{"title":"High performance bit-activation code index modulation method","authors":"Fang Liu, Yuanfang Zheng, Yongxin Feng","doi":"10.1049/sil2.12202","DOIUrl":null,"url":null,"abstract":"<p>With the increasing demand of applications for the spread spectrum technique, especially the demand for data transmission rates and spectral efficiency, the advantages of the traditional direct sequence spread spectrum (DSSS) system are limited. Therefore, multi-ary spread spectrum (M-ary) technology, parallel combinatory spread spectrum (PCSS) technology, and code index modulation (CIM) technology have been proposed. Although these three new technologies can improve the data rate, they all face the problem of the large consumption of pseudo-code resources. In order to solve the problem of pseudo-code resources, a bit-activation code index modulation (BA-CIM) method is proposed. At the transmitter, considering the good correlation among multiple pseudo-codes, the corresponding pseudo-code activation principle is established, and the corresponding spreading pseudo-code is activated by using the status of each bit of the index data according to the pseudo-code activation principle. Then, multicode superposition processing is carried out to spread the modulation data. At the receiver, the corresponding activation pseudo-code is obtained using the maximum peak-to-average ratio (MPAR) and secondary peak-to-average ratio (SPAR) judgement mechanisms to decode the multibit index data. Compared with existing methods, the proposed BA-CIM method can not only achieve a better bit error rate performance but also use the least pseudo-code resources. Moreover, BA-CIM has the best comprehensive performance improvement and is far superior to other methods. This research can provide technical support for the application of efficient spread spectrum communication.</p>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"17 4","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2.12202","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/sil2.12202","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}
引用次数: 0
Abstract
With the increasing demand of applications for the spread spectrum technique, especially the demand for data transmission rates and spectral efficiency, the advantages of the traditional direct sequence spread spectrum (DSSS) system are limited. Therefore, multi-ary spread spectrum (M-ary) technology, parallel combinatory spread spectrum (PCSS) technology, and code index modulation (CIM) technology have been proposed. Although these three new technologies can improve the data rate, they all face the problem of the large consumption of pseudo-code resources. In order to solve the problem of pseudo-code resources, a bit-activation code index modulation (BA-CIM) method is proposed. At the transmitter, considering the good correlation among multiple pseudo-codes, the corresponding pseudo-code activation principle is established, and the corresponding spreading pseudo-code is activated by using the status of each bit of the index data according to the pseudo-code activation principle. Then, multicode superposition processing is carried out to spread the modulation data. At the receiver, the corresponding activation pseudo-code is obtained using the maximum peak-to-average ratio (MPAR) and secondary peak-to-average ratio (SPAR) judgement mechanisms to decode the multibit index data. Compared with existing methods, the proposed BA-CIM method can not only achieve a better bit error rate performance but also use the least pseudo-code resources. Moreover, BA-CIM has the best comprehensive performance improvement and is far superior to other methods. This research can provide technical support for the application of efficient spread spectrum communication.
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