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6th Australian Microwave Symposium, AMS 2025

作者： Lv, Xiaojing Luo, Zhen Tyo, J. Scott Yang, Yang University of Technology School of Electrical and Data Engineering Sydney Australia University of Technology School of Mechanical and Mechatronic Engineering Sydney Australia Monash University Department of Electrical and Computer Systems Engineering Australia

ISBN: (纸本)9798350379969

This paper highlights the benefits of advanced multi-material 3D printing technology in enhancing the electromagnetic performance of metasurfaces. By systematically evolving geometric structures, the proposed bandpass filtering meta-Atom outperforms existing designs in key metrics, including bandwidth, out-of-band suppression, in-band insertion loss, and transition band steepness, underscoring the significant potential of 3D unit cells. Through the integration of qualitative approach and semi-Analytical design algorithm, the geometric topology is fully optimized without imposing substantial computational demands. © 2025 IEEE.

关键词： Wiener filtering

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A Microwave Twin-Port Probe for Non-Invasive Skin Cancer Detection 6

A Microwave Twin-Port Probe for Non-Invasive Skin Cancer Det...

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6th Australian Microwave Symposium, AMS 2025

作者： Chamani, Shaghayegh Lv, Xiaojing Esfandiari, Meisam Bird, Trevor S Yang, Yang School of Electrical and Data Engineering UltimoNSW2007 Australia Global Big Data Technology Centre UltimoNSW2007 Australia

ISBN: (纸本)9798350379969

This paper presents a novel microwave twin-port coaxial probe integrated with a coplanar waveguide (CPW) transmission line for non-invasive skin cancer detection. Operating at approximately 14 GHz, the probe utilizes both reflection and transmission-based analyses. The design was evaluated through full-wave simulations, and the results demonstrate the probe's capability to differentiate the dielectric properties of various materials. © 2025 IEEE.

关键词： Coplanar waveguides

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(Information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

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Energy engineering 2025年第1期122卷 243-264页

作者： Liyao Yang Hongyan Ma Yingda Zhang Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Institute of Distributed Energy Storage Safety Big Data Beijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China

Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management ***,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is *** address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and ***,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance ***,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution *** Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction ***,the proposed models are validated using NASA and CALCE lithium-ion battery *** results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.

关键词： State of health remaining useful life variational modal decomposition random forest twin support vector machine convolutional optimization algorithm

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Privacy-Preserving Cyberattack Detection in Blockchain-Based IoT Systems Using AI and Homomorphic Encryption

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IEEE Internet of Things Journal 2025年第11期12卷 16478-16492页

作者： Manh, Bui Duc Nguyen, Chi-Hieu Hoang, Dinh Thai Nguyen, Diep N. Zeng, Ming Pham, Quoc-Viet University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia Université Laval Department of Electrical Engineering and Computer Engineering QuebecQCG1V 0A6 Canada University of Dublin School of Computer Science and Statistics Trinity College Dublin D02 PN40 Ireland

This work proposes a novel privacy-preserving cyberattack detection framework for blockchain-based Internet-of-Things (IoT) systems. In our approach, artificial intelligence (AI)-driven detection modules are strategically deployed at blockchain nodes to identify real-time attacks, ensuring high accuracy and minimal delay. To achieve this efficiency, the model training is conducted by a cloud service provider (CSP). Accordingly, blockchain nodes send their data to the CSP for training, but to safeguard privacy, the data is encrypted using homomorphic encryption (HE) before transmission. This encryption method allows the CSP to perform computations directly on encrypted data without the need for decryption, preserving data privacy throughout the learning process. To handle the substantial volume of encrypted data, we introduce an innovative packing algorithm in a Single-Instruction-Multiple-data (SIMD) manner, enabling efficient training on HE-encrypted data. Building on this, we develop a novel deep neural network training algorithm optimized for encrypted data. We further propose a privacy-preserving distributed learning approach based on the FedAvg algorithm, which parallelizes the training across multiple workers, significantly improving computation time. Upon completion, the CSP distributes the trained model to the blockchain nodes, enabling them to perform real-time, privacy-preserved detection. Our simulation results demonstrate that our proposed method can not only mitigate the training time but also achieve detection accuracy that is approximately identical to the approach without encryption, with a gap of around 0.01%. Additionally, our real implementations on various blockchain consensus algorithms and hardware configurations show that our proposed framework can also be effectively adapted to real-world systems. © 2025 IEEE.

关键词： Differential privacy

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A survey on cross-user federated recommendation

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Science China(Information Sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Joint Near Field Uplink Communication and Localization Using Message Passing-Based Sparse Bayesian Learning

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7666-7675页

作者： Liu, Fei Yuan, Zhengdao Guo, Qinghua Zhang, Yuanyuan Wang, Zhongyong Zhang, J. Andrew Zhengzhou University School of Geoscience and Techonology The School of Electrical and Information Engineering Zhengzhou450000 China Open University of Henan Artificial Intelligence Technology Engineering Research Center Zhengzhou450000 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University of Light Industry School of Electronics and Information Henan Zhengzhou450002 China University of Technology Sydney Global Big Data Technologies Centre SydneyNSW2007 Australia

This work deals with the problem of uplink communication and localization in an integrated sensing and communication system, where users are in the near field (NF) of antenna aperture due to the use of high carrier frequency and large antenna arrays at base stations. We formulate joint NF signal detection and localization as a problem of recovering signals with a sparse pattern, and we adopt differentiation modulations to avoid the frequent use of pilot signals. To solve the problem, we develop a message passing based sparse Bayesian learning (SBL) algorithm, where multiple unitary approximate message passing (UAMP)-based sparse signal estimators work jointly to recover the sparse signals with low complexity. Simulation results demonstrate the effectiveness of the proposed method. © 1967-2012 IEEE.

关键词： Carrier communication

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Securing MIMO Wiretap Channel With Learning Based Friendly Jamming under Imperfect CSI

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IEEE Internet of Things Journal 2025年第11期12卷 16009-16022页

作者： Tuan, Bui Minh Nguyen, Diep N. Trung, Nguyen Linh Nguyen, Van-Dinh Huynh, Nguyen Van Hoang, Dinh Thai Krunz, Marwan Dutkiewicz, Eryk University of Technology School of Electrical and Data Engineering SydneyNSW2007 Australia University of Engineering and Technology Vietnam National University Faculty of Electronics and Telecommunications Hanoi Viet Nam VinUniversity College of Engineering and Computer Science Vinhomes Ocean Park Hanoi100000 Viet Nam University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom The University of Arizona Department of Electrical and Computer Engineering TucsonAZ United States

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g. for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g. cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely AEFJ, to jointly maximize the secrecy rate and minimize the block error rate at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the block error rate, offering a good solution for enhancing communication security in wireless communication systems. © 2025 IEEE.

关键词： Jamming

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Miniaturized 3-D Printable Composite Metasurface for 1-D Beam Steering 6

Miniaturized 3-D Printable Composite Metasurface for 1-D Bea...

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6th Australian Microwave Symposium, AMS 2025

作者： Ali, Md Yeakub Singh, Khushboo Ahmed, Foez Mukhopadhyay, Subhas Macquarie University School of Engineering SydneyNSW2109 Australia School of Electrical and Data Engineering SydneyNSW2007 Australia University of Rajshahi Dept. of Information and Communication Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350379969

This paper presents a 3-D-printable metal-dielectric unit cell-based composite metasurface with a lower profile for 1-D beam steering of aperture-Type antennas. The proposed metasurface also incorporates near-field phase correction to enhance far-field radiation performance. Two types of highly transmitting unit cells are introduced to achieve a wide range of transmission phase delays. The efficacy of the proposed metasurface is analyzed numerically in conjunction with a resonant cavity antenna. The system can steer the beam 360° in the azimuth plane (φ), with a predefined beam tilting in the elevation plane (θ). Moreover, the directivity deviation is minimal, at 1.53 dB, across the beam steering range, coupled with excellent impedance matching. © 2025 IEEE.

关键词： 3-D printing beam steering metasurfaces near-field phase transformation Resonant cavity antennas

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Antenna Array with Memory Metal for CubeSats Applications 6

Antenna Array with Memory Metal for CubeSats Applications

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6th Australian Microwave Symposium, AMS 2025

作者： Liang, Zhenxi Sun, Zhichao Lv, Xiaojing Thalakotuna, Dushmantha N. Esselle, Karu P. Li, Xiaopeng Yang, Yang School of Electrical and Data University of Technology Sydney NSW2007 Australia University of New South Wales School of Mechanical and Manufacturing Engineering NSW2052 Australia

ISBN: (纸本)9798350379969

In this paper, we proposed a 4×12 expandable antenna array with memory metal for CubeSats applications. First, an X-band 4×4 circularly polarised (CP) microstrip subarray with excellent radiation performance is developed. By deploying memory metal at the rotating joints of CubeSat, the antenna array can work in two different states. Compared with the folded antenna array, the realised gain of the expanded array can be increased by 4.7 dB. The proposed scheme provides a potential application approach for CubeSat communications. © 2025 IEEE.

关键词： Antenna arrays

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