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Intelligent integrated sensing and communication:a survey

Science China(Information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

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Journal of Energy Chemistry 2025年第4期103卷 759-772页

作者： Lei Cai Jing Yan Haiyan Jin Jinhao Meng Jichang Peng Bin Wang Wei Liang Remus Teodorescu School of Computer Science and Engineering Xi'an University of Technology School of Electrical Engineering Xi'an Jiaotong University Smart Grid Research Institute Nanjing Institute of Technology Department of Energy Aalborg University

To predict the lithium-ion(Li-ion) battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles' degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM) network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.

关键词： Autoencoder Battery degradation trajectory Early prediction Synthetic data generation

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Architecture Decisions for Supporting Web-based Visualization of Distribution System Operating Envelopes

Architecture Decisions for Supporting Web-based Visualizatio...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Fernandez, Jorge Grijalva, Santiago School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798331541125

Operating envelopes are dynamic limits on power injections, computed in advance based on the operational con-ditions of the network, such as load profiles. These dynamic limits can unlock the full potential of distributed energy resources (DERs) by maximizing the use of existing infrastructure while ensuring a reliable power supply. Visualizing operating envelopes presents a significant challenge, it requires effectively conveying spatio-temporal information for multiple connection sites simultaneously. This paper discusses, demonstrates, and evaluates a set of software architecture decisions to support web-based visualization of distribution system operating envelopes. We propose a microservice-oriented architecture with two processing modules: one for time series power flow and another for three-phase unbalanced multi-period optimal power flow (also known as energy scheduling) computations. Both modules are con-tainerized, ensuring strong separation of duties and portability. Additionally, a web-based visualization interface is developed to incorporate advanced interactive visualization techniques to support the analysis of complex spatio-temporal relationships and 'what if' scenarios. The performance of the approach is evaluated by measuring processing time and size from responses considering various distribution feeders, including the 3- Bus, IEEE 4-Bus, IEEE 13-Bus, and IEEE 123-Bus test feeders. © 2025 IEEE.

关键词： Electric load flow

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Fully Digital Ultra-low-bit Linear Embedding Beamforming System with Quantization Compensation

Fully Digital Ultra-low-bit Linear Embedding Beamforming Sys...

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2025 IEEE Radio and Wireless Symposium, RWS 2025

作者： Mao, Xiangyu Delude, Coleman Wang, Wei-Chun Romberg, Justin Mukhopadhyay, Saibal Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9798350377743

We propose the first digital ultra-low-bit precision implementation of a Linear Embedded Beamforming (LE-BF) system, which transforms high-dimensional input signals into low-dimensional outputs. The use of quantization compensation in this operation ensures beamforming performance is preserved. The system then processes the low-dimensional outputs to generate the final output with lower power consumption. The paper presents two different implementations of LE-BF systems: 1) An FPGA-based 2-bit LE-BF system configured with 64 inputs and 1 output, achieving a 12.4× decrease in design component utilization and a 11.2× decrease in power consumption compared to corresponding traditional True-Time-Delay digital beamforming (TTD-DBF) designs. 2) An ASIC-based (TSMC 28nm) 2-bit LE-BF configured with 256 inputs and 1 output, exhibiting a 18.7× greater efficiency in both design area and power consumption compared to corresponding TTDDBF designs. The ASIC-based system is capable of supporting a 1 GHz instantaneous bandwidth. © 2025 IEEE.

关键词： Beamforming

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Towards Harsh Environment Silicon Carbide Based On-chip Antenna

Towards Harsh Environment Silicon Carbide Based On-chip Ante...

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2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Dasari, Sree Adinarayana Lee, Seung Yoon Ghalichechian, Nima School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9781946815200

This paper presents an on-chip dielectric resonator antenna (DRA) based on silicon carbide (SiC). SiC is a semiconductor material known for its large bandgap, high dielectric breakdown, and temperature-stable dielectric properties, making it well-suited for harsh environment electronics. We propose a DRA on a SiC substrate, incorporating a sapphire rectangular resonator bonded to the SiC chip. The fundamental TE111 mode is excited at 29.5 GHz within the resonator via a rectangular aperture on the SiC chip, which is fed by a coplanar waveguide (CPW). An inductive series stub at the feed is used for impedance matching. Simulation results demonstrate a high gain of 7.4 dBi with a radiation efficiency of 84%. Furthermore, we propose a custom probe station with an integrated hot plate and robotic arm for high-temperature testing of the DRA. © 2025 USNC-URSI.

关键词： Sapphire

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A Novel Contingency Analysis-Driven Deep Learning Framework for Evaluation of a Primary Power System Security Metric

A Novel Contingency Analysis-Driven Deep Learning Framework ...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： King, Adam Grijalva, Santiago School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798331541125

Continuous operation of power systems is critical to modern society, even during unexpected or emergency situations. One method to evaluate the security of power systems is contingency analysis, a computationally-intensive process which considers how a set of possible scenarios would affect the electrical grid. This paper presents a novel security evaluation method which enables a topology- and parameter-free estimation of a system-level security metric, the system aggregate megawatt contingency overload (SysAMWCO), by leveraging a novel deep learning-based approach. Using the results of conventional contingency analysis for a simulated power system, a deep neural network (DNN) is trained to predict the SysAMWCO value using only bus loads as input. Experimentation across different neural network parameters reveals a complex relationship between these parameters and the accuracy of the predicted SysAMWCO value. While each DNN is trained on data generated using conventional contingency analysis methods, fully-trained models achieve R-squared scores in excess of 0.98 without needing electrical grid topology or power device parameter information as input. Given these results, this framework shows promise for potential real-world applications. © 2025 IEEE.

关键词： Power system analysis

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Tightening QC Relaxations of AC Optimal Power Flow Through Improved Linear Convex Envelopes

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IEEE Transactions on Power Systems 2025年第2期40卷 1465-1480页

作者： Narimani, Mohammad Rasoul Molzahn, Daniel K. Davis, Katherine R. Crow, Mariesa L. Department of Electrical and Computer Engineering NorthridgeCA91330 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Texas A&M University Electrical and Computer Engineering Department College StationTX77840 United States Missouri University of Science and Technology Electrical and Computer Engineering Department RollaMO65409 United States

AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for global optima, recent research has developed various convex relaxations that bound the optimal objective values of AC OPF problems. The QC relaxation convexifies the AC OPF problem by enclosing the non-convex terms within convex envelopes. The QC relaxation's accuracy strongly depends on the tightness of these envelopes. This paper proposes two improvements for tightening QC relaxations of OPF problems. We first consider a particular nonlinear function whose projections are the nonlinear expressions appearing in the polar representation of the power flow equations. We construct a polytope-shaped convex envelope around this nonlinear function and derive convex expressions for the nonlinear terms using its projections. Second, we use sine and cosine expression properties, along with changes in their curvature, to tighten this convex envelope. We also propose a coordinate transformation to tighten the envelope by rotating power flow equations based on individual bus-specific angles. We compare these enhancements to a state-of-the-art QC relaxation method using PGLib-OPF test cases, revealing improved optimality gaps in 68% of the cases. © 1969-2012 IEEE.

关键词： Load flow optimization

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Transforming the electrical grid: the role of AI in advancing smart, sustainable, and secure energy systems

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Energy Informatics 2025年第1期8卷 1-43页

作者： Rajaperumal, T.A. Columbus, C. Christopher School of Electrical Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

The evolution of the electrical grid from its early centralized structure to today’s advanced "smart grid" reflects significant technological progress. Early grids, designed for simple power delivery from large plants to consumers, faced challenges in efficiency, reliability, and scalability. Over time, the grid has transformed into a decentralized network driven by innovative technologies, particularly artificial intelligence (AI). AI has become instrumental in enhancing efficiency, security, and resilience by enabling real-time data analysis, predictive maintenance, demand-response optimization, and automated fault detection, thereby improving overall operational efficiency. This paper examines the evolution of the electrical grid, tracing its transition from early limitations to the methodologies adopted in present smart grids for addressing those challenges. Current smart grids leverage AI to optimize energy management, predict faults, and seamlessly integrate electric vehicles (EVs), reducing transmission losses and improving performance. However, these advancements are not without limitations. Present grids remain vulnerable to cyberattacks, necessitating the adoption of more robust methodologies and advanced technologies for future grids. Looking forward, emerging technologies such as Digital Twin (DT) models, the Internet of Energy (IoE), and decentralized grid management are set to redefine grid architectures. These advanced technologies enable real-time simulations, adaptive control, and enhanced human–machine collaboration, supporting dynamic energy distribution and proactive risk management. Integrating AI with advanced energy storage, renewable resources, and adaptive access control mechanisms will ensure future grids are resilient, sustainable, and responsive to growing energy demands. This study emphasizes AI’s transformative role in addressing the challenges of the early grid, enhancing the capabilities of the present smart grid, and shaping a secure

关键词： Risk management

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Accurate Bias Stress Instability Measurements in High-Performance ITO FETs Using Modified On-the-Fly Technique

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 864-867页

作者： Rahman, Md Sazzadur Sarkar, Arijit Silva, Dinuk R. De Harrison, Austin T. Kuo, Yu-Hsin Chen, Jiazheng Khan, Asif Islam Roy, Tania Duke University Department of Electrical and Computer Engineering DurhamNC27708 United States Georgia Institute of Technology Department of Electrical and Computer Engineering AtlantaGA30332 United States

We report on Indium Tin Oxide (ITO) dual-gated field-effect transistors (DG-FETs) achieving a high ION of 1.2 mA/μ m at a gate overdrive, VOV of 2.5 V, with a top-gate effective oxide thickness of 0.85 nm. When stressed with VOV = 3.5 V, the devices exhibit ultralow threshold voltage shift (Δ Vth) of just 19 mV, as measured by the conventional measure-stress-measure (MSM) technique commonly used in the amorphous oxide semiconductor (AOS) community. However, employing a modified on-the-fly (OTF) method for reliability testing reveals a 500% increase in Δ Vth for the same ITO device, due to the Vth recovery occurring within milliseconds after stress is removed. This substantial difference, also observed in multiple devices, highlights the possible underreporting of threshold shifts in the MSM method due to fast Vth recovery. Thus, our results underscore the importance of recovery-analysis for reliability study of AOS devices and choosing OTF method for devices with fast recovery. Our study establishes a robust framework for measuring and understanding the root causes of Vth instabilities in ITO transistors. © 1980-2012 IEEE.

关键词： Tin oxides

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Improving Machine Translation Formality with Large Language Models

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computers, Materials & Continua 2025年第2期82卷 2061-2075页

作者： Murun Yang Fuxue Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China College of Electrical Engineering Yingkou Institute of TechnologyYingkou115014China

Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a formal translation style. The second approach employs an LLM as a reward model for scoring translation formality, and then uses reinforcement learning algorithms to fine-tune the NMT model to maximize the reward score, thereby enhancing the formality of the generated translations. Considering the substantial parameter size of LLMs, we also explore methods to reduce the computational cost of INMTF. Experimental results demonstrate that INMTF significantly outperforms baselines in terms of translation formality and translation quality, with an improvement of +9.19 style accuracy points in the German-to-English task and +2.16 COMET score in the Russian-to-English task. Furthermore, our work demonstrates the potential of integrating LLMs within NMT frameworks to bridge the gap between NMT outputs and the formality required in various real-world translation scenarios.

关键词： Neural machine translation formality large language model text style transfer style evaluation reinforcement learning

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