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Outage, Capacity, and Error Performance of Downlink RSMA-based systems: Analysis and Resource Optimization

IEEE Transactions on Communications 2025年

作者： Vu, Thai-Hoc Da Costa, Daniel Benevides Kim, Sunghwan Pham, Quoc-Viet University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Kyonggi University School of Electronic Engineering Kyonggi Korea Republic of University of Dublin School of Computer Science and Statistics Trinity College Dublin Dublin 2 D02PN40 Ireland

This paper comprehensively investigates the performance of downlink multi-user rate-splitting multiple access (RSMA) networks under Nakagami-m fading channels. We first develop the mathematical outage probability (OP) and ergodic capacity (EC) frameworks, deriving exact expressions for both, along with asymptotic analysis in high signal-to-noise ratio (SNR) and low-rate regions, which serve as the foundation for deducing the approximate and maximal energy-reliability and energy-spectral formulas. To enhance system performance, we tackle the non-convex problems of jointly optimizing common and private power allocation (PA) coefficients to minimize the maximal OP performance. Moreover, we also delve into optimizing PA coefficients and rate-splitting factors concurrently to maximize the ergodic sum capacity (ESC). Addressing the influence of modulation schemes on user error rates, we introduce mathematical frameworks for symbol error rate (SER) considering four combined modulation schemes based on binary phase-shift keying (BPSK) and quadrature-phase shift keying (QPSK), the whole cases are quantified in terms of exact and asymptotic manners. Furthermore, we present a straightforward approach to optimize the PA coefficients to minimize the maximal SER performance. Finally, Monte-Carlo simulations are presented to validate our developed frameworks and optimization solutions. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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Language-Inspired Relation Transfer for Few-shot Class-Incremental Learning

arXiv

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arXiv 2025年

作者： Zhao, Yifan Li, Jia Song, Zeyin Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China School of Electronic and Computer Engineering Peking University Shenzhen518055 China School of Computer Science Peking University Beijing100871 China Pengcheng Laboratory Shenzhen518055 China

Depicting novel classes with language descriptions by observing few-shot samples is inherent in human-learning systems. This lifelong learning capability helps to distinguish new knowledge from old ones through the increase of open-world learning, namely Few-Shot Class-Incremental Learning (FSCIL). Existing works to solve this problem mainly rely on the careful tuning of visual encoders, which shows an evident trade-off between the base knowledge and incremental ones. Motivated by human learning systems, we propose a new Language-inspired Relation Transfer (LRT) paradigm to understand objects by joint visual clues and text depictions, composed of two major steps. We first transfer the pretrained text knowledge to the visual domains by proposing a graph relation transformation module and then fuse the visual and language embedding by a text-vision prototypical fusion module. Second, to mitigate the domain gap caused by visual finetuning, we propose context prompt learning for fast domain alignment and imagined contrastive learning to alleviate the insufficient text data during alignment. With collaborative learning of domain alignments and text-image transfer, our proposed LRT outperforms the state-of-the-art models by over 13% and 7% on the final session of miniImageNet and CIFAR-100 FSCIL benchmarks. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Control of Grid-Connected Inverters Using PLL for Synchronization and Harmonic Mitigation

Control of Grid-Connected Inverters Using PLL for Synchroniz...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Kamran Yousefpour Mohammad Yousefpour Seyyed Javad Hosseini Molla Mansour Taheri Andani Sepideh Alaei Varnousdaderani Hamid Taheri Andani Department of Electrical and Computer Engineering Mississippi State University Azad University Department of Electrical and Computer Engineering Babol Noshirvani University of Technology School of Manufacturing Systems and Networks Arizona State University Department of Computer Engineeering Azad University Department of Electronic Engineering Isfahan University of Technology

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This paper presents the design and simulation of a single-phase grid-connected inverter control system, focusing on enhancing power quality and dynamic performance. The control system comprises a resonant controller for real and reactive power regulation, ensuring sinusoidal current injection into the grid. Additionally, a Phase-Locked Loop (PLL) is incorporated to improve synchronization with the grid voltage, generating accurate reference signals for current control and mitigating harmonic disturbances. The PLL design leverages specific gain parameters, $\boldsymbol{\mu}_{\mathbf{1}},\ \boldsymbol{\mu}_{\mathbf{2}}$ , and $\boldsymbol{\mu}_{\mathbf{3}}$ , optimized for a critically damped response. A comprehensive simulation study evaluates the system's performance under different conditions, including soft-start operation, transient power commands, and grid voltage distortion with low-order harmonics (Sth, 7th, LIth, and 13th). Results highlight the system's robust dynamic response, low total harmonic distortion (THD), and effective harmonic attenuation, demonstrating its suitability for grid-connected distributed energy resource (DER) applications. This work provides a scalable and reliable control framework for next-generation inverter systems operating in modern, complex grid environments.

关键词： Total harmonic distortion System performance Power quality Power system harmonics Harmonic analysis Inverters Synchronization Phase locked loops Voltage control Transient analysis

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Classification of energy graph-based signatures for FDI using machine learning

Classification of energy graph-based signatures for FDI usin...

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Power engineering Conference (SAUPEC), Southern African Universities

作者： Luke Nel Kenneth R. Uren George van Schoor School of Electrical Electronic and Computer Engineering North-West University Potchefstroom South Africa Unit for Energy and Technology Systems North-West University Potchefstroom South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

This paper evaluates energy graph-based visualisation (EGBV) data structures for fault classification using machine learning (ML). EGBV transforms process variable (PV) data into node signature matrices (NSMs) and cost matrices. Tests with k-nearest neighbour (KNN), logistic regression, and feedforward neural networks (FNNs) show that transforming data into the NSM format achieves comparable performance to the PV format, indicating that EGBV offers similar classification characteristics. Future work could apply deep learning to the NSMs and cost matrices to leverage their structural features.

关键词： Deep learning Power engineering Logistic regression Costs Fault detection Energy measurement Transforms Nearest neighbor methods Data structures Feedforward neural networks

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Load-profile based sizing method for distributed energy resources in industrial microgrids

Load-profile based sizing method for distributed energy reso...

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Power engineering Conference (SAUPEC), Southern African Universities

作者： Phillip Jansen George van Schoor Kenneth R. Uren School of Electrical Electronic and Computer Engineering North-West University Potchefstroom South Africa Unit for Energy and Technology Systems North-West University Potchefstroom South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

The demand for microgrids is increasing. Many countries such as South Africa are experiencing difficulties with power reliability, whereas other countries are on a drive to minimise carbon footprint in the power generation industry. In addressing the difficulties mentioned above, optimal sizing of renewable energy resources within a microgrid is essential. Studies show that a variety of sizing approaches have proven viable for analysing and optimising the sizes of distributed energy resources (DERs) within a microgrid. This paper gives an overview of existing sizing approaches from the literature. This overview of existing methods will contextualize the developed method presented in this paper. This study proposes a DER sizing approach called the load-profile based sizing method (LPBSM). The method simplifies factors such as the cost saving, the meteorological area, the peak demands and the average consumption of the site being tested and then calculates the sizes of the DERs using real hourly load data. The LPBSM follows the same fundamentals as that of the intuitive sizing approach from literature but now uses real-time load data as well as the specific electricity tariff of the end user to verify the reliability and availability of power. In this paper, a case study is presented on a site in Cape Town to verify how the LPBSM simulates real results based on a constructed site.

关键词： Renewable energy sources Power engineering Costs Accuracy Tariffs Microgrids Software Real-time systems Distributed power generation Reliability

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Multi-step Sensor Attackability in Cyber-Physical systems

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IEEE Transactions on Automatic Control 2025年

作者： Duo, Wenli Wang, Shouguang Zhou, MengChu The Institute of Systems Engineering Macau University of Science and Technology 999078 China The School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China The Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

This work investigates cyber attacks targeting cyber-physical systems in the framework of discrete event systems. From intruders’ perspective, we propose a concept called k-step attackability to explore attack scenarios where intruders are constrained by a limited number of attack operations. In particular, we focus on a class of sensor attacks where sensor readings can be replaced, deleted, and inserted during transmission. We examine k-step attackability under such attacks and address its verification problem by proposing a structure termed an attack recognizer. It evaluates whether an arbitrary strategy generated by an attack structure can compromise a system within a certain number of attacks. Furthermore, we extend this work to general attack scenarios and provide a method to identify desired attack strategies. © 1963-2012 IEEE.

关键词： Cyber attacks

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Multi-user Visible Light Communications with Probabilistic Constellation Shaping and Precoding

arXiv

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arXiv 2025年

作者： Nguyen, Thang K. Pham, Thanh V. Le, Hoang D. Nguyen, Chuyen T. Pham, Anh T. Department of Computer Science and Engineering The University of Aizu Fukushima Japan Department of Mathematical and Systems Engineering Shizuoka University Shizuoka Japan School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolar M-pulse amplitude modulation (M-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex multivariate non-convex problem due to the non-convexity of the objective function. To tackle the original non-convex optimization problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima. The FA-based approach, however, suffers from high computational complexity. Thus, using zero-forcing (ZF) precoding, we propose a low-complexity design, which is solved using an alternating optimization approach. Additionally, considering the channel uncertainty, a robust design based on the concept of end-to-end learning with autoencoder (AE) is also presented. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. For instance, the joint design achieves 17.5% and 19.2% higher sum-rate for 8-PAM and 16-PAM, respectively, at 60 dB peak amplitude-to-noise ratio. Some insights into the optimal symbol distributions of the two joint design approaches are also provided. Furthermore, our results show the advantage of the proposed robust design over the non-robust one under uncertain channel conditions. © 2025, CC BY.

关键词： Convex optimization

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Correction to: Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes (Neural Computing and Applications, (2025), 10.1007/s00521-025-10991-3)

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Neural Computing and Applications 2025年 1-2页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

In this article, the author’s name Stephen Ojo was incorrectly written as Stepehn Ojo and the affiliation details for author Stephen Ojo was incorrectly given as ‘Department of Electrical and computer engineering, College of engineering Anderson, Anderson University, Anderson, SC 29621, USA’ but should have been ‘Department of Electrical and computer engineering, College of engineering, Anderson University, Anderson, SC 29621, USA’. In addition, the affiliation details for author Gabriel Avelino Sampedro were incorrectly given as ‘College of computer Studies, De La Salle University, 2401 Taft Ave., Malate, 1004 Manila, Philippines and Faculty of Information and Communication Studies, University of the Philippines Open University, 4031 Los Baños, Philippines’ but should have been ‘Department of computer Science, University of the Philippines Diliman, 1101 Quezon City, Philippines’. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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Lag Synchronization and Lag H∞ Synchronization for Multiweighted Coupled Reaction-Diffusion Neural Networks Suffering Topology Attacks

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IEEE Transactions on Control of Network systems 2025年

作者： Wang, Jin-Liang Zhao, Xin-Yu Ren, Shun-Yan Huang, Tingwen Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Tiangong University School of Electronics & Information Engineering Tianjin300387 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper mainly discusses two kinds of coupled reaction-diffusion neural networks (CRNN) under topology attacks, that is, the cases with multistate couplings and with multiple spatial-diffusion couplings. On one hand, by adopting state feedback control scheme, the problem of the lag synchronization for the multistate coupled CRNN is addressed. Moreover, the influences of external disturbances for the lag synchronization of the CRNN with multistate couplings are also taken into consideration. On the other hand, several lag and lag H∞ synchronization conditions for CRNN with multiple spatial-diffusion couplings are derived under the help of the Kronecker product and the state feedback control method. Finally, two numerical examples are provided to verify the correctness of the derived criteria. © 2014 IEEE.

关键词： State feedback

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