高性能比特激活码索引调制方法

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC IET Signal Processing Pub Date : 2023-04-18 DOI:10.1049/sil2.12202
Fang Liu, Yuanfang Zheng, Yongxin Feng
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引用次数: 0

摘要

随着扩频技术应用需求的增加,特别是对数据传输速率和频谱效率的需求,传统的直接序列扩频(DSSS)系统的优势受到限制。因此,提出了多进制扩频(M-ary)技术、并行组合扩频(PCSS)技术和码索引调制(CIM)技术。尽管这三种新技术可以提高数据速率,但它们都面临着伪代码资源消耗大的问题。为了解决伪码资源的问题,提出了一种比特激活码索引调制(BA-CIM)方法。在发射机处,考虑到多个伪码之间的良好相关性,建立了相应的伪码激活原理,并根据伪码激活原则利用索引数据的每个比特的状态来激活相应的扩展伪码。然后,执行多码叠加处理以扩展调制数据。在接收机处,使用最大峰均比(MPAR)和二次峰均比判断机制来获得相应的激活伪码,以解码多位索引数据。与现有方法相比,所提出的BA-CIM方法不仅可以获得更好的误码率性能,而且可以使用最少的伪码资源。此外,BA-CIM具有最好的综合性能改进,并且远优于其他方法。本研究可为高效扩频通信的应用提供技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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High performance bit-activation code index modulation method

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.

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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
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
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