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Impulsive noise estimation for underwater acoustic OFDM communication using signal-noise separation and distributed compressed sensing methods

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ALEXANDRIA ENGINEERING JOURNAL 2025年 122卷 243-254页

作者： Yang, Xiaoyu Zhou, Yuehai Yao, Junhui Tong, Feng Xiamen Univ Coll Ocean & Earth Sci Xiamen 361005 Peoples R China Hanjiang Natl Lab Wuhan 430000 Peoples R China Xiamen Univ Natl & Local Joint Engn Res Ctr Nav & Locat Serv Technol Xiamen 361005 Peoples R China

In underwater acoustic (UWA) orthogonal frequency division multiplexing systems, impulsive noise and carrier frequency offsets (CFOs) may contaminate subcarriers. Traditionally, CFOs and impulsive noise are estimated using null subcarriers, reducing spectral efficiency. This article proposes a signal-noise separation based and distributed compressed sensing based method to jointly estimate impulsive noise, channels, and CFOs using only pilot subcarriers, thereby significantly improving spectral efficiency. Specifically, the signal- noise separation method segregates channel and impulsive noise portions in a specific orthogonal subspace, preventing error propagation. To further facilitate impulsive noise estimation and reduce the required number of pilot subcarriers, the distributed compressed sensing method is utilized based on the significant sparse correlation of impulsive noise. Additionally, an adaptive sparsity detection algorithm for measuring the sparsity of impulsive noise is introduced. The effectiveness of the proposed methods is comprehensively validated through simulation experiments demonstrating improvements in impulsive noise estimation, channel estimation, and output signal-to-noise ratio (SNR), with gains of at least 26.30 dB, 22.91 dB, and 11.05 dB, respectively, compared to existing algorithms. Moreover, sea trial experiments indicate an average output SNR gain exceeding 3.10 dB. The superior systems with higher data rates and more robustness significantly accelerate the development of UWA communication.

关键词： Underwater acoustic communication Orthogonal frequency division multiplexing Impulsive noise and channel estimation Signal-noise separation distributed compressed sensing

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One-bit distributed compressed sensing with partial gaussian circulant matrices

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APPLIED INTELLIGENCE 2025年第8期55卷 1-14页

作者： Leng, Yuke Hou, Jingyao Liu, Xinling Wang, Jianjun Southwest Univ Sch Math & Stat Beibei 400715 Chongqing Peoples R China China West Normal Univ Sch Math & Informat Key Lab Optimizat Theory & Applicat Nanchong 637001 Sichuan Peoples R China

One-bit distributed compressed sensing has been widely used in multi-node networks and many other fields. Conventional approaches often employ random Gaussian measurement matrices, but these unstructured matrices demand significant memory and computational resources. To address this limitation, we propose the use of structured partial Gaussian circulant matrices. This kind of matrix facilitates faster matrix operations and permits low storage, making it more practical. To the best of our knowledge, we are the first to theoretically prove that these matrices satisfy the & ell;1/& ell;2,1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _1/\ell _{2,1}$$\end{document}-RIP in one-bit distributed compressed sensing. We prove that the required number of measurements under partial Gaussian circulant measurements enjoys the same order with that of Gaussian, which, however, is more computational efficient. Furthermore, numerical experiments confirm that partial Gaussian circulant matrices and random Gaussian matrices exhibit comparable reconstruction performance. Additionally, partial Gaussian circulant matrices spend less recovery time, offering higher computational efficiency.

关键词： One-bit quantization distributed compressed sensing Restricted isometry property Joint recovery

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distributed compressed sensing in wireless local area networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2014年第11期27卷 2723-2743页

作者： Yang, Hao Huang, Liusheng Xu, Hongli Liu, An Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Anhu Peoples R China Univ Sci & Technol China Suzhou Inst Adv Study Suzhou Jiangsu Peoples R China

In order to improve energy efficiency in the process of gathering data and transmitting information, compressed sensing (CS) has been applied increasingly in wireless local area networks (WLANs). In traditional usages of CS techniques in previous literatures, the sparsities of the signals have to be known beforehand, which is significant for the recovered results. However, it is difficult to realize precisely the structures of the actual signals in WLANs. Therefore, it is important to further exploit the reasonable practicality of signals in actual applications. In this paper, we present a spatial-temporal correlation model to optimize measure matrix of CS on the basis of a local region. Furthermore, two algorithms based on two kinds of situations are designed, both of which are pervasive data distributions in real environments. Our algorithms have been proven to be valuable and could be considered for actual applications. Experiments show that the signals could be reconstructed accurately and stably even if their sparsities could not be known in advance. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： compressed sensing distributed compressed sensing spatial-temporal correlation local region

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distributed compressed sensing for multi-sourced fusion and secure signal processing in private cloud

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2016年第4期27卷 891-908页

作者： Zhao, Huimin Wei, Wenguo Cai, Jun Lei, Fangyuan Luo, Jianzhen Guangdong Polytech Normal Univ Sch Elect & Informat Guangzhou Guangdong Peoples R China

In this paper, a novel scheme is proposed for multi-sourced signal fusion and secure processing. Within a distributed compressed sensing (DCS) framework, traditional sampling, compression and encryption for signal acquisition are unified under the secure multiparty computation protocol. In the proposed scheme, generation of the pseudo-random sensing matrix offers a natural method for data encryption in DCS, allowing for joint recovery of multiparty data at legal users' side. Experimental analysis and results indicate that the secure signal processing and recovery in DCS domain is feasible, and requires fewer measurements than the achievable approach of separate CS and Nyquist processing. The proposed scheme can be also extended to other cloud-based collaborative secure signal processing and data-mining applications.

关键词： Multi-sourced fusion Secure signal processing distributed compressed sensing Cloud computing Measurement matrix

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distributed compressed sensing based joint detection and tracking for multistatic radar system

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INFORMATION SCIENCES 2016年第0期369卷 100-118页

作者： Liu, Jing Lian, Feng Mallick, Mahendra Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian Peoples R China

In this paper, we present a novel distributed compressed sensing based joint detection and tracking algorithm for multistatic radar system, which significantly reduces the computational load in a centralized fusion framework. For different receivers in the multi static radar system, their corresponding sparse vectors, which are represented in state space, share the same locations of nonzero reflection coefficients. This fits the joint sparsity model 2 (JSM-2) in distributed compressed sensing. In this paper, a novel algorithm, named distributed general similar sensing matrix pursuit (DGSSMP) algorithm, is proposed to tackle the generalized JSM-2 model when each individual sensing matrix is different and with high coherence. In contrast to the classical greedy algorithms dealing with single subspace, the proposed DGSSMP algorithm has to tackle a union of different subspaces, with each subspace corresponding to a different sensing matrix for each individual receiver. The simulation results show that in the proposed distributed compressed sensing based joint detection and tracking framework, the proposed DGSSMP algorithm together with the track before detect (TBD) scheme can effectively distinguish true targets from clutter based on the information from multiple scans. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Multistatic radar system distributed compressed sensing Generalized joint sparsity model 2 distributed general similar sensing Matrix pursuit (DGSSMP) algorithm

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distributed compressed sensing of Hyperspectral Images According to Spectral Library Matching

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IEEE ACCESS 2021年 9卷 112994-113006页

作者： Xiao, Hua Wang, Zhongliang Cui, Xueying Tongling Univ Dept Math & Comp Tongling 244061 Anhui Peoples R China Tongling Univ Dept Elect Engn Tongling 244061 Anhui Peoples R China

The ever-increasing resolution puts tremendous pressure to the onboard hyperspectral imaging system. compressed sensing technology is one of the important ways to solve this problem. distributed compressed sensing was proposed to exploit both intra- and inter-correlation structures of hyperspectral images. However, the implementation method of distributed compressed sampling has not been reported, and the joint sparsity reconstruction algorithm cannot achieve excellent image reconstruction performance. In this paper, a distributed compressed sampling strategy inspired by distributed compressed video sensing and optical implementation model are proposed to collect compressed hyperspectral data. In the image reconstruction process, we discard the direct application of the joint sparsity constraint on the data itself. Instead, we explore the estimation method of abundance and endmember with the help of the existing spectral library. Then, the images are recovered by applying the linear mixing model of hyperspectral. The comparison experiments of various schemes show that the proposed compressed sensing scheme has an obvious advantage in reconstruction performance under the low sampling rate. The proposed compressed sensing scheme has great potential in a high-compression ratio onboard hyperspectral imaging system.

关键词： Image coding Hyperspectral imaging Imaging Image reconstruction compressed sensing Libraries Micromirrors Hyperspectral images distributed compressed sensing spectral unmixing linear mixing model

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distributed compressed sensing via Generalized Approximate Message Passing for Jointly Sparse Signals

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2019年第4期E102A卷 702-707页

作者： Si, Jingjing Cheng, Yinbo Liu, Kai Yanshan Univ Sch Informat Engn Qinhuangdao Hebei Peoples R China Hebei Key Lab Informat Transmiss & Signal Proc Qinhuangdao Hebei Peoples R China Hebei Agr Univ Ocean Coll Baoding Peoples R China

Generalized approximate message passing (GAMP) is introduced into distributed compressed sensing (DCS) to reconstruct jointly sparse signals under the mixed support-set model. A GAMP algorithm with known support-set is presented and the matching pursuit generalized approximate message passing (MPGAMP) algorithm is modified. Then, a new joint recovery algorithm, referred to as the joint MPGAMP algorithm, is proposed. It sets up the jointly shared support-set of the signal ensemble with the support exploration ability of matching pursuit and recovers the signals' amplitudes on the support-set with the good reconstruction performance of GAMP. Numerical investigation shows that the joint MPGAMP algorithm provides performance improvements in DCS reconstruction compared to joint orthogonal matching pursuit, joint look ahead orthogonal matching pursuit and regular MPGAMP.

关键词： distributed compressed sensing generalized approximate message passing joint recovery joint sparsity model

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distributed compressed sensing Aided Sparse Channel Estimation in FDD Massive MIMO System

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IEEE ACCESS 2018年 6卷 18383-18397页

作者： Zhang, Ruoyu Zhao, Honglin Zhang, Jiayan Harbin Inst Technol Commun Res Ctr Harbin 150080 Heilongjiang Peoples R China

Massive multi-input multi-output (MIMO), which employs large number of antennas at the base station, can significantly boost the spectral efficiency and multiplexing gain. To fully exploit the huge array gain, the accurate channel state information is required at the transmitter side. However, the associated training overhead for downlink channel estimation consumes large amount of communication resource, especially for frequency division duplexing massive MIMO system. To address this issue, a distributed compressed sensing (DCS)-aided channel estimation approach is proposed, which fully exploits slow variation of the channel statistics in consecutive frames and spatially common sparsity within multiple subchannels in the frequency domain. Specifically, by exploiting the slow variation of the channel statistics, a hybrid training structure is proposed to probe the channel in the current frame based on the support information in previous frame. Then, a DCS-aided channel estimation algorithm, which combines least square method and DCS method, is proposed to estimate the two parts of channel vector in angular domain among different subcarriers. In addition, to effectively acquire the support information at the beginning of communication, a prior information estimation method is proposed by exploiting the uplink-downlink angular reciprocity. Simulation results demonstrate that the proposed approach outperforms the counterparts and is capable to significantly reduce the training overhead for channel estimation.

关键词： Massive MIMO frequency division duplexing sparse channel estimation distributed compressed sensing

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distributed compressed sensing multi-target positioning method based on wavelet transform

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND distributed SYSTEMS 2021年第2期26卷 198-218页

作者： Zhang, Yuangang He, Fangyuan Liu, Zhenzhen Wang, Xuqi Wang, Wei Informatizat Ctr YanKuang Grp 298 Fushan South Rd Zoucheng 273500 Shandong Peoples R China China Univ Min & Technol Beijing Ding 11 Xueyuan Rd Beijing 100083 Peoples R China China Mobile Hangzhou R&D Ctr 1600 Yuhangtang Rd Hangzhou 311100 Zhejiang Peoples R China China Univ Min & Technol Beijing 11 Xueyuan Rd Beijing 100083 Peoples R China Zhengzhou Power Supply Co State Grid Qinling Rd Zhengzhou 450006 Henan Peoples R China

In the environment of coal mine underground tunnel, it is difficult to establish a unified electromagnetic propagation model because of the factors that multi-path effect and electromagnetic wave propagation loss are numerous and complicated, in addition, the trilateral positioning of electromagnetic wave ranging is very demanding on the hardware system. In order to solve the problems, a multi-target positioning method of distributed compressed sensing based on wavelet transform is proposed, which is on the basis of researching the low power consumption and lightweight design of underground tunnel hardware. The method makes full use of the spatial correlation information of the nodes in the tunnel and transforms the target positioning problem into a joint recovery problem of signal group data. The target positions can be achieved through a non-cooperative manner and a small number of sample values, which effectively reduces the additional energy consumption caused by the exchange of data between nodes. The simulation results show that the proposed method satisfies the application of underground hardware nodes in an energy efficient way.

关键词： distributed compressed sensing DCS wavelet transform joint recovery multi-target positioning Taylor series

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distributed compressed sensing for Block-sparse Signals

Distributed Compressed Sensing for Block-sparse Signals

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22nd IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Wang, Xing Guo, Wenbin Lu, Yang Wang, Wenbo Beijing Univ Posts & Telecommun Wireless Signal Proc & Network Lab Key Lab Univ Wireless Commun Minist Educ Beijing 100876 Peoples R China

ISBN: (纸本)9781457713484

To address the problems of high sampling rates, shadow fading and additive noise from the receiver, in this paper, a distributed compressed sampling (DCS) and centralized reconstruction approach which utilize the spatial diversity against fading channels is proposed. Unlike traditional centralized reconstruction, in this paper, we centralized recover the spectrums by exploiting the block-sparsity which is rather prevalent in multi-band signals. In DCS, first each CR samples the signals with a sub-Nyquist sampling rate independently, then the sampled data are uploaded to the fusion center (FC), finally FC reconstructs these data simultaneously. To exploit the block-sparsity, two new centralized recovery algorithms simultaneous block orthogonal matching pursuit (S-BOMP) and simultaneous binary tree based block orthogonal matching pursuit (S-BTBOMP) are developed. Simulation results show they outperform existing simultaneous recovery algorithms which don't take block-sparsity into consideration.

关键词： Cognitive radio distributed compressed sensing block-sparsity simultaneous recovery

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