检索结果-内蒙古大学图书馆

USSD: Unsupervised Sleep Spindle Detector

IEEE ACCESS 2025年 13卷 18644-18659页

作者： Ramirez, Edgardo Estevez, Pablo A. Adams, Martin D. Perez, Claudio A. Garrido Gonzalez, Marcelo Peirano, Patricio Univ Chile Dept Elect Engn Santiago 8330015 Chile Millennium Inst Intelligent Healthcare Engn Santiago 1025000 Chile Ctr Intervent Med Precis & Adv Cellular Therapy IMPACT Santiago 1025000 Chile Univ Chile Inst Nutr & Food Technol Sleep Lab Santiago 8330015 Chile Univ Diego Portales UDP Fac Educ Santiago 8370109 Chile

Sleep spindles (SSs) appear in electroencephalogram (EEG) recordings during sleep stage N2, and they are usually detected through visual inspection by an expert. Labeling SSs in large datasets is time-consuming and depends on the expert criteria. In this work, we propose an unsupervised SS detector based on dictionary learning called the Unsupervised Sleep Spindle Detector (USSD). The proposed detector learns prototype SSs of different lengths (called atoms). An unsupervised adaptive threshold method based on the distribution of the automatically detected SS lengths is developed, which allows the adaptation of the USSD algorithm to different datasets in an unsupervised way. For each detection, the USSD estimates the probability of being an SS. The USSD performances on the labeled MASS-SS2 and INTA-UCH datasets yield F1-scores of $0.72 \pm 0.02$ and $0.72 \pm 0.04$ , respectively. The USSD outperforms the A7 and LUNA detectors, which are traditional unsupervised models. Next, we fine-tune the resulting USSD model with 20% of the labeled MASS-SS2 and INTA-UCH datasets, achieving F1 scores of $0.78 \pm 0.06$ and $0.75 \pm 0.05$ , respectively. In addition, the SSs detected by USSD on the unlabeled CAP dataset are used to pre-train a supervised deep learning method, which after fine-tuning with 20% of the MODA dataset, reaches an F1-score of $0.81 \pm 0.02$ .

关键词： Sleep Electroencephalography Signal processing algorithms Brain modeling Detectors Dictionaries matching pursuit algorithms Machine learning Recording Filtering algorithms EEG sleep spindle unsupervised learning dictionary learning

来源：评论

学校读者我要写书评

暂无评论

Near-Field High-Speed User Sensing in Wideband mmWave Communications: algorithms and Bounds

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 919-935页

作者： Miao, Hongxia Peng, Mugen Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Integrated sensing and communications (ISAC) has been expected to be a key technique in the sixth-generation cellular networks. With the increase of carrier frequency (to millimeter-wave or Terahertz spectrum) and antenna array size (to extremely large-scale antenna) in wireless communications, the near-field area is enlarged and cannot be ignored. Accordingly, the channel model and its estimation algorithms are changed, which bring new chances in ISAC. However, the impact of both Doppler and spatial wideband effects, caused by high mobility and multicarriers, on sensing performance using communication signals is not well studied. In this study, these two effects are shown to be helpful in user sensing. First, the channel model is proposed for a high-speed moving user transmitting an orthogonal frequency division multiplex (OFDM) signal, where there are six unknown parameters. Then, the Cramer-Rao lower bounds (CRLB) for joint six parameter estimation is determined, where the impact of the near-field parameter and the velocity on the CRLB of positioning are discussed and quantified. Further, to compensate for the deficiency that the CRLB is tight only in high signal-to-noise-ratio (SNR) scenarios, we derive the Ziv-Zakai bound (ZZB) for positioning by exploiting the prior information on positioning parameters. Subsequently, a joint position and velocity parameter estimation algorithm is designed by first performing a discrete fractional Fourier transform on the received signal to obtain a coarse estimation and then refining it by Newton-based refinement. Numerical results coincide with our analysis.

关键词： OFDM Estimation Signal processing algorithms Channel estimation Chirp Frequency estimation Training Signal to noise ratio Sensors matching pursuit algorithms Near-field ISAC high-speed moving CRLB ZZB

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Near-Field User Localization With Beam Squint in Wideband XL-MIMO Systems

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第2期24卷 1568-1583页

作者： Lei, Hao Zhang, Jiayi Xiao, Huahua Ng, Derrick Wing Kwan Ai, Bo Beijing Jiaotong Univ State Key Lab Adv Rail Autonomous Operat Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China ZTE Corp State Key Lab Mobile Network & Mobile Multimedia T Shenzhen 518057 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Extremely large-scale multiple-input multiple-output (XL-MIMO) is gaining attention as a prominent technology for enabling the sixth-generation (6G) wireless networks. However, the vast antenna array and the huge bandwidth introduce a non-negligible beam squint effect, causing beams of different frequencies to focus at different locations. One approach to cope with this is to employ true-time-delay lines (TTDs)-based beamforming to control the range and trajectory of near-field beam squint, known as the near-field controllable beam squint (CBS) effect. In this paper, we investigate the user localization in near-field wideband XL-MIMO systems under the beam squint effect and spatial non-stationary properties. Firstly, we derive the expressions for Cram & eacute;r-Rao Bounds (CRBs) for characterizing the performance of estimating both angle and distance. This analysis aims to assess the potential of leveraging CBS for precise user localization. Secondly, a user localization scheme combining CBS and beam training is proposed. Specifically, we organize multiple subcarriers into groups, directing beams from different groups to distinct angles or distances through the CBS to obtain the estimates of users' angles and distances. Furthermore, we design a user localization scheme based on a convolutional neural network model, namely ConvNeXt. This scheme utilizes the inputs and outputs of the CBS-based scheme to generate high-precision estimates of angle and distance. The numerical results derived from CRBs reveal that the inherent spatial non-stationary characteristics notably increase the CRB for angle, but have an insignificant impact on the CRB for distance estimation. In addition, the CRBs for both angle and distance decrease with increasing bandwidth and number of subcarriers. More importantly, our proposed ConvNeXt-based user localization scheme achieves centimeter-level accuracy in localization estimates.

关键词： Location awareness Accuracy Channel estimation Array signal processing Wideband Vectors matching pursuit algorithms Uplink Noise Maximum likelihood estimation Near-field XL-MIMO user localization deep learning beam squint integrated sensing and communication

来源：评论

学校读者我要写书评

暂无评论

Adaptive Block Sparse Backtracking-Based Channel Estimation for Massive MIMO-OTFS Systems

引用

IEEE INTERNET OF THINGS JOURNAL 2025年第1期12卷 673-682页

作者： Wang, Han Chen, Qiulin Wang, Xianpeng Du, Wencai Li, Xingwang Nallanathan, Arumugam Yichun Univ Coll Phys Sci & Engn Yichun 336000 Peoples R China Hubei Univ Automot Technol Inst Vehicle Informat Control & Network Technol Shiyan 442002 Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China Univ St Joseph Inst Data Engn & Sci Macau Peoples R China Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo 454000 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Kyung Hee Univ Dept Elect Engn Yongin 17104 Gyeonggi South Korea

Orthogonal time frequency space (OTFS) modulation, combined with massive multiple-input-multiple-output (MIMO) technology, offers robust performance in high-mobility environments and high-user densities by capturing the full diversity of the wireless channel and effectively utilizing spatial multiplexing. This article introduces an adaptive block sparse backtracking (ABSB) algorithm designed to enhance channel estimation in OTFS with massive MIMO (massive MIMO-OTFS) systems. The proposed ABSB algorithm features dynamic block size adjustment based on the residual signal, improving its adaptability to the varying sparsity structure of the channel. Additionally, the algorithm extends the selection range of related block atoms to increase redundancy, reducing the risk of underfitting. Comprehensive simulation results demonstrate that the ABSB algorithm significantly outperforms traditional pilot-based methods in terms of channel estimation accuracy. It also surpasses the block orthogonal matching pursuit (BOMP) method as well as other classical compressed sensing methods. Specifically, the ABSB algorithm achieves up to a 20% reduction in estimation error compared to some of these traditional methods. The enhanced adaptability and robustness of the ABSB algorithm make it a promising solution for channel estimation in massive MIMO-OTFS systems, paving the way for more reliable and efficient next-generation wireless communications.

关键词： Channel estimation Modulation Wireless communication Heuristic algorithms matching pursuit algorithms Massive MIMO Backtracking Adaptive block sparse channel estimation massive multiple-input-multiple-output (MIMO) orthogonal time frequency space (OTFS)

来源：评论

学校读者我要写书评

暂无评论

Bayesian Learning-Aided Channel Estimators for Superimposed-Pilot-Based OTFS Systems

引用

IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第2期73卷 950-967页

作者： Sheikh, Mudasir Ahmad Rajoriya, Anupama Singh, Prem Budhiraja, Rohit Indian Inst Technol Kanpur IIT Kanpur Dept Elect Engn Kanpur 208016 India IIIT Bangalore Bengaluru 560100 India

Orthogonal time-frequency space (OTFS) transmission is a promising multi-carrier modulation scheme for high-mobility communication scenarios. We consider a novel multiple frame-based superimposed pilot (SP)-OTFS system, which exhibits two-dimensional (2-D) delay-Doppler domain channel sparsity. We design a multiple-frame-based coupled prior sparse Bayesian learning (M-CPSBL) algorithm to exploit the 2-D channel sparsity while estimating it. This algorithm, however, inverts a large-dimensional matrix in each iteration with a high complexity. We next design its low-complexity version, which works on a smaller dimensional problem. The two proposed algorithms are used for SP-OTFS systems, which superpose low-powered pilots on to data signals. The combination of M-CPSBL and SP-OTFS frameworks is shown to outperform existing OTFS systems in terms of channel estimation normalized mean squared error, and spectral efficiency (SE). We derive a closed-form expression to optimize pilot and data powers to maximize the signal-to-interference-plus-noise ratio of SP-OTFS systems. We numerically show that optimal power allocation reduces the bit error rate, and increases the SE.

关键词： Channel estimation Symbols Interference Doppler effect Delays matching pursuit algorithms Transforms Orthogonal time-frequency space (OTFS) sparse Bayesian learning (SBL) superimposed pilot (SP)

来源：评论

学校读者我要写书评

暂无评论

Instantaneous Energy Consumption Estimation for Electric Buses With a Multi-Model Fusion Method

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第1期26卷 371-381页

作者： Lin, Mingqiang Chen, Shouxin Meng, Jinhao Wang, Wei Wu, Ji Chinese Acad Sci Quanzhou Inst Equipment Mfg Jinjiang 362200 Fujian Peoples R China Chinese Acad Sci Fujian Inst Res Struct Matter Jinjiang 362200 Fujian Peoples R China Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350116 Fujian Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Anhui Peoples R China

The accurate instantaneous energy consumption estimation for electric buses (EBs) is helpful for drivers to schedule and use EBs more reasonably, which is also essential for the realization of electrification, intelligence, and cyber-physical integration of public transportation systems. In this paper, real-world driving data collected from ten electric buses in Beijing city are obtained, and the influencing factors of energy consumption from multiple sources such as driving-related, vehicle status, and external environment are extracted. With the extracted key influencing factors, we propose a new multi-model fusion method to estimate the 1 Hz energy consumption of electric buses. The results demonstrate that the proposed fusion model has a mean absolute percentage error of 6.21%, which is superior to the single model. Finally, we use the control variable method for feature importance analysis, which provides valuable insights into the factors that affect the energy consumption of EBs in real-world scenarios.

关键词： Energy consumption Data models Predictive models Estimation Accuracy Temperature matching pursuit algorithms Machine learning Motors Feature extraction electric buses single-model fusion model feature importance

来源：评论

学校读者我要写书评

暂无评论

Robust Activity Detection for Massive Access using Covariance-based matching pursuit

Robust Activity Detection for Massive Access using Covarianc...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinjue Wang Esa Ollila Sergiy A. Vorobyov Department of Information and Communications Engineering Aalto University Espoo Finland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We propose a robust activity detection for grant free random access using greedy covariance-learning-based matching pursuit (RCL-MP) algorithm. The method incorporates a robust loss function into the Gaussian negative log-likelihood function, and uses matching pursuit framework for greedily selecting the indices of active users. This algorithm employs a flexible loss function effectively recovering sparse support under non-Gaussian noise conditions. Furthermore, we numerically demonstrate the robustness of RCL-MP across various conditions in massive access scenarios.

关键词： Performance evaluation Accuracy Noise matching pursuit algorithms Signal processing algorithms Robustness Acoustics Speech processing

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Based Energy-Efficient Hybird RSMA for UAV-Assisted mmWave Communications

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 4625-4638页

作者： Wang, Kehao Sun, Yingzhao Quek, Tony Q. S. Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China Singapore Univ Technol & Design Singapore Singapore

This paper investigates hybrid rate-splitting multiple access (RSMA) in unmanned aerial vehicle (UAV) assisted millimeter-wave (mmWave) communication network (RSMA-UAV-MMWCN), where a UAV transmits messages to multiple ground user equipment under the influence of an external jammer. We formulate a non-convex joint optimization problem involving hybrid RSMA matrices and a common rate allocation vector, with the objective of maximizing energy efficiency while approaching the performance of ideal hybrid RSMA. Departing from traditional non-convex problem-solving methods, we introduce a hybrid RSMA optimization scheme based on deep residual networks to enhance the feasibility of hybrid precoding and decoding. Initially, due to the absence of standardized and universal datasets, we propose a dataset generation algorithm to create training and testing datasets for subsequent communication model training. Subsequently, we construct a loss function that integrates the objective function with the constraints of the optimization problem. Lastly, to ensure that the optimization variables strictly comply with the constraints, we design a mandatory constraint module comprising modulus, power, and rate constraint sub-modules. Simulation results demonstrate that the proposed algorithm surpasses traditional optimization methods, and RSMA shows significant advantages over conventional multiple access (MA) schemes.

关键词： Optimization Autonomous aerial vehicles Millimeter wave communication Array signal processing Deep learning Resource management matching pursuit algorithms NOMA Interference Training Deep residual network energy efficiency hybrid rate-splitting multiple access (RSMA) millimeter-wave

来源：评论

学校读者我要写书评

暂无评论

Over-the-Air FEEL With Integrated Sensing: Joint Scheduling and Beamforming Design

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第4期24卷 3273-3288页

作者： Asaad, Saba Wang, Ping Tabassum, Hina York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada

Employing wireless systems with dual sensing and communications functionalities is becoming critical in next generation of wireless networks. In this paper, we propose a robust design for over-the-air federated edge learning (OTA-FEEL) that leverages sensing capabilities at the parameter server (PS) to mitigate the impact of target echoes on the analog model aggregation. We first derive novel expressions for the Cramer-Rao bound of the target response and mean squared error (MSE) of the estimated global model to measure radar sensing and model aggregation quality, respectively. Then, we develop a joint scheduling and beamforming framework that optimizes the OTA-FEEL performance while keeping the sensing and communication quality, determined respectively in terms of Cramer-Rao bound and achievable downlink rate, in a desired range. The resulting scheduling problem reduces to a combinatorial mixed-integer nonlinear programming problem (MINLP). We develop a low-complexity hierarchical method based on the matching pursuit algorithm used widely for sparse recovery in the literature of compressed sensing. The proposed algorithm uses a step-wise strategy to omit the least effective devices in each iteration based on a metric that captures both the aggregation and sensing quality of the system. It further invokes alternating optimization scheme to iteratively update the downlink beamforming and uplink post-processing by marginally optimizing them in each iteration. Convergence and complexity analysis of the proposed algorithm is presented. Numerical evaluations on MNIST and CIFAR-10 datasets demonstrate the effectiveness of our proposed algorithm. The results show that by leveraging accurate sensing, the target echoes on the uplink signal can be effectively suppressed, ensuring the quality of model aggregation to remain intact despite the interference.

关键词： Sensors Interference matching pursuit algorithms Downlink Array signal processing Uplink Servers Wireless sensor networks Reflection Computational modeling Over-the-air federated learning integrated sensing and computation Cram & eacute r-Rao bound device scheduling matching pursuit algorithm

来源：评论

学校读者我要写书评

暂无评论

Narrowband Interference Cancellation for OFDM Based on Deep Learning and Compressed Sensing

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 1612-1625页

作者： Hu, Yue Huang, Songkang Zhao, Lei Jiang, Ming Sun Yat Sen Univ Sch Elect & Informat Technol Sch Microelect Guangzhou 510275 Peoples R China China Telecom Res Inst Dept Mobile & Terminal Technol Res Guangzhou 510275 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Guangzhou 510275 Peoples R China

The orthogonal frequency division multiplexing (OFDM) technology has been widely used in modern wireless communication systems. Under the hostile wireless propagation channels, the transmitted signal may be corrupted by narrowband interference (NBI), resulting in the loss of data in part of the system band. To address this challenging problem, we propose a joint deep learning (DL) and compressed sensing (CS) approach to estimate and eliminate multiple NBIs. With unknown interfering sources, we first propose an NBI detection network (NDNet) trained with a new loss function to identify the number of NBIs. Different from existing networks, NDNet is designed to cope with both synchronous NBI (S-NBI) and asynchronous NBI (A-NBI). Based on the output of NDNet, an orthogonal matching pursuit (OMP) and improved dichotomous search (IDS) based NBI cancellation scheme, which is referred to as the OMP-IDS algorithm, is proposed to accurately estimate NBIs at a modest complexity. Furthermore, an enhanced OMP-IDS (eOMP-IDS) algorithm is devised to reduce the errors in estimating the frequencies interfered especially by multiple adjacent NBIs. The estimated NBIs can then be effectively cancelled. Theoretical analysis, simulations and experiments validate the feasibility and competitiveness of the proposed schemes.

关键词： OFDM Interference Frequency-domain analysis Estimation matching pursuit algorithms Wireless communication Prevention and mitigation Complexity theory Vectors Time-domain analysis Narrowband interference (NBI) orthogonal frequency division multiplexing (OFDM) NBI detection network (NDNet) improved dichotomous search (IDS) orthogonal matching pursuit (OMP)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：