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UAV-Enabled wireless networks With Movable-Antenna Array: Flexible Beamforming and Trajectory Design

IEEE wireless communicationS LETTERS 2025年第3期14卷 566-570页

作者： Liu, Wenchao Zhang, Xuhui Xing, Huijun Ren, Jinke Shen, Yanyan Cui, Shuguang Chinese Acad Sci Shenzhen Inst Adv Technol Guangzhou 518055 Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Future Network Intelligence Inst Sch Sci & Engn Shenzhen 518066 Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Guangdong Prov Key Lab Future Networks Intelligenc Shenzhen 518066 Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Future Network Intelligence Inst Sch Sci & Engn Shenzhen 518066 Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Guangdong Prov Key Lab Future Networks Intelligenc Shenzhen 518066 Guangdong Peoples R China

Recently, movable-antenna (MA) array becomes a promising technology for improving the communication quality in wireless communication systems. In this letter, an unmanned aerial vehicle (UAV) enabled multi-user multi-input-single-output system enhanced by the MA array is investigated. To enhance the throughput capacity, we aim to maximize the achievable data rate by jointly optimizing the transmit beamforming, the UAV trajectory, and the positions of the MA array antennas. The formulated data rate maximization problem is a highly coupled non-convex problem, for which an alternating optimization based algorithm is proposed to get a sub-optimal solution. Numerical results have demonstrated the performance gain of the proposed method compared with conventional method with fixed-position antenna array.

关键词： Autonomous aerial vehicles Antenna arrays Array signal processing Trajectory Optimization Transmitting antennas Internet of Things Movable-antenna array unmanned aerial vehicles transmit beamforming trajectory design

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ISAC Prototype System for Multi-Domain Cooperative communication networks

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IEEE wireless communicationS LETTERS 2025年第1期14卷 108-112页

作者： Yang, Jie Que, Hang Du, Tao Liang, Le Li, Xiao Wen, Chao-Kai Jin, Shi Southeast Univ Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Nanjing 210096 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 80424 Taiwan

Future wireless networks are poised to transform into integrated sensing and communication (ISAC) networks, unlocking groundbreaking services such as digital twinning. To harness the full potential of ISAC networks, it is essential to experimentally validate their sensing capabilities and the role of sensing in boosting communication. However, current prototype systems fall short in supporting multiple sensing functions or validating sensing-assisted communication. In response, we have developed an advanced ISAC prototype system that incorporates monostatic, bistatic, and network sensing modes. This system supports multimodal data collection and synchronization, ensuring comprehensive experimental validation. On the communication front, it excels in sensing-aided beam tracking and real-time high-definition video transmission. For sensing applications, it provides precise angle and range measurements, real-time angle-range imaging, and radio-based simultaneous localization and mapping (SLAM). Our prototype aligns with the 5G New Radio standard, offering scalability for up to 16 user equipments (UEs) in uplink transmission and 10 UEs in downlink transmission. Real-world tests showcase the system's superior accuracy, with root mean square errors of 2.3 degrees for angle estimation and 0.3 meters (m) for range estimation. Additionally, the estimation errors for multimodal-aided real-time radio SLAM localization and mapping are 0.25 m and 0.8 m, respectively.

关键词： sensors Prototypes Real-time systems OFDM Synchronization Integrated sensing and communication Simultaneous localization and mapping Millimeter wave communication Phased arrays Location awareness Monostatic sensing bistatic sensing network sensing sensing-aided beam tracking imaging radio SLAM

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CRS-based Joint CFO and Channel Estimation using Deep Learning in OFDM-based Vehicular communication Systems

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IEEE Transactions on wireless communications 2025年第5期24卷 3882-3893页

作者： Wu, Hong Chen, Zhuo Liu, Zhiang Geng, Xue Zhao, Yingxin Liu, Zhiyang Nankai University College of Electronic Information and Optical Engineering Tianjin300350 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Tianjin300350 China Nankai University Engineering Research Center of Thin Film Optoelectronics Technology Ministry of Education Tianjin300350 China Nankai University Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China

Vehicular communication in high mobility environments has been widely explored in recent years. However, due to the existence of carrier frequency offset (CFO) and dynamic channel, the performance of vehicular communication systems over fast time-varying scenes drops severely. To address this problem, in this paper, we propose a simple and effective CRS-based joint CFO and channel estimation baseline method using deep learning (DL) for orthogonal frequency division multiplexing (OFDM) systems. Concretely, we construct a joint neural network (NN) architecture consisting of a CFO estimation network (CFOENet) based on fully connected layers and a channel estimation network (CENet) composed of convolutional layers. The proposed NN architecture can fully exploit the correlation of the cell reference signal (CRS), while learning the CFO characteristics and channel state information (CSI) changes simultaneously, which highly improves the pilot usage efficiency. We conduct adequate simulation experiments, and the results demonstrate that the proposed DL-based scheme can achieve better performance in terms of CFO estimation, channel estimation and overall system performance than conventional methods, while our method has stronger robustness and generalization ability under various channel conditions. The proposed joint CFO and channel estimation scheme has great potential in the field of the Internet of Vehicles. © 2002-2012 IEEE.

关键词： Frequency allocation

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Distortion Resilience for Goal-Oriented Semantic communication

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第5期24卷 3489-3501页

作者： Nguyen, Minh-Duong Do, Quang Vinh Yang, Zhaohui Pham, Quoc-Viet Hwang, Won-Joo Pusan Natl Univ Dept Informat Convergence Engn Busan 46241 South Korea Ton Duc Thang Univ Fac Elect & Elect Engn Wireless Commun Res Grp Ho Chi Minh City 700000 Vietnam Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China Univ Dublin Trinity Coll Dublin Sch Comp Sci & Stat Dublin D02PN40 Ireland

Recent research efforts on Semantic communication (SemCom) have mostly considered accuracy as a main problem for optimizing goal-oriented communication systems. However, these approaches introduce a paradox: the accuracy of Artificial Intelligence (AI) tasks should naturally emerge through training rather than being dictated by network constraints. Acknowledging this dilemma, this work introduces an innovative approach that leverages the rate distortion theory to analyze distortions induced by communication and compression, thereby analyzing the learning process. Specifically, we examine the distribution shift between the original data and the distorted data, thus assessing its impact on the AI model's performance. Founding upon this analysis, we can preemptively estimate the empirical accuracy of AI tasks, making the goal-oriented SemCom problem feasible. To achieve this objective, we present the theoretical foundation of our approach, accompanied by simulations and experiments that demonstrate its effectiveness. The experimental results indicate that our proposed method enables accurate AI task performance while adhering to network constraints, establishing it as a valuable contribution to the field of signal processing. Furthermore, this work advances research in goal-oriented SemCom and highlights the significance of data-driven approaches in optimizing the performance of intelligent systems.

关键词： Semantics Artificial intelligence Distortion Measurement Optimization Accuracy wireless communication communication efficiency data compression goal-oriented semantic communication resource allocation

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A survey of Machine Learning-based Physical-Layer Authentication in wireless communications

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JOURNAL OF network AND COMPUTER APPLICATIONS 2025年 235卷

作者： Meng, Rui Xu, Bingxuan Xu, Xiaodong Sun, Mengying Wang, Bizhu Han, Shujun Lv, Suyu Zhang, Ping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518066 Guangdong Peoples R China Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China

To ensure secure and reliable communication in wireless systems, authenticating the identities of numerous nodes is imperative. Traditional cryptography-based authentication methods suffer from issues such as low compatibility, reliability, and high complexity. Physical-Layer Authentication (PLA) is emerging as a promising complement due to its exploitation of unique properties in wireless environments. Recently, Machine Learning (ML)-based PLA has gained attention for its intelligence, adaptability, universality, and scalability compared to non-ML approaches. However, a comprehensive overview of state-of-the-art ML-based PLA and its foundational aspects is lacking. This paper presents a comprehensive survey of characteristics and technologies that can be used in the ML-based PLA. We categorize existing ML-based PLA schemes into two main types: multi-device identification and attack detection schemes. In deep learning-based multi-device identification schemes, Deep Neural networks are employed to train models, avoiding complex processing and expert feature transformation. Deep learning-based multi-device identification schemes are further subdivided, with schemes based on Convolutional Neural networks being extensively researched. In ML-based attack detection schemes, receivers utilize intelligent ML techniques to set detection thresholds automatically, eliminating the need for manual calculation or knowledge of channel models. ML-based attack detection schemes are categorized into three sub-types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Additionally, we summarize open-source datasets used for PLA, encompassing Radio Frequency fingerprints and channel fingerprints. Finally, this paper outlines future research directions to guide researchers in related fields.

关键词： Physical-layer authentication Machine learning Identity security

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Multimodal Semantic communication for Generative Audio-Driven Video Conferencing

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IEEE wireless communicationS LETTERS 2025年第1期14卷 93-97页

作者： Tong, Haonan Li, Haopeng Du, Hongyang Yang, Zhaohui Yin, Changchuan Niyato, Dusit Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conver Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Adv Informat Network Lab Beijing 100876 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore

This letter studies an efficient multimodal data communication scheme for video conferencing. In our considered system, a speaker gives a talk to the audiences, with talking head video and audio being transmitted. Since the speaker does not frequently change posture and high-fidelity transmission of audio (speech and music) is required, redundant visual video data exists and can be removed by generating the video from the audio. To this end, we propose a wave-to-video (Wav2Vid) system, an efficient video transmission framework that reduces transmitted data by generating talking head video from audio. In particular, full-duration audio and short-duration video data are synchronously transmitted through a wireless channel, with neural networks (NNs) extracting and encoding audio and video semantics. The receiver then combines the decoded audio and video data, as well as uses a generative adversarial network (GAN) based model to generate the lip movement videos of the speaker. Simulation results show that the proposed Wav2Vid system can reduce the amount of transmitted data by up to 83% while maintaining the perceptual quality of the generated conferencing video.

关键词： Feature extraction Visualization Videoconferences Data mining wireless communication Video codecs Streaming media Receivers Decoding Streams Multimodal semantic communication video generation generative adversarial network

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Up-Downlink AoI-Driven Multi-Source Data Collection in UAV-Assisted wireless sensor networks

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IEEE TRANSACTIONS ON wireless communicationS 2025年第2期24卷 1178-1192页

作者： Zhao, Mingxiong Xiao, Yiming Yao, Jianping Wang, Tongda Lee, Jemin Quek, Tony Q. S. Yunnan Univ Natl Pilot Sch Software Kunming 650500 Peoples R China Minist Educ Engn Res Ctr Integrat & Applicat Digital Learning Beijing 100039 Peoples R China Minist Educ Engn Res Ctr Cyberspace Kunming 650504 Peoples R China Yunnan Univ Finance & Econ Yunnan Key Lab Serv Comp Kunming 650221 Peoples R China Yunnan Univ Natl Pilot Sch Software Kunming 650504 Peoples R China Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China Chinese Univ Hong Kong Shenzhen Guangdong Prov Key Lab Future Networks Intelligenc Shenzhen 518172 Peoples R China Yonsei Univ Sch Elect & Elect Engn Seoul 03727 South Korea Singapore Univ Technol & Design Singapore 487372 Singapore

This paper explores an unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN), in which one UAV-enabled mobile data collector periodically collects data from a set of ground sensor nodes (SNs) to the data center (DC) and then DC transmits the processed data back to a group of ground users to fulfill their diverse needs. To accurately evaluate information freshness, we introduce the Age of Multi-sensor Association Information (AomaI) metric by incorporating the multi-source and up-downlink aspects. Under this framework, we formulate the optimization problem aiming to minimize the average AomaI for all users. To tackle this non-convex problem, we decompose it into two sub-problems: the SN-side optimization problem and the UAV-side optimization problem. For the first subproblem, we propose parallel optimization and primal-dual methods to obtain the optimal solution. For the second subproblem, we first determine the optimal UAV transmission power, then develop the data processing and results distribution scheduling strategies for the DC, and lastly propose the task-associated genetic algorithm (TAGA) and the improved Nawas-Enscore-Ham (INEH) algorithm to design the UAV's visiting order. Simulation results demonstrate that uplink and downlink AoI influence each other, and the consideration of up-downlink AoI can effectively enhance the freshness of AomaI.

关键词： Autonomous aerial vehicles wireless sensor networks Data collection Uplink Optimization Downlink Trajectory Simulation Correlation Time measurement Unmanned aerial vehicle age of information data collection correlated information trajectory design scheduling

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Flexible Pressure sensor Composed of Multi-Layer Textile Materials for Human-Machine Interaction Applications

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ACS sensorS 2025年第1期10卷 350-359页

作者： Wang, Dakai Ma, Guoliang Zhang, Xiangxiang Zheng, Kejin Zhang, Junfeng Ma, Zhichao Han, Zhiwu Ren, Luquan Jilin Univ Key Lab Bion Engn Minist Educ Changchun 130022 Peoples R China Yanshan Univ State Key Lab Crane Technol Qinhuangdao 066000 Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Peoples R China Liaoning Acad Mat Inst Struct & Architected Mat Shenyang 110167 Peoples R China

Flexible pressure sensors have shown significant application prospects in fields such as artificial intelligence and precision manufacturing. However, most flexible pressure sensors are often prepared using polymer materials and precise micronano processing techniques, which greatly limits the widespread application of sensors. Here, this work chooses textile material as the construction material for the sensor, and its latitude and longitude structure endows the sensor with a natural structure. The flexible pressure sensor was designed using a multilayer stacking strategy by combining multilayer textile materials with two-dimensional MXene materials. The experiment shows that its sensitivity is 52.08 kPa-1 at 30 and 7.29 kPa-1 within 30-120 kPa. As a demonstration, these sensors are applied to wireless human motion monitoring, as well as related applications involving auxiliary communication and robotic arm integration. Furthermore, relevant demonstrations of sensor array applications are presented. This work provides inspiration for the design and application of flexible pressure sensors.

关键词： pressure sensor textile multilayer high sensitivity human-machine interaction

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Defeating CSI obfuscation mechanisms: A study on unauthorized Wi-Fi Sensing in wireless sensor network

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COMPUTER networkS 2025年 263卷

作者： Chu, Zhiming Li, Guyue Meng, Qingchun Li, Haobo Zeng, Yuwei Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China Univ Dundee Sch Sci & Engn Dundee DD14HN Scotland Minist Educ China Key Lab Cyberspace Secur Henan Key Lab Cyberspace Situat Awareness Zhengzhou 450001 Peoples R China

The proliferation of Wi-Fi sensing technology has raised significant privacy concerns due to potential unauthorized environmental monitoring. As a typical countermeasure, the Channel State Information (CSI) fuzzer uses a time-varying filter at the transmitter to obfuscate CSI, allowing only legitimate receiver who has the pre-shared filter parameters as keys to restore the original CSI. In this work, we present SnoopFi, a framework enabling unauthorized reconstruction of environment-matching sensing signals from obfuscated CSI, even with limited training samples. SnoopFi acquires accurate raw CSI when attackers exploit security vulnerabilities to obtain keys. It can also generate a new base signal that reflect the physical environment for sensing when the attackers' capabilities are limited. SnoopFi employs two strategies to negate the filter's effects: (1) The attacker first attempts to guess the keys, and then it inverts the filter by modeling the nonlinear relationship between the filter's response and the keys;(2) With multiple receiving antennas, the attacker utilizes the ratio of CSIs between different antennas to wipe off the filter effect. Once the obfuscation is removed, SnoopFi uses a few-shot learning technique for precise sensing of user localization with constrained training samples. The experimental results show that SnoopFi achieves localization accuracies of 91.79% and 92.05% under the two strategies, respectively, with an average of only 18 samples per class.

关键词： Wi-Fi sensing Privacy concerns Channel obfuscation Unauthorized sensing wireless security

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Performance Analysis of RIS-Enhanced Secure Transmission for Symbiotic IoV Systems

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IEEE TRANSACTIONS ON COGNITIVE communicationS AND networkING 2025年第1期11卷 454-464页

作者： Ji, Baofeng Zhang, Jingjing Zhang, Hui Yu, Yang Zhang, Gaoyuan Fan, Huitao Mumtaz, Shahid Henan Univ Sci & Technol Sch Informat Engn Luoyang 471023 Peoples R China Longmen Lab Intelligent Syst Sci & Technol Innovat Ctr Luoyang 471000 Peoples R China China Shipbuilding Ind Corp Luoyang Inst Ship Mat Inst Beijing 100097 Peoples R China Future Secur Inst Grp 360 San Francisco CA 94105 USA Longmen Lab Luoyang 471000 Peoples R China Silesian Tech Univ Dept Appl Informat PL-44100 Gliwice Poland Nottingham Trent Univ Dept Engn Nottingham NG1 4FQ England

With the continuous development of information and communication technology, it is critical to design novel spectrum, energy and cost-efficient wireless communication technologies to support massive Internet of Things (IoT) devices over a large area. Symbiotic communication that integrates active and passive communications has emerged as a promising technology to fulfill such demand. This paper focuses on using reconfigurable intelligent surfaces (RIS) to address the communication challenges faced by vehicles, such as data privacy concerns, network security threats, and interference issues. The key idea of symbiotic communication is to modify and reflect the incident signal from the active main transmitter. Therefore, the base model of this paper is a transmitter consisting of radio frequency (RF) signal and RIS that transmits a signal to one of the vehicles. In this process, inevitable threats of interference or eavesdropping arise. To ensure secure communication in Internet of Vehicles (IoV) systems, we establish a functional relationship between security performance of the system and variables such as the number of RIS elements and the aggregate interferences. We validate the accuracy of the analytical results through Monte Carlo simulations. Simulation outcomes demonstrate that increasing the number of RIS elements can mitigate adverse effects from aggregate interference or eavesdropping, thereby enhancing the security performance of IoV systems.

关键词： Interference Security Eavesdropping wireless communication Symbiosis Aggregates Research and development Symbiotic communication reconfigurable intelligent surfaces aggregate interference Internet of Vehicles security performance Monte Carlo simulations

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