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Impact of Elevation Angle on Multi-Beam LEO Satellite communication Systems

IEEE ACCESS 2025年 13卷 71723-71737页

作者： Fastenbauer, Agnes Kaneko, Megumi Svoboda, Philipp Rupp, Markus Tech Univ Wien Inst Telecommun A-1040 Vienna Austria Christian Doppler Lab Digital Twin Assisted AI Sus A-1040 Vienna Austria Natl Inst Informat Inst Telecommun Tokyo 1018430 Japan Univ Tokyo Dept Comp Sci Tokyo 1138654 Japan

Compared to well-established geosynchronous equatorial orbit (GEO) satellite networks, low Earth orbit (LEO) satellites bring new challenges to overcome, such as the distortion of the satellite footprint with varying elevation angle. The impact of the elevation angle on system behavior is not sufficiently studied in literature, and guidelines to parameterize LEO systems are lacking. This paper addresses these gaps by providing a framework to analyze the satellite footprint behavior of arbitrary multi-beam satellite systems with large antenna arrays and analyzing the system behavior of a LEO satellite operating in the K-a-band (30GHz) for varying elevation angle and serving area size. The analysis considers the directivity and antenna array steering of the antenna array and the curvature of the Earth. The provided framework allows repeatable analysis and offers a means to parameterize systems in terms of serving area size, beam design, and operating elevation angles. Analysis over elevation angles confirms the strong influence of the satellite elevation angle on the system performance and indicates that elevation angle dependence of LEO systems needs to be considered in the evaluation of future technologies. It is shown that the system drifts from a noise-limited regime at high elevation angles to an interference-limited regime with decreasing elevation angle. The findings suggest a minimum elevation angle of 30 degrees for practical systems, as lower elevation angles show excessive propagation loss and severe interference due to beam distortion. Link budget analysis further indicates that systems require highly directional antennas with large gain to serve handheld user devices.

关键词： Satellites Antenna arrays Low earth orbit satellites Earth Fading channels Satellite broadcasting Interchannel interference Shape Rician channels Ka-band 6G beamforming LEO low Earth orbit multi-beam satellite non-terrestrial network NTN satellite communication wireless communication

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PSRNet: Few-Shot Automatic Modulation Classification Under Potential Domain Differences

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IEEE TRANSACTIONS ON wireless communicationS 2025年第1期24卷 371-384页

作者： Xing, Hantong Wang, Shuang Wang, Jiacheng Mei, Luyang Xu, Yi Zhou, Huaji Xu, Hua Jiao, Licheng Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Natl Key Lab Electromagnet Space Secur Jiaxing 314033 Peoples R China Air Force Engn Univ Informat & Nav Coll Xian 710077 Peoples R China

Learning from a limited number of samples in automatic modulation classification (AMC) has garnered considerable attention. However, existing few-shot AMC works solely focus on single-domain conditions where the training and testing data share the same data distribution, which overlook the potential domain differences. In practice, the complex and variable communication channels, along with different radio frequency (RF) devices, may result in significant data distribution differences, which can be defined as cross-domain conditions. The neglect of such cross-domain conditions may leads to a significant decline in the performance of existing few-shot AMC models. To consider a more general situation, this paper unifies single-domain and cross-domain few-shot AMC into one task, named SaC-FSL. We propose the Paired Samples Relationship network (PSRNet) as a solution. PSRNet does not require additional network structure design for domain shifts. It distinguishes categories by learning the relationships between sample pairs rather than directly learning the features of samples. To achieve this, we randomly pair the samples to construct different relationships between different classes and domains, and learn these relationships through classification task. Extensive experiments conducted on multiple datasets have demonstrated the superiority of our PSRNet, which can achieve considerable improvements in both single-domain and cross-domain conditions.

关键词： Automatic modulation classification few-shot learning cross-domain feature relationship learning Automatic modulation classification few-shot learning cross-domain feature relationship learning

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Hybrid Implementation for Untrusted-node-based Quantum key Distribution network

arXiv

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arXiv 2025年

作者： Liu, Jingyang Zhou, Xingyu Ding, Huajian Xu, Jiaxin Zhang, Chunhui Li, Jian Wang, Qin Institute of quantum information and technology Nanjing University of Posts and Telecommunications Nanjing210003 China "Broadband Wireless Communication and Sensor Network Technology"Key Lab of Ministry of Education Ministry of Education Nanjing210003 China "Telecommunication and Networks"National Engineering Research Center NUPT Nanjing210003 China

Quantum key distribution (QKD) serves as a cornerstone of secure quantum communication, providing unconditional security grounded in quantum mechanics. While trusted-node networks have facilitated early QKD deployment, their vulnerability to node compromise underscores the need for untrusted-node architectures. Measurement-device-independent QKD (MDI-QKD) and twin-field QKD (TF-QKD) have emerged as leading candidates, addressing security vulnerabilities and extending transmission distances. Despite the wide adoptions in various fiber scaling, no integrated implementation of these two protocols has been demonstrated to date. Here, we present a hybrid system that seamlessly integrates TF-QKD and MDI-QKD into one untrusted-node-based architecture. Utilizing an efficient phase estimation method based on asymmetric interferometers, we convert twin-field global phase tracking to relative phase calibration, allowing near continuous running of both protocols. Experiments demonstrate secure finite-size key rates for sending-or-not-sending QKD and MDI-QKD over fiber distances of 150 to 431 km. The results align with theoretical simulations and show the ability to surpass the absolute repeaterless key capacity. Our work offers an unified framework for deploying multi-protocol QKD networks, laying the foundation for adaptable and scalable quantum infrastructures that can meet a wide range of security and performance needs. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum communication

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An Innovative Linear wireless sensor network Reliability Evaluation Algorithm

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sensorS 2025年第1期25卷 285-285页

作者： Ma, Tao Guo, Huidong Li, Xin Shenyang Univ Technol Coll Informat Sci & Engn Shenyang 110167 Peoples R China Shenyang Key Lab Adv Comp & Applicat Innovat Shenyang 110167 Peoples R China

In recent years, wireless sensor networks (WSNs) have become a crucial technology for infrastructure monitoring. To ensure the reliability of monitoring services, evaluating the network's reliability is particularly important. sensor nodes are distributed linearly when monitoring linear structures, such as railway bridges, forming what is known as a Linear wireless sensor network (LWSN). Although existing evaluation methods, such as enumeration and Binary Decision Diagram (BDD)-based methods, can be used to assess the reliability of various types of networks, their efficiency is relatively low. Therefore, we classified network states based on the number of failed nodes located at the network's ends and analyzed the arrangement characteristics of nodes under different network states. This paper proposed a new reliability assessment method for LWSNs. This method is based on the combinatorial patterns of nodes and uses the concept of integer partitions to calculate the total number of states at different performance levels, applying probability formulas to assess network reliability. Compared to Multi-Valued Decision Diagram (MDD)-based evaluation algorithms, this method is suitable for large-scale LWSNs and offers lower time complexity.

关键词： LWSNs reliability evaluation performance level failure probability number splitting

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A 49.8-mm² IR-UWB Transmitter With Co-Designed Power Amplifier and Antenna for Neural Implants With Extended Transmission Range

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 2025年

作者： Ding, Cong Gao, Mingxiang Skrivervik, Anja K. Shoaran, Mahsa EPFL Inst Elect & Micro Engn & Neuro X Integrated Neurotechnol Lab CH-1202 Geneva Switzerland Ecole Polytech Fed Lausanne Inst Elect & Micro Engn Microwaves & Antennas Grp CH-1015 Lausanne Switzerland Fdn Res Informat Technol Soc ITIS Fdn CH-8004 Zurich Switzerland

This work presents an implantable impulse-radio ultrawideband transmitter (IR-UWB TX) optimized for high-density neural recording. To address the tradeoffs between data rate, transmission range, implant size, and power consumption, we propose a novel co-designed power amplifier (PA) and an antenna interface. This approach eliminates the need for a bulky and lossy matching network (MN), thereby simplifying implementation and enhancing the energy efficiency of the TX. Moreover, unlike other implantable TXs that utilize conventional free-space communication antennas, our TX features an antenna specifically optimized in size and structure for neural implants. A compact and power-efficient ring oscillator (RO)-based wide-band phase-locked loop (PLL) is proposed to generate an RF carrier within the 3-6-GHz UWB band. Additionally, we introduce a 4-bit asymmetric pulse shaping technique that reduces intersymbol interference (ISI), enhancing data transmission rates. The proposed TX was fabricated in a 65-nm TSMC process. The wireless module, including a custom-designed printed antenna, has a compact form factor of only 49.8 mm(2). The efficiency of the wireless link was demonstrated in vitro at implantation depths of 3 and 15 mm, achieving a record-long transmission range of over 1 m and a distance-normalized energy efficiency below 27 pJ/b/m at data rates up to 500 Mb/s.

关键词： Antennas Neural implants Radio frequency Power demand Phase locked loops Energy efficiency wireless communication wireless sensor networks Transmitting antennas Recording Asymmetrical pulse shaping fully integrated high data rate impulse-radio ultrawideband transmitter (IR-UWB TX) meter-level transmission range mm-scale size neural implants power amplifier (PA) and antenna co-design

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基于双注意力图神经网络的链路预测

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南京邮电大学学报(自然科学版) 2025年第1期45卷 106-114页

作者：杨真真林泽龙杨永鹏南京邮电大学宽带无线通信与传感网技术教育部重点实验室江苏南京210003

链路预测是在图结构中预测未知或潜在的边,对挖掘图中的隐含信息、补全图中的缺失数据和发现图中的新知识都具有重要意义。图神经网络(Graph Neural network,GNN)已被广泛应用于链路预测,然而,现有基于GNN的链路预测方法存在一些问题:(1... 详细信息

链路预测是在图结构中预测未知或潜在的边,对挖掘图中的隐含信息、补全图中的缺失数据和发现图中的新知识都具有重要意义。图神经网络(Graph Neural network,GNN)已被广泛应用于链路预测,然而,现有基于GNN的链路预测方法存在一些问题:(1)大多数基于GNN的方法往往容易忽略为链路预测提供额外帮助的边信息的重要性;(2)大多数基于GNN的方法都仅捕获表示图的邻居节点间相似性的低频信息,忽略了表示邻居节点间差异性的高频信息;(3)大多数基于GNN的方法都未考虑输入特征矩阵的节点维度和特征维度两个维度,只关注其中一个维度。针对这些问题,提出了一种基于双注意力图神经网络(Dual Attention Graph Neural network,DAGNN)的链路预测方法,该方法包含两条路径,以不同的角度更新节点表示。其中一条是基于图神经网络的路径,采用含边信息的频率自适应图注意力网络(Frequency Adaptive Graph Attention network with Edge Information,FAGAT⁃EI)作为基础模型,有效地利用边信息增强节点之间的关系,并利用频率自适应机制平衡高低频率邻居信息的权重,从而缓解GNN的过度平滑问题;另一条是基于通道注意力网络的路径,提出了一种新的压缩-激励通道注意力模块(Squeeze and Excitation⁃Channel At⁃tention Module,SE⁃CAM)作为基础模型,充分考虑输入特征矩阵的节点维度和特征维度,并自动学习和调整每个节点的不同特征权重,从而得到更有意义的节点表示。最后在两个基准数据集上进行了实验,实验结果表明,提出的链路预测方法在Last⁃FM和Book⁃Crossing两个数据集上的AUC和ACC指标均优于其他基线模型,展现出了卓越的链路预测性能。

关键词：链路预测图神经网络注意力机制压缩-激励模块频率自适应

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A 6.78MHz wireless Power Transfer System With 3-Mode Global Power Control

A 6.78MHz Wireless Power Transfer System With 3-Mode Global ...

引用

2025 International Conference on Electronics, Information, and communication, ICEIC 2025

作者： Hong, Sungsik Park, Changwoo Burm, Jinwook Sogang University Department of Electronic Engineering Seoul Korea Republic of

ISBN: (纸本)9798331510756

This paper proposes wireless power transfer system(WPT) with 3-mode global power control loop to maintain high efficiency in the high-power range of watt-level systems. To enhance receiver efficiency, a single-stage 3- Mode regulating rectifier was employed. For the transmitter, Tx 3- Mode global power control was implemented using load-shift keying (LSK) signals to improve light-load and overall efficiency. The proposed Class-D Power Amplifier operates in 3 modes: 1X, 1×10.25X, and 0.25X. To reduce PCB area, the Tx sensing coil was replaced with an on-chip current sensor. A negative feedback loop was added to the stacked ramp generator in the receiver to compensate for delay caused by PVT (Process, Voltage, Temperature) variations. The proposed WPT system was designed using a O.13tLm BCD process and regulates a 5V output voltage. The RX peak efficiency is 95.5%, and the Rx and end-to-end (E2E) efficiency under light-load conditions improved by 11 % and 21.66%, respectively. © 2025 IEEE.

关键词： Inductive power transmission

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A Conditional GAN and Dual-Channel Hybrid Deep Feature Framework for Robust sensor Fault Detection in WSNs

A Conditional GAN and Dual-Channel Hybrid Deep Feature Frame...

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2025 International Conference on Electronics, Information, and communication, ICEIC 2025

作者： Khan, Rehan Saeed, Umer Koo, Insoo University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of School of Computer Science and Technology University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9798331510756

sensor-generated data is vital to the operation of numerous systems and services in the rapidly growing field of the Internet of Things. wireless sensor networks, as an essential setup for these systems, are frequently deployed in large, diverse, and often harsh environments. However, these networks are highly vulnerable to various faults, potentially leading to improper data transmission, reliability, and financial stability of the systems. To address these challenges, we propose a hybrid model for sensor fault detection that integrates a machine learning classifier with the deep learning (DL) model, specifically VGG-16 and ResNet-50. Synthetic samples are generated using a Conditional Generative Adversarial network and common sensor faults, such as hardover, drift, spike, erratic, and stuck fault are introduced by leveraging a publicly available temperature sensor dataset. Time-series data is transformed into Gramian Angular Field images, from which deep features are extracted using VGG-16 and ResNet-50. These extracted features are then fused to form a hybrid feature pool. Our framework effectively addresses problems related to data imbalance and enhances accuracy. The proposed model outperforms the individual feature sets, VGG-16 (89.22%) and ResNet-50 (84.21%), achieving notable accuracy of 92.55% with the fused feature set, underscoring its potential for robust sensor fault detection. © 2025 IEEE.

关键词： Gallium nitride

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Cross-Modal Haptic Generation for Emergency Rescue in Internet of Robotic Things

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IEEE Internet of Things Journal 2025年

作者： Liu, Hengfa Wei, Xin Gao, Yun Zhou, Liang Qian, Yi Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China University of Nebraska-Lincoln Department of Electrical and Computer Engineering OmahaNE68588 United States

Robots equipped with multi-modal sensing capabilities play an important role in the Internet of Robotic Things (IoRT), especially in emergency rescue. However, existing rescue robots pose challenges for human operators in achieving precise manipulation due to the absence of haptic signals. Additionally, limited and fluctuating bandwidth in emergency rescue renders current cross-modal haptic generation schemes ineffective. To overcome this dilemma, we propose a novel cross-modal haptic generation scheme that enhances scalability across diverse network conditions by leveraging correlations among audio-visual-haptic modalities. Specifically, we first propose an edge-device collaboration-based architecture that dynamically extracts semantics from audio-visual signals, tailored to the current network conditions, for haptic generation. This is achieved by predicting the network state at the edge and performing multi-modal encoding and fusion at the device. Next, we design a scalable cross-modal haptic generation scheme that implements the optimal generation strategy based on received audio-visual semantics of different granularities, ensuring real-time acquisition of coarse-grained or fine-grained haptic feedback. Finally, numerical experimental results conducted on a multi-modal dataset and a simulated emergency environment indicate that the proposed scheme reliably generates haptic signals in emergency rescue. © 2025 IEEE.

关键词： Robots

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Generative AI for Secure Physical Layer communications: A Survey

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IEEE TRANSACTIONS ON COGNITIVE communicationS AND networkING 2025年第1期11卷 3-26页

作者： Zhao, Changyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin Letaief, Khaled B. Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore CNRS CREATE Singapore 138602 Singapore Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Singapore Univ Technol & Design Pillar Informat Syst Technol & Design Singapore Singapore Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. Beyond content creation, GAI has significant analytical abilities to learn complex data distribution, offering numerous opportunities to resolve security issues. In the realm of security from physical layer perspectives, traditional AI approaches frequently struggle, primarily due to their limited capacity to dynamically adjust to the evolving physical attributes of transmission channels and the complexity of contemporary cyber threats. This adaptability and analytical depth are precisely where GAI excels. Therefore, in this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We first emphasize the importance of advanced GAI models in this area, including Generative Adversarial networks (GANs), Autoencoders (AEs), Variational Autoencoders (VAEs), and Diffusion Models (DMs). We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity. Furthermore, we also present future research directions focusing model improvements, multi-scenario deployment, resource-efficient optimization, and secure semantic communication, highlighting the multifaceted potential of GAI to address emerging challenges in secure physical layer communications and sensing.

关键词： Security Artificial intelligence Physical layer security Surveys Authentication communication networks Resilience Generative AI physical layer communications physical layer security wireless sensor network anomaly detection

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