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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks

IEEE Access 2025年 13卷 74917-74929页

作者： Moaty, Ahmed Hamad, Ehab K. I. Hussein, Aziza I. Mourad Mabrook, M. Donkol, A.A. Nahda University Faculty of Engineering Electrical Engineering Dept Beni-Suef Egypt Aswan University Faculty of Engineering Electrical Engineering Department Aswan81542 Egypt Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia Faculty of Navigation Science & Space Technology Beni-Suef University Space communication Dept Egypt South Valley University Electrical Engineering Faculty of engineering Communication and electronics department Egypt

Millimeter-wave (mmWave) communication systems utilize narrow beamforming to ensure adequate signal power. However, beam alignment requires significant training overhead, especially in high-mobility scenarios. Previous research has utilized synthetic data for position-aided beam prediction, which does not fully capture real-world complexities. In this work, an Enhanced Convolutional Neural Network model (E-CNN) is proposed for optimal prediction of beam indices with the aid of real-world GPS position data. The proposed E-CNN model has been investigated across nine different scenarios from the DeepSense 6G dataset and compared against the conventional algorithms. For 64-beams Scenario 1, the E-CNN model showed an increase in average top-1 accuracy from 55.57% to 63.92%, and in case of 32-beams, the accuracy increased from 71.34 % to 82.06%. For 16-beams, the accuracy increased from 86.17% to 94.64 %, while for 8-beams, the accuracy increased from 90.24% to 97.11%. In addition, besides showing significant power loss reduction in various scenarios, the proposed E-CNN model has demonstrated robustness regarding real-word conditions and adaptability for various beam setups. The model realized as high as a 50% power loss reduction in arguably the most challenging graphs, which is an exercise in reliability. This research fills the existing gap between the simulated aid beam alignment and real-world position beam aided alignment, which can be useful in improving beamforming in the upcoming wireless networks. © 2013 IEEE.

关键词： Convolutional neural networks

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Zero-dynamics attack detection based on data association in feedback pathway

Cognitive Robotics

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Cognitive Robotics 2025年 5卷 126-139页

作者： Zhang, Zeyu Li, Hongran Todo, Yuki Division of Electrical Engineering and Computer Science Kanazawa University Ishikawa Kanazawa9201192 Japan School of Computer Engineering Jiangsu Ocean University Jiangsu Lianyungang222005 China Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa9201192 Japan

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results. © 2025

关键词： Cyber attacks

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A Review of Anonymization Algorithms and Methods in Big Data

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Annals of Data science 2025年第1期12卷 253-279页

作者： Shamsinejad, Elham Banirostam, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution to this challenge. In this study, we examined various data anonymization methods, including differential privacy, advanced encryption, and strong access controls. In addition, the operation, advantages, disadvantages, and use of these methods, the challenges of adapting these methods to big data, and possible solutions for them were also examined. Our results show that traditional data anonymization methods lack scalability, leading to privacy breaches and data loss. When faced with large volumes of data, these methods may not be able to fully process the data. Also, these methods may be ineffective against re-identification attacks, linkage attacks, and inference attacks. We introduced emerging methods that are capable of providing improved privacy with minimal data loss. These methods have scalability for big data. Finally, we examined future research works and raised important questions that can help improve existing algorithms or develop new methods, better manage the complexity and scale of unstructured data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

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Representing a Model for the Anonymization of Big Data Stream Using In-Memory Processing

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Annals of Data science 2025年第1期12卷 223-252页

In light of the escalating privacy risks in the big data era, this paper introduces an innovative model for the anonymization of big data streams, leveraging in-memory processing within the Spark framework. The approach is founded on the principle of K-anonymity and propels the field forward by critically evaluating various anonymization methods and algorithms, benchmarking their performance with respect to time and space complexities. A distinctive formula for optimized cluster determination in the K-means algorithm is presented, along with a novel tuple expiration time strategy for the efficient purging of clusters. The integration of these components into Spark’s RDD and MLlib modules results in a significant decrease in execution time and data loss rates, even with increasing data volumes. The paper’s notable contributions are its methodological advancements that offer a robust, scalable solution for data anonymization, safeguarding user privacy without sacrificing data utility or processing efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

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Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

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Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

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Joint Distributed Estimation and Random Gain Channel Estimation with Beta Prior

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IEEE Communications Letters 2025年第5期29卷 1008-1012页

作者： Zayyani, Hadi Korki, Mehdi Torkamani, Razieh Department of Electrical and Computer Engineering Qom Iran Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne Australia Bu-Ali Sina University Faculty of Engineering Department of Electrical Engineering Hamedan Iran

In this letter, we propose a joint distributed estimation and channel estimation algorithm for wireless sensor networks (WSNs). We assume a random gain channel model with a Beta prior, where the channel gain is an attenuation factor ranging between zero and one. To estimate the augmented unknown vector, which includes both the unknown vector and the channel gain vector, we employ Maximum A Posteriori (MAP) estimation. This is achieved through an iterative steepest-descent method to find the MAP estimators for both the unknown vector and the channel gain vector. We mathematically derive the optimum combination coefficients that minimize disturbance. Additionally, we propose a separate approach for channel gain estimation using Least Squares (LS) and provide the convexity analysis of the cost function along with the sufficient conditions for the convergence of the iterative channel gain estimator. Simulation results demonstrate the effectiveness of the proposed algorithm compared to some other algorithms in the literature, specially when channel gains are modeled with a Beta prior. © 1997-2012 IEEE.

关键词： Cost functions

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Approximate Message Passing for Spatial Modulation with Structured Sparsity

Approximate Message Passing for Spatial Modulation with Stru...

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14th International ITG Conference on Systems, Communications and Coding, SCC 2025

作者： Franz, Daniel Kuhn, Volker University of Rostock Faculty of Computer Science and Electrical Engineering Germany

ISBN: (纸本)9798331522896

Multiple antennas at transmitter and receiver have significantly improved the performance of wireless communications systems. Traditionally, space-time coding, beamforming, or spatial multiplexing are applied to achieve diversity, SNR, and multiplexing gains, respectively. A different concept is pursued with Generalized Spatial Modulation (GSM). It conveys information through the selection of active antennas from a set of available antennas in addition to the transmission of conventional modulation symbols. This results in sparse transmit vectors, and detection algorithms known from compressed sensing can be applied. In this paper, Vector Approximate Message Passing (VAMP) is considered for the detection of GSM signals in massive Multiple-Input Multiple-Output (MIMO) systems. Its performance is improved by introducing structured redundancy in space or in time. In order to exploit this redundancy, the VAMP algorithm has to be extended by an additional graph layer. The resulting performance is evaluated by analyzing the error rates and the throughput obtained from Monte Carlo simulations. © 2025 IEEE.

关键词： Space time codes

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A Novel Audio-Visual Multimodal Semi-Supervised Model Based on Graph Neural Networks for Depression Detection

A Novel Audio-Visual Multimodal Semi-Supervised Model Based ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yaqin Sun, Chenjian Dong, Yihong Faculty of Electrical Engineering and Computer Science Ningbo University China

ISBN: (纸本)9798350368741

There is a significant correlation between depression, verbal behavior, and facial expressions. By analyzing patients' audio and facial visuals, depression assessments can be conducted. However, existing work is predominantly based on single modalities. Additionally, acquiring a sufficient amount of labeled data in clinical settings is challenging and costly. To leverage multimodal audio-visual data while addressing the issue of lacking trainable labeled data, we propose an audiovisual multimodal semi-supervised depression detection model based on Graph Neural Networks (AVS-GNN). This model first extracts dual-modality temporal information from audio features and facial visual features of patients and obtains modality-specific high-level embedding representations through graph representation learning. Subsequently, it utilizes graph-based contrastive unsupervised learning to capture consistency information between pairs of unlabeled samples across different modalities and to facilitate cross-modal interactions. We specifically designed a hybrid weighted pseudo-labeling strategy to assign high-confidence pseudo-labels to unlabeled data and further retraining the model. Experiments on two depression datasets show that this model outperforms baseline methods across all evaluation metrics. © 2025 IEEE.

关键词： audio-visual multimodal depression detection graph neural networks semi-supervised

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Generating Temporal Contact Graphs Using Random Walkers

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IEEE Transactions on Network science and engineering 2025年第3期12卷 1649-1659页

作者： Almasan, Anton-David Shvydun, Sergey Scholtes, Ingo Van Mieghem, Piet Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands 50 Würzburg97074 Germany

We study human mobility networks through timeseries of contacts between individuals. Our proposed Random Walkers Induced temporal Graph (RWIG) model generates temporal graph sequences based on independent random walkers that traverse an underlying graph in discrete time steps. Co-location of walkers at a given node and time defines an individual-level contact. RWIG is shown to be a realistic model for temporal human contact graphs, which may place RWIG on a same footing as the Erdos–Renyi (ER) and Barabasi–Albert (BA) models for fixed graphs. Moreover, RWIG is analytically feasible: we derive closed form solutions for the probability distribution of contact graphs. © 2013 IEEE.

关键词： Generative adversarial networks

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Multi-orthogonal-oppositional enhanced African vultures optimization for combined heat and power economic dispatch under uncertainty

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Neural Computing and Applications 2025年第8期37卷 6097-6123页

作者： Rizk-Allah, Rizk M. Snášel, Václav Hassanien, Aboul Ella Basic Engineering Science Department Faculty of Engineering Menoufia University Shebin El-Kom32511 Egypt Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava70800 Czech Republic Faculty of Computers and AI Cairo University Giza Egypt

This paper attempts to conceptualize a potent methodology by combining the African vultures optimization algorithm (AVOA) with a multi-orthogonal-oppositional strategy (M2OS), named AVO-M2OS, to address the nonconvexity and multidimensional nature of the combined heat and power economic dispatch (CHPED) problem under both crisp and uncertainty aspects. The AVO-M2OS uses the M2OS to simultaneously explore the search region, improving solutions’ diversity as well as solution quality. Therefore, AVO-M2OS can perform deeper exploration and exploitation features and thus mitigate the trapping at local optima, especially when tackling the more complicated nature of the CHPED problem. A three-stage analysis is conducted to assess the effectiveness of the proposed AVO-M2OS algorithm. During the first stage, the algorithm’s performance is evaluated on benchmark problems such as CEC 2005 and CEC 2019, employing statistical verifications and convergence characteristics. In the second stage, the significance of the results is evaluated using the nonparametric Friedman test to demonstrate that the results did not occur by chance. The results indicate that the AVO-M2OS algorithm outperforms the best existing algorithm (AVOA) by an average rank of the Friedman test exceeding 26% for the CEC 2005 suite while outperforming the gray wolf optimization (GWO) by 60% for the CEC 2019 suite. Moreover, the AVO-M2OS demonstrates exceptional performance compared to existing state-of-the-art algorithms, surpassing the best algorithm available by an average rank of the Friedman test that exceeds 41%. Finally, the AVO-M2OS’s applicability is achieved by minimizing the operational costs by finding the optimal power and heat generation scheduling for the CHPED problem. The recorded results realize that the AVO-M2OS algorithm offers accurate performance compared to competing optimizers, where it saves the operational cost of the 48-unit system by 24% on the original AVO variant. Furthermore, the u

关键词： Quality control

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