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An Efficient Direct Downlink Sensing Method Using 5G NR SSB Signals in Perceptive Mobile Networks

IEEE Internet of Things Journal 2025年第11期12卷 15360-15369页

作者： Li, Hang Xiang, Yang Guo, Qinghua Liu, Lizhe Huang, Xiaojing Cheng, Zhiqun Pang, Yashan Hangzhou Dianzi University School of Electronics and Information Hangzhou310018 China Academy of Network and Communications of CETC National Key Laboratory of Advanced Communication Networks Hebei Shijiazhuang050081 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia University of Technology Sydney Global Big Data Technologies Centre UltimoNSW2007 Australia

In perceptive mobile networks (PMNs), using 5G new radio (NR) signals for direct sensing poses a significant challenge to practical implementation due to the high computational complexity involved in estimating sensing parameters. In this article, an efficient sensing method is proposed to incorporate both downlink active sensing and passive sensing to estimate multiple sensing parameters, including delays, Angle of Arrival (AoA), Angle of Departure (AoD), and Doppler. In particular, it exploits the synchronization signal blocks (SSBs) to facilitate sensing with multiple remote radio units (RRUs). To reduce the computational complexity of direct sensing, a sparse model is developed to decouple multiple sensing parameter estimation, enabling efficient sensing method design. Then, leveraging unitary approximate message passing (UAMP) and sparse Bayesian learning (SBL), we propose an efficient method to achieve parameter estimation and association with corresponding RRUs. This method is further extended to general scenarios involving multiple path components with the same delay. Extensive simulations demonstrate the effectiveness of the proposed method, showing that it outperforms the existing ones in terms of sensing accuracy and complexity. © 2014 IEEE.

关键词： 5G mobile communication systems

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Meta-Fluid Antenna for Multiplexing Multiple Users in Rich Scattering Environments 10

Meta-Fluid Antenna for Multiplexing Multiple Users in Rich S...

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10th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, MAPE 2024

作者： Huang, Jiewei Meng, Huan Ning, Lei Liu, Baiyang Wong, Kai-Kit Tong, Kin-Fai Wong, Hang College of Big Data and Internet Shenzhen Technology University Shenzhen China University College London Department of Electronic and Electrical Engineering London United Kingdom City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Hong Kong

ISBN: (纸本)9798331533922

With the rapid development of wireless communication technologies, supporting a massive number of user devices has become a significant challenge. Traditional multiple-input multiple-output (MIMO) technology requires sophisticated signal processing techniques to support a large number of terminal devices in a rich scattering environment. To address this problem, this paper proposes a full-wave simulation model of a meta-fluid antenna that enables multiple devices to access the channel simultaneously, with Rayleigh fading mimicked by artificial rich-scattering generators. This multiple access scheme is referred to as fluid antenna multiple access (FAMA). The FAMA technology enhances system multiplexing efficiency and network performance by allowing user devices to dynamically select optimal receiving positions, thereby maximizing the signal-to-interference ratio (SIR). The proposed method aims to simplify signal processing complexities for terminal devices and eliminate the need for channel state information (CSI) from base stations, thus achieving a larger number of connections and higher data rates by leveraging scattering with low system complexity. © 2024 IEEE.

关键词： Rayleigh fading

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LRCu: A High-Performance Parallel Program for Identifying Hub Genes 17

LRCu: A High-Performance Parallel Program for Identifying Hu...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Liu, Jiaxin Ji, Xinglai Nanjing University of Posts and Telecommunications Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing210003 China Yunnan University State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunan Kunming650091 China

ISBN: (纸本)9798331507398

The LeaderRank algorithm identifies influential nodes using the idea of the random walk and performs well in the identification of hub genes in biological networks. The rapid development of biological technologies promotes the evolution of networks to be highly complex and increasing largely in size. In this study, we introduce LRCu, for computing LeaderRank using CUDA, to identify hub genes. The results show that the parallel implementation is highly effective and achieves a speedup of more than 42 times for a 13,000-gene network, thereby enhancing large-scale network computation. © 2024 IEEE.

关键词： CUDA gene co-expression network hub gene parallel computing

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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Decision mamba: reinforcement learning via hybrid selective sequence modeling 24

Decision mamba: reinforcement learning via hybrid selective ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Sili Huang Jifeng Hu Zhejian Yang Liwei Yang Tao Luo Hechang Chen Lichao Sun Bo Yang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education and School of Artificial Intelligence Jilin University China School of Artificial Intelligence Jilin University China Institute of High Performance Computing Agency for Science Technology and Research Singapore Lehigh University Bethlehem Pennsylvania Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

ISBN: (纸本)9798331314385

Recent works have shown the remarkable superiority of transformer models in reinforcement learning (RL), where the decision-making problem is formulated as sequential generation. Transformer-based agents could emerge with self-improvement in online environments by providing task contexts, such as multiple trajectories, called in-context RL. However, due to the quadratic computation complexity of attention in transformers, current in-context RL methods suffer from huge computational costs as the task horizon increases. In contrast, the Mamba model is renowned for its efficient ability to process long-term dependencies, which provides an opportunity for in-context RL to solve tasks that require long-term memory. To this end, we first implement Decision Mamba (DM) by replacing the backbone of Decision Transformer (DT). Then, we propose a Decision Mamba-Hybrid (DM-H) with the merits of transformers and Mamba in high-quality prediction and long-term memory. Specifically, DM-H first generates high-value sub-goals from long-term memory through the Mamba model. Then, we use sub-goals to prompt the transformer, establishing high-quality predictions. Experimental results demonstrate that DM-H achieves state-of-the-art in long and short-term tasks, such as D4RL, Grid World, and Tmaze benchmarks. Regarding efficiency, the online testing of DM-H in the long-term task is 28× times faster than the transformer-based baselines.

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Signage Detection Based on Adaptive SIFT 1

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Wang, Jiaji Chen, Shuwen Shao, Jiaqi Qu, Hui Wang, Ziyi School of Physics and Information Engineering Jiangsu Second Normal University Nanjing211200 China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China

ISBN: (数字)9789819967063

ISBN: (纸本)9789819967056

In navigation and wayfinding applications, signage is crucial for finding destinations. This paper proposes a new method for detecting signage, with the aim of helping blind individuals navigate unfamiliar indoor environments. To enhance the accuracy of signage detection and eliminate interference, the proposed method initially obtains the attended areas through a saliency map. The signage is then identified in these attended areas using SIFT algorithm-based adaptive threshold. This innovative approach is capable of detecting and recognizing multiple signs. Finally, audio facilitates the detection and recognition of results transferred to the users. Our experimental results, based on indoor signage datasets we collected, demonstrate the efficiency of our proposed method. On average, it takes 0.104 s to process the data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer programming

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Task Offloading Optimization Based on Position Prediction in Pedestrian- Robot Mixed Traffic Flows

Task Offloading Optimization Based on Position Prediction in...

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International Conference on Computer and Communications (ICCC)

作者： Dawen Zheng Lusheng Wang Caihong Kai Min Peng Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision with pedestrians, and the tasks to be offloaded are closely related to pedestrians. How to analyze the tasks of robots and select the appropriate roadside unit is an important issue. In this paper, the social force model is used to predict the positions of pedestrians and robots, taking into account the influence of various forces to avoid collisions. A task offloading resource optimization algorithm with position prediction is proposed. According to the predicted information, the size and position distribution of all tasks in the scenario are obtained, and then the neural network trained beforehand based on deep Q-Iearning is used to generate a task offloading strategy. The simulation results show that the running time of the proposed algorithm is very short, and the resource allocation required for task offloading is completed in advance based on the predicted information before robots arriving the corresponding positions. Besides, the algorithm significantly reduces the task offloading delay.

关键词： Service robots Simulation Force Predictive models Prediction algorithms Delays Task analysis

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A Pruning Based Continuous RkNN Query Algorithm for Large k

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Chinese Journal of Electronics 2023年第3期21卷 523-527页

作者： Shengsheng WANG Sheng CHAI Qiannan LV College of Computer Science and Technology Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Reverse k-nearest neighbor (RkNN) query is a hot-spot in spatio-temporal database. With the developing of mobile devices, continuous RkNN query becomes more and more important. Most of the previous methods use the two-step (filter-refinement) processing. However, for large k, the amount of calculation becomes very heavy, especially in the filter step. This is not acceptable for most mobile devices. This paper presents a novel algorithm for continuous RkNN queries based on pruning heuristics. The experiments show that the processing time of our method is still acceptable for most mobile devices when k is large.

关键词： Location awareness databases Heuristic algorithms Filtering algorithms Mobile handsets Mobile applications

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Harmonic Long-Range Backscatter with Frequency-Shifted Lightweight Tag 100

Harmonic Long-Range Backscatter with Frequency-Shifted Light...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Lin, Junliang Zhang, Xiannan Xu, Rongtao Wang, Gongpu Quek, Tony Q. S. Beijing Jiaotong University Beijing Key Laboratory of Transportation Data Analysis and Mining Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Singapore University of Technology and Design Department of Information Systems Technology and Design Singapore

ISBN: (纸本)9798331517786

In this paper, we introduce a simplified long-range (LoRa) backscatter system that enables a lightweight tag to communicate with a remote transceiver using chirp carrier and harmonic backscatter. The key idea is twofold: first, we delegate the chirp waveform generation from the tag to the transceiver, thereby maintaining lightweight tag design;second, we introduce a frequency shift on the tag during its carrier modulation to create harmonic backscatter, thereby mitigating self-interference. To refine the framework, we first detail the system model, including the design for harmonic backscatter. We then present an efficient method to address synchronization and detection issues at the transceiver. Finally, we prototype the proposed system and evaluate its performance. Experimental results demonstrate that in a corridor environment with a carrier power of 0 dBm, the tag can backscatter a signal from the transceiver at a maximum bit rate of 4 kb/s over a distance of 50 meters. © 2024 IEEE.

关键词： Harmonic analysis

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Angiosperm Genus Classification by RBF-SVM 1

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Chen, Shuwen Wang, Jiaji Ni, Yiyang Shao, Jiaqi Qu, Hui Wang, Ziyi School of Physics and Information Engineering Jiangsu Second Normal University Nanjing211200 China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China

ISBN: (数字)9789819967063

ISBN: (纸本)9789819967056

Angiosperm genus classification performance has plateaued in the last few years. This paper proposed a novel method based on gray-level co-occurrence matrix and radial basis function kernel support vector machine for angiosperm genus classification. We collected a 300-image dataset, 100 are Hibiscus, 100 are Orchis, and the rest 100 are Prunus by digital camera. The results showed that our method achieved an accuracy of 84.73 ± 0.41%. In all, this method is promising in angiosperm genus classification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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