Liangliang Li, Huaguo Zhang, Songmao Du, Tao Liang, Lin Gao
{"title":"多径信道下异步多用户直接序列扩频信号的盲解扩和反褶积","authors":"Liangliang Li, Huaguo Zhang, Songmao Du, Tao Liang, Lin Gao","doi":"10.1049/sil2.12220","DOIUrl":null,"url":null,"abstract":"<p>In non-cooperative scenarios, the spreading sequences or waveforms of the direct sequence spread spectrum (DSSS) signals is unknown to the receiver. This paper focuses on addressing the problem of blind estimation of the spreading waveform under multipath channels. In the scenario of direct signal path transmission, the spreading sequences can be directly obtained based on the estimated spreading waveforms. However, in the presence of multipath channels, the spreading waveform becomes the convolution of the spreading sequence and channel response, thus deconvolution should also be performed after estimating the spreading waveforms. In order to perform blind despreading and deconvolution of asynchronous multiuser DSSS signals under multipath channels, the authors propose to exploit the finite symbol characteristics of information and spreading sequences and then the iterative least square with projection method is adopted. Besides, the Cramer-Rao bound of spreading waveforms is derived in such a circumstance as a performance benchmark. The effectiveness of the proposed method is verified via simulation experiments.</p>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"17 5","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2.12220","citationCount":"2","resultStr":"{\"title\":\"Blind despreading and deconvolution of asynchronous multiuser direct sequence spread spectrum signals under multipath channels\",\"authors\":\"Liangliang Li, Huaguo Zhang, Songmao Du, Tao Liang, Lin Gao\",\"doi\":\"10.1049/sil2.12220\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In non-cooperative scenarios, the spreading sequences or waveforms of the direct sequence spread spectrum (DSSS) signals is unknown to the receiver. This paper focuses on addressing the problem of blind estimation of the spreading waveform under multipath channels. In the scenario of direct signal path transmission, the spreading sequences can be directly obtained based on the estimated spreading waveforms. However, in the presence of multipath channels, the spreading waveform becomes the convolution of the spreading sequence and channel response, thus deconvolution should also be performed after estimating the spreading waveforms. In order to perform blind despreading and deconvolution of asynchronous multiuser DSSS signals under multipath channels, the authors propose to exploit the finite symbol characteristics of information and spreading sequences and then the iterative least square with projection method is adopted. Besides, the Cramer-Rao bound of spreading waveforms is derived in such a circumstance as a performance benchmark. The effectiveness of the proposed method is verified via simulation experiments.</p>\",\"PeriodicalId\":56301,\"journal\":{\"name\":\"IET Signal Processing\",\"volume\":\"17 5\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2.12220\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/sil2.12220\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/sil2.12220","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Blind despreading and deconvolution of asynchronous multiuser direct sequence spread spectrum signals under multipath channels
In non-cooperative scenarios, the spreading sequences or waveforms of the direct sequence spread spectrum (DSSS) signals is unknown to the receiver. This paper focuses on addressing the problem of blind estimation of the spreading waveform under multipath channels. In the scenario of direct signal path transmission, the spreading sequences can be directly obtained based on the estimated spreading waveforms. However, in the presence of multipath channels, the spreading waveform becomes the convolution of the spreading sequence and channel response, thus deconvolution should also be performed after estimating the spreading waveforms. In order to perform blind despreading and deconvolution of asynchronous multiuser DSSS signals under multipath channels, the authors propose to exploit the finite symbol characteristics of information and spreading sequences and then the iterative least square with projection method is adopted. Besides, the Cramer-Rao bound of spreading waveforms is derived in such a circumstance as a performance benchmark. The effectiveness of the proposed method is verified via simulation experiments.
期刊介绍:
IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more.
Topics covered by scope include, but are not limited to:
advances in single and multi-dimensional filter design and implementation
linear and nonlinear, fixed and adaptive digital filters and multirate filter banks
statistical signal processing techniques and analysis
classical, parametric and higher order spectral analysis
signal transformation and compression techniques, including time-frequency analysis
system modelling and adaptive identification techniques
machine learning based approaches to signal processing
Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques
theory and application of blind and semi-blind signal separation techniques
signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals
direction-finding and beamforming techniques for audio and electromagnetic signals
analysis techniques for biomedical signals
baseband signal processing techniques for transmission and reception of communication signals
signal processing techniques for data hiding and audio watermarking
sparse signal processing and compressive sensing
Special Issue Call for Papers:
Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf