关于倾斜对称二进制序列和优点因子问题

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Miroslav Dimitrov
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引用次数: 0

摘要

绩因问题对数字通信工程、雷达、系统调制、系统测试、信息论、物理学、化学等多个领域都具有重要的实际意义。本研究提出了一些与偏斜对称二进制序列的翻转操作相关的有用数学特性。利用这些特性,最先进的随机优点因子优化算法的空间复杂度可从 O(n2) 降至 O(n)。作为概念验证,我们构建了一种轻量级随机算法,它可以将伪随机生成的长度较长(达 105+1)的偏斜对称二进制序列优化为优点因子大于 5 的偏斜对称二进制序列。同时还提供了所需时间的近似值。数值实验表明,该算法具有通用性,可用于任意长度的偏斜对称二进制序列。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the skew-symmetric binary sequences and the merit factor problem
The merit factor problem is of practical importance to manifold domains, such as digital communications engineering, radars, system modulation, system testing, information theory, physics, chemistry. In this work, some useful mathematical properties related to the flip operation of the skew-symmetric binary sequences are presented. By exploiting those properties, the space complexity of state-of-the-art stochastic merit factor optimization algorithms could be reduced from O(n2) to O(n). As a proof of concept, a lightweight stochastic algorithm was constructed, which can optimize pseudo-randomly generated skew-symmetric binary sequences with long lengths (up to 105+1) to skew-symmetric binary sequences with a merit factor greater than 5. An approximation of the required time is also provided. The numerical experiments suggest that the algorithm is universal and could be applied to skew-symmetric binary sequences with arbitrary lengths.
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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