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Active RIS-Aided Massive MIMO With Imperfect CSI and Phase Noise

IEEE Transactions on Wireless Communications 2025年

作者： Peng, Zhangjie Zhu, Jianchen Pan, Cunhua Zhang, Zaichen Da Costa, Daniel Benevides Elkashlan, Maged Karagiannidis, George K. Shanghai Normal University College of Information Mechanical and Electrical Engineering Shanghai200234 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece Artificial Intelligence and Cyber Systems Research Center Lebanon

As a recently proposed reconfigurable intelligent surface (RIS) architecture, active RIS has drawn considerable interest. The important feature of the active RIS is its ability to strengthen the impinging signals to mitigate the multiplicative fading effect inherent in passive RIS-aided systems. Herein, we explore the performance of an active RIS-aided uplink multi-user massive multiple-input multiple-output (MIMO) system, considering the phase noise at the RIS. Furthermore, a two-timescale scheme is utilized, where the base station (BS) beamforming is designed based on the instantaneous aggregated channel state information (CSI), while the statistical CSI is used for designing the phase shifts of the active RIS. In addition, the linear minimum mean square error (LMMSE) estimator is adopted to estimate the aggregated channel, which combines both the cascaded and direct channels. According to the estimated channel, a closedform expression for the lower bound of achievable rate is derived. Based on the theoretical expressions, the power scaling laws for the considered system are also investigated. Specifically, when each user’s transmit power is proportionally reduced by the quantity of BS antennas M or RIS elements N, we find that the amplified thermal noise causes the lower bound of the achievable rate to approach zero as M or N tends to infinity. Moreover, an optimization approach based on a genetic algorithm (GA) is introduced to obtain the optimal phase shifts for maximizing the achievable rate. Numerical results reveal that the active RIS can greatly enhance the performance of the massive MIMO system compared to its passive counterpart. © 2002-2012 IEEE.

关键词： Genetic algorithms

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Dynamic Modeling and Fault Response Analysis of SMR-Integrated Power Grid

Dynamic Modeling and Fault Response Analysis of SMR-Integrat...

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IEEE Green Technologies Conference (IEEE-Green)

作者： Javier Bejarano Paras Mandal Department of Electrical and Computer Engineering Power and Renewable Energy Systems (PRES) Lab. The University of Texas at El Paso TX USA

ISBN: (数字)9798331520182

ISBN: (纸本)9798331520199

Small Modular Reactors (SMRs) are an emerging technology with load-following capabilities, offering significant potential for enhancing grid stability and resilience. This paper presents the development of an SMR model by replacing an existing generator in the IEEE 39-bus system with a synchronous machine round rotor configured to reflect the operational characteristics of an SMR. The paper focuses on analyzing the dynamic fault response of the SMR at various operating levels, including minimum generation, half, and maximum capacity. Key metrics such as terminal voltage (p.u.) are evaluated to assess the SMR's ability to maintain stability during and after fault scenarios. By examining the SMR's performance under different generation conditions, this paper provides valuable insights into the feasibility of integrating SMRs into modern power systems and their contributions to grid stability during transient events.

关键词： Power system dynamics Green products Power system stability Stability analysis Power system reliability Reliability Inductors Oscillators Resilience Load modeling

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Approximately Counting and Sampling Hamiltonian Motifs in Sublinear Time

arXiv

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arXiv 2025年

作者： Eden, Talya Levi, Reut Ron, Dana Rubinfeld, Ronitt Department of Computer Science Bar Ilan University Israel Efi Arazi School of Computer Science Reichman University Israel School of Electrical and Computer Engineering Tel Aviv University Israel Computer Science and Artificial Intelligence Laboratory MIT United States

Counting small subgraphs, referred to as motifs, in large graphs is a fundamental task in graph analysis, extensively studied across various contexts and computational models. In the sublinear-time regime, the relaxed problem of approximate counting has been explored within two prominent query frameworks: the standard model, which permits degree, neighbor, and pair queries, and the strictly more powerful augmented model, which additionally allows for uniform edge sampling. Currently, in the standard model, (optimal) results have been established only for approximately counting edges, stars, and cliques, all of which have a radius of one. This contrasts sharply with the state of affairs in the augmented model, where algorithmic results (some of which are optimal) are known for any input motif, leading to a disparity which we term the "scope gap" between the two models. In this work, we make significant progress in bridging this gap. Our approach draws inspiration from recent advancements in the augmented model and utilizes a framework centered on counting by uniform sampling, thus allowing us to establish new results in the standard model and simplify on previous results. In particular, our first, and main, contribution is a new algorithm in the standard model for approximately counting any Hamiltonian motif in sublinear time, where the complexity of the algorithm is the sum of two terms. One term equals the complexity of the known algorithms by Assadi, Kapralov, and Khanna (ITCS 2019) and Fichtenberger and Peng (ICALP 2020) in the (strictly stronger) augmented model and the other is an additional, necessary, additive overhead. Our second contribution is a variant of our algorithm that enables nearly uniform sampling of these motifs, a capability previously limited in the standard model to edges and cliques. Our third contribution is to introduce even simpler algorithms for stars and cliques by exploiting their radius-one property. As a result, we simplify all previo

关键词： Stars

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A Novel Approach to Smart Grid Energy Management Using the Osprey Optimization Algorithm 4

A Novel Approach to Smart Grid Energy Management Using the O...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rudrapogu, Nissisolomon Hemalatha, N. Warkad, Sanjay B Jayanthi, A. Vinoth, K. Jayaram, B. Lecturer in Eee Nissisolomon Rudrapogu Mbts Government Polytechnic College Department of Electrical and Electronics Engineering Section Andhra Pradesh Guntur522005 India Bharath Institute of Higher Education and Research Department of Electrical and Electronics Engineering Tamil Nadu Chennai600073 India P. R. Pote Patil College of Engineering & Management Department of Electrical Engineering Maharashtra Amravati444602 India Jeppiaar Institute of Technology Department of Physics Tamil Nadu Sriperumbudur631502 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India Rmk Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India

ISBN: (纸本)9798331523923

Smart Grid Energy Management (SGEM) denotes the function of optimizing energy flow in the grid while enhancing grid efficiency and stability. One major issue is that increased integration of Renewable Energy Sources (RESs) may add a layer of complexity to EM, which may, in turn, reduce the reliability and stability of the grid. Keeping such challenges in mind, this paper proposes the Osprey Optimization Algorithm (OOA) on its part to enhance SGEM through various facets of load balancing and energy distribution. The objective of this method is to minimize the Mean Absolute Percentage Error (MAPE), enhancing the precision of energy predictions and optimizing grid performance. The OOA optimizes energy distribution in micro grids, improving efficiency and reducing power losses. The performance of the suggested OOA method is assessed by contrasting it with other optimization approaches that are currently in use, including Particle Swarm Optimization (PSO), Multi Objective Genetic Algorithm (MOGA), Sparrow Search Algorithm (SSA), and Multi Objective Wind Driven Optimization (MOWDO). The OOA method is implemented on the MATLAB platform. The outcomes show that the proposed OOA method provides superior performance in minimizing MAPE, achieving the lowest MAPE value of 0.28%, indicating its enhanced accuracy in energy demand forecasting and optimization. © 2025 IEEE.

关键词： Clean energy

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Improved Scalable Lipschitz Bounds for Deep Neural Networks

arXiv

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arXiv 2025年

作者： Syed, Usman Hu, Bin The Coordinated Science Laboratory The Department of Electrical and Computer Engineering University of Illinois Urbana–Champaign ChampaignIL61801 United States

Computing tight Lipschitz bounds for deep neural networks is crucial for analyzing their robustness and stability, but existing approaches either produce relatively conservative estimates or rely on semidefinite programming (SDP) formulations (namely the LipSDP condition) that face scalability issues. Building upon ECLipsE-Fast, the state-of-the-art Lipschitz bound method that avoids SDP formulations, we derive a new family of improved scalable Lipschitz bounds that can be combined to outperform ECLipsE-Fast. Specifically, we leverage more general parameterizations of feasible points of LipSDP to derive various closed-form Lipschitz bounds, avoiding the use of SDP solvers. In addition, we show that our technique encompasses ECLipsE-Fast as a special case and leads to a much larger class of scalable Lipschitz bounds for deep neural networks. Our empirical study shows that our bounds improve ECLipsE-Fast, further advancing the scalability and precision of Lipschitz estimation for large neural networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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Generative Adversarial Networks for Imputing Sparse Learning Performance 27th

Generative Adversarial Networks for Imputing Sparse Learnin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Liang Yeasin, Mohammed Lin, Jionghao Havugimana, Felix Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Monash University MelbourneVIC3800 Australia Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783031781711

Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring systems (ITSs) is crucial for tracking and assessing the learners’ progress and mastery of knowledge. However, the issue of data sparsity, characterized by unexplored questions and missing attempts, hampers accurate assessment and the provision of tailored, personalized instruction within ITSs. This paper proposes using the Generative Adversarial Imputation Networks (GAIN) framework to impute sparse learning performance data, reconstructed into a three-dimensional (3D) tensor representation across the dimensions of learners, questions and attempts. Our customized GAIN-based method computational process imputes sparse data in a 3D tensor space, significantly enhanced by convolutional neural networks for its input and output layers. This adaptation also includes the use of a least squares loss function for optimization and aligns the shapes of the input and output with the dimensions of the questions-attempts matrices along the learners’ dimension. Through extensive experiments on six datasets from various ITSs, including AutoTutor, ASSISTments and MATHia, we demonstrate that the GAIN approach generally outperforms existing methods such as tensor factorization and other generative adversarial network (GAN) based approaches in terms of imputation accuracy. This finding enhances comprehensive learning data modeling and analytics in AI-based education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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RA-DP: Rapid Adaptive Diffusion Policy for Training-Free High-frequency Robotics Replanning

arXiv

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arXiv 2025年

作者： Ye, Xi Yang, Rui Heng Jin, Jun Li, Yinchuan Rasouli, Amir Department of Computer Science and Technology Tsinghua University China Noah’s Ark Laboratory Huawei Technologies Canada Department of Electrical and Computer Engineering University of Alberta Canada Noah’s Ark Laboratory Huawei Technologies China

Diffusion models exhibit impressive scalability in robotic task learning, yet they struggle to adapt to novel, highly dynamic environments. This limitation primarily stems from their constrained replanning ability: they either operate at a low frequency due to a time-consuming iterative sampling process, or are unable to adapt to unforeseen feedback in case of rapid replanning. To address these challenges, we propose RA-DP, a novel diffusion policy framework with training-free high-frequency replanning ability that solves the above limitations in adapting to unforeseen dynamic environments. Specifically, our method integrates guidance signals which are often easily obtained in the new environment during the diffusion sampling process, and utilizes a novel action queue mechanism to generate replanned actions at every denoising step without retraining, thus forming a complete training-free framework for robot motion adaptation in unseen environments. Extensive evaluations have been conducted in both well-recognized simulation benchmarks and real robot tasks. Results show that RA-DP outperforms the state-of-the-art diffusion-based methods in terms of replanning frequency and success rate. Moreover, we show that our framework is theoretically compatible with any training-free guidance signal. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Allocation Multiplicity: Evaluating the Promises of the Rashomon Set

arXiv

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arXiv 2025年

作者： Jain, Shomik Wang, Margaret Creel, Kathleen Wilson, Ashia Institute for Data Systems and Society MIT United States Department of Electrical Engineering and Computer Science MIT United States Department of Philosophy and Religion Khoury College of Computer Sciences Northeastern University United States

The Rashomon set of equally-good models promises less discriminatory algorithms, reduced outcome homogenization, and fairer decisions through model ensembles or reconciliation. However, we argue from the perspective of allocation multiplicity that these promises may remain unfulfilled. When there are more qualified candidates than resources available, many different allocations of scarce resources can achieve the same utility. This space of equal-utility allocations may not be faithfully reflected by the Rashomon set, as we show in a case study of healthcare allocations. We attribute these unfulfilled promises to several factors: limitations in empirical methods for sampling from the Rashomon set, the standard practice of deterministically selecting individuals with the lowest risk, and structural biases that cause all equally-good models to view some qualified individuals as inherently risky. © 2025, CC BY.

关键词： Consensus algorithm

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Detection of Malicious Interpreted Code using ML to Optimize Parameters for Minimizing Prediction Errors

Detection of Malicious Interpreted Code using ML to Optimize...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Dhawan, Manoj Kuril, Devendra Panadiwal, Himanshu Rawat, Hitesh Rawat, Anjali Rawat, Romil School of Engineering Avantika University Ujjain India Shri Vaishnav Vidyapeeth Vishwavidyalaya Department of Information Technology Indore India University of Extremadura Department of Business Management and Economics Badajoz Spain University of Extremadura Department of Computer and Communication Technology Badajoz Spain LabGeoInf-Research LABoratory in GEOmatics and INFormation systems Rome Italy

ISBN: (纸本)9798331538538

Mobile devices face growing cybersecurity threats, including malware, data breaches, malicious applications, and social engineering attacks. Cybercriminals exploit these vulnerabilities to gain unauthorized access, disrupt communication, and compromise sensitive information. Conventional malware detection methods, such as signature-based and heuristic techniques, often struggle to identify evolving threats due to their reliance on predefined patterns. This study presents an advanced malware detection framework that integrates a Reservoir Network with a Deep Learning Approach (DLA) to enhance detection accuracy and minimize prediction errors. The proposed method utilizes two static features-API call permissions and alert signals- to improve detection efficiency. Experimental evaluation, conducted using the Drebin malware dataset, demonstrates that the proposed model achieves an accuracy of 99.7 %, reduces the false positive rate to below 1 %, and ensures optimal processing speed with a batch size of 64 and a learning rate of 0.001. Compared to traditional approaches, this framework exhibits superior adaptability to obfuscated malware while enabling real-time threat detection. The findings emphasize the effectiveness of machine learning (ML)-driven security mechanisms in addressing emerging cybersecurity threats. © 2025 IEEE.

关键词： computer viruses

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PathGPT: Leveraging Large Language Models for Personalized Route Generation

arXiv

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arXiv 2025年

作者： Marcelyn, Steeve Cuthbert Gao, Yucen Zhang, Yuzhe Gao, Xiaofeng Chen, Guihai Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

The proliferation of GPS-enabled devices has led to the accumulation of a substantial corpus of historical trajectory data. By leveraging these data for training machine learning models, researchers have devised novel data-driven methodologies that address the personalized route recommendation (PRR) problem. In contrast to conventional algorithms such as Dijkstra’s shortest path algorithm, these novel algorithms possess the capacity to discern and learn patterns within the data, thereby facilitating the generation of more personalized paths. However, once these models have been trained, their application is constrained to the generation of routes that align with their training patterns. This limitation renders them less adaptable to novel scenarios and the deployment of multiple machine learning models might be necessary to address new possible scenarios, which can be costly as each model must be trained separately. Inspired by recent advances in the field of Large Language Models (LLMs), we leveraged their natural language understanding capabilities to develop a unified model to solve the PRR problem while being seamlessly adaptable to new scenarios without additional training. To accomplish this, we combined the extensive knowledge LLMs acquired during training with further access to external hand-crafted context information, similar to RAG (Retrieved Augmented Generation) systems, to enhance their ability to generate paths according to user-defined requirements. Extensive experiments on different datasets show a considerable uplift in LLM performance on the PRR problem. © 2025, CC BY.

关键词： Natural language processing systems

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