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15th IEEE International Conference on Wireless communications and Signal processing, WCSP 2023

作者： Wang, He Zhang, Ting Zhao, Hangfang Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Communication and Networking Zhejiang Provincial Key Laboratory of Information Processing Hangzhou China Key Laboratory of Ocean Observation Imaging Testbed of Zhejiang Province Zhoushan China

ISBN: (纸本)9798350324662

A knowledge-aided approach for active target localization is presented and applied to the real-sea data to verify its feasibility in snapshot-deficient scenarios in the presence of reverberation. By leveraging the a priori reverberation covariance computed from a reverberation model, the knowledge-aided minimum variance distortionless response (KA-MVDR) is derived to facilitate adaptive processing and thus localize weak targets in reverberation. The KA-MVDR-based bearing-time record is employed to analyze the experimental data collected in the South China Sea, which verifies that the weak target echoes can be localized using single ping data with significantly reduced reverberation levels. Moreover, the impact of the bottom roughness mismatch on the model-based reverberation covariance and the performance of KA-MVDR are evaluated by the output signal-interference-plus-noise ratio, manifesting the robustness of this approach in real-sea applications. © 2023 IEEE.

关键词： active target localization reverberation knowledge-aided snapshot deficiency

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Distributed Semi-Supervised Learning with Positive and Unlabeled Data 3

Distributed Semi-Supervised Learning with Positive and Unlab...

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3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Liu, Ying Zhang, Chen Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

Due to the prejudicial interest on positive aspect and/or absent of negative training data on some applications, positive and unlabeled learning (PU learning) has attracted much attention. However, all of the existing PU learning algorithms belong to centralized processing. Yet, in many real-world scenarios, data are usually collected and stored over multiple nodes with restriction of geographic factors or memory space. Considering this, in this paper, we focus on distributed classification with positive and unlabeled data, and propose a distributed PU learning algorithm based on Semi-Supervised Support Vector Machine (dPU-S3VM). To exploit the underlying information in the unlabeled data, the manifold regularization is incorporated into the design of the cost function. Furthermore, the random feature map is used for mapping the original data to high dimensional kernel space such that the global optimization problem can be decentralized without the direct transmission of the original data. The convergence performance of the proposed dPU-S3VM algorithm is analyzed theoretically. Experimental results show that the proposed algorithm has better classification performance than the state-of-the-art PU learning algorithms on various synthetic and real-world datasets especially when the portion of labeled positive examples is small. © 2022 IEEE.

关键词： Support vector machines

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LSTM Network-based SNR Estimator for DS-UWB Wireless Sensor Network 9

LSTM Network-based SNR Estimator for DS-UWB Wireless Sensor ...

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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Liu, Qingzhi Yin, Zhendong Wu, Mingyang Wu, Zhilu Xu, Zhijiang Harbin Institute of Technology Heilongjiang province Harbin China Communication and Networking Zhejiang Provincial Key Laboratory of Information Processing Zhejiang Provincial Hangzhou China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Reliability measurement is vital for wireless communication and SNR is an important indicator of communication reliability. This paper mainly focused on the SNR estimation for the direct-sequence ultra-wideband (DS-UWB) wireless sensor network. The system channel is contaminated by AWGN and MAI. We provide an SNR estimator based on LSTM, an RNN network mainly designed for long-term dependency problems that can remember information for an extended period. The LSTM network is trained to predict the SNR of the input signal that has through code mapping. Compared with the original signal, the mapping data can distinguish the MAI and AWGN of the received bits, improving the estimator's RMSE performance. The simulation experiments show that using LSTM to design an SNR estimator for the DS-UWB wireless network can draw attention due to its simplicity and effectiveness. Moreover, the performance of the estimator is more accurate using mapping data. © 2022 IEEE.

关键词： Wireless sensor networks

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Fundamental Limits of Multiple-Access Integrated Sensing and communication Systems

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IEEE Transactions on information Theory 2025年第6期71卷 4317-4341页

作者： Liu, Yao Li, Min Liu, An Ong, Lawrence Yener, Aylin Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China University of Newcastle School of Engineering CallaghanNSW2308 Australia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results. © 1963-2012 IEEE.

关键词： Signal distortion

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A Scheduling-based Hybrid Medium Access Control Protocol for Underwater Acoustic Sensor Networks 15

A Scheduling-based Hybrid Medium Access Control Protocol for...

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15th IEEE International Conference on Wireless communications and Signal processing, WCSP 2023

作者： Zhao, Hongyu Chen, Huifang Xie, Lei Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Communication and Networking Zhejiang Provincial Key Laboratory of Information Processing Hangzhou310027 China Zhejiang University Zhoushan Ocean Research Center Zhoushan316021 China

ISBN: (纸本)9798350324662

Designing a media access control (MAC) protocol for underwater acoustic sensor networks (UASNs) related to application scenario is widely researched recently. From the perspective of data transmission characteristics of distributed data collection task via UASNs, a hybrid MAC protocol with centralized scheduling and distributed scheduling strategies is proposed in this paper. In the proposed MAC protocol, two scheduling strategies, namely the centralized scheduling for sensor nodes with periodic data and the distributed scheduling strategy for sensor nodes with intermittent data. And the scheduling strategy used by a sensor node is determined by its data rate. Moreover, the proportion of resource allocated for the centralized scheduling and the distributed scheduling is determined by the sink node according to the network load. Moreover, to improve channel utilization, specific control packets are designed to replace handshake packet interaction. Simulation results show that the proposed MAC protocol outperforms in terms of network throughput, end-to-end delay, packet delivery ratio, and energy consumption. © 2023 IEEE.

关键词： Hybrid Medium Access Control (MAC) Scheduling-based Underwater Acoustic Sensor Networks (UASNs)

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Multimodal Deep Learning Empowered Millimeter-Wave Beam Prediction

Multimodal Deep Learning Empowered Millimeter-Wave Beam Pred...

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IEEE Conference on Vehicular Technology (VTC)

作者： Binpu Shi Min Li Ming-Min Zhao Ming Lei Liyan Li Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Traditional millimeter-wave beam selection or prediction algorithms typically rely on beam scanning measurements at the transceivers, incurring substantial training overhead and exhibiting limited adaptability in diverse environments. Recent efforts have aimed to mitigate these challenges by incorporating sensing information, thereby reducing or eliminating the need for extensive beam training. However, existing works predominantly concentrate on exploiting a single sensing modality and often overlook the potential benefits of utilizing historical sensing information. In this paper, we introduce an intelligent beam prediction framework that leverages a deep integration of multimodal sensing data, encompassing GPS, camera, radar, and LiDAR data. The design proposed involves the application of customized deep neural networks to extract features from camera, radar, and LiDAR data. These extracted features, combined with user position and selected beam index, are concatenated to form an aggregated feature vector at each time instance. Subsequently, a time series of these concatenated feature vectors is utilized to exploit temporal correlation for beam prediction through a dedicated long short-term memory network module. Numerical simulations confirm the effectiveness of the proposed design and its superiority over several considered state-of-the-art baselines.

关键词： Training Laser radar Multimodal sensors Millimeter wave measurements Millimeter wave radar Feature extraction Cameras

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A Real-time Fall Detection System Using ToF Depth Images 10

A Real-time Fall Detection System Using ToF Depth Images

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10th IEEE International Conference on Smart City and Informatization, iSCI 2022

作者： Li, Rihui Li, Dongxiao Zhang, Ming College of Information Science and Electronic Engineering Zhejiang University China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China Zhejiang University Hangzhou China

ISBN: (纸本)9798350396317

Fall accident is a major health problem resulting in serious injuries even deaths, especially among the elderly. Moreover, the population is aging globally, indicating that more fall accidents will probably happen in the future. A robust fall detection system is essential for avoiding injuries and deaths in fall accidents. In this paper, we propose a vision-based real-time fall detection system using Time-of- Flight depth maps with pose classification, height calculation, and temporal information processing. Our method sticks to the fall definition. We first divide the process of fall into three categories, and each subject in each frame is classified into different categories based on the pose by efficient YOLOv4 model. Combining with the depth data and temporal information in a time window, the proposed system can extract subjects' height and downward velocity, which are the key factors in fall judgment. Lastly the detection is achieved by comparing them with certain criteria. According to experimental results, our detection system achieved an average accuracy 97.92 % with a recall of 97.5 % and a precision of 98.3 % in off-line test on public dataset, and all three metrics above 100% in real-time test. © 2022 IEEE.

关键词： Fall detection

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Position-Aware Beam Training for Near-Field Milimeter-Wave XL-MIMO communications

Position-Aware Beam Training for Near-Field Milimeter-Wave X...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yongcheng Liu Weicao Deng Min Li Min-Jian Zhao College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking (IPCAN)

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In Millimeter-wave extremely large-scale multiple-input multiple-output (XL-MIMO) communications, fast and reliable alignment of transceiver beams is essential for optimizing beamforming gain during data transmission. As XL-MIMO systems feature increasingly large antenna arrays, the electromagnetic propagation field shifts from the far field to the near field. In addition to angular domain beam training, near-field scenarios require distance domain beam training. However, many existing near-field beam training methods are essentially adaptations of far-field techniques, leading to issues such as excessive training overhead and suboptimal alignment. This paper introduces a position-aware beam training algorithm. In the initialization phase, position information is leveraged to reduce the beam search space. Subsequently, a multi-round iterative measurement strategy is employed. This approach updates the polar-domain codebook's posterior distribution using Bayes' rule based on measurements and dynamically adjusts the candidate beam set until the iteration termination condition is met. Numerical evaluations confirm the superiority of our proposed position-aware beam training algorithm, demonstrating significant enhancements in both normalized beamforming gain and achievable spectrum efficiency.

关键词： Training Vehicular and wireless technologies Array signal processing Heuristic algorithms Millimeter wave technology Millimeter wave propagation Transceivers

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Effective Rate Enhancement for RIS-Assisted Systems: An Irregular Element-Grouping Structure

Effective Rate Enhancement for RIS-Assisted Systems: An Irre...

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International Conference on Computing, networking and communications (ICNC)

作者： Yansong Bai Liyan Li Yunlong Cai Minjian Zhao College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking (IPCAN) Hangzhou China

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

This paper studies a wireless communication system assisted by a reconfigurable intelligent surface (RIS). We design a practical communication protocol that considers the time and power required for channel estimation and control feedback. Based on this protocol, we propose a novel irregular element-grouping RIS. By designing the selection topology, elements are irregularly selected from the RIS, providing additional spatial degrees of freedom despite a limited number of elements. To further reduce the overhead associated with channel estimation and control feedback, we employ an element-grouping method. Each group consists of a set of adjacent elements that share the same combined channel and phase shift. To maximize the effective rate (ER), we propose an algorithm that jointly optimizes the selection topology, phase shift design, and transmit power allocation. Numerical results demonstrate that the proposed structure increases the ER by up to 41% and 24% compared to regular RIS and regular RIS with element-grouping, respectively.

关键词： Wireless communication Protocols Network topology Channel estimation Reconfigurable intelligent surfaces Topology Resource management Data communication Optimization

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Enhancing mmWave Beam Prediction through Deep Learning with Sub-6 GHz Channel Estimate

Enhancing mmWave Beam Prediction through Deep Learning with ...

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IEEE Conference on Wireless communications and networking

作者： Weicao Deng Min Li Yongcheng Liu Ming-Min Zhao Ming Lei College of Information Science and Electronic Engineering and also with Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking (IPCAN) Zhejiang University Hangzhou China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Optimizing beamforming is crucial in mitigating pronounced propagation loss and ensuring reliable communication at millimeter-wave (mmWave) frequencies. Traditional beam optimization methods rely on either precise channel estimation or extensive beam training in the mm Wave band, both of which entail substantial pilot overhead. To alleviate this overhead, we leverage the spatial congruence between sub-6 GHz (sub-6G) and mm Wave channels and propose a sub-6G information and few pilots aided beam prediction network (SPBPNet) through deep learning. Specifically, the proposed SPBPNet comprises two cascaded modules: i) the angular information extraction module, which extracts angular features from the available sub-6G channel estimate and maps them to a minimal set of narrow beam directions to be measured in the mm Wave band; and ii) the beam prediction module, which takes limited beam training along the selected directions and then fuses measurements in the mm Wave band with the sub-6G channel information to generate mmWave beam predictions. Numerical results demonstrate that SPBPNet efficiently maps sub-6G channel estimates to mmWave beams and achieves a superior balance between performance and pilot overhead compared to state-of-the-art benchmarks. Moreover, SPBPNet exhibits resilience to varying sub-6G channel estimates at different signal-to-noise ratio levels.

关键词： Deep learning Training Millimeter wave technology Channel estimation Feature extraction Transceivers Data mining

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