so that the flight time can be significantly extended. However, the balloons of the buoyancy-aided UAV can cause "an inverted pendulum effect" and an instability issue on the drone attitude because the increased surface is easily affected by the gusty wind. We propose a buoyancy-aided adaptive stabilization control (BAASC) method with the DRL to stabilize the attitude and extend the flight time of the quadrotor-based buoyancy-aided UAV. This proposed model can immediately control the speeds of all rotors to balance the attitude based on the current state of the drone. Therefore, the degree of swing can be stabilized, and the inverted pendulum effect can be eliminated. The experimental results reveal that the designed buoyancy-aided UAV with the proposed BAASC scheme can effectively stabilize the attitude to extend the flight time by 112.8% compared with a nonbuoyancy-aided UAV under a gusty wind disturbance. 2002-2012 IEEE.

关键词： Buoyancy

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