| Title: | Dual-step optimization for binary sequences with high merit factors |
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| Authors: | ID Pšeničnik, Blaž (Author)
ID Mlinarič, Rene (Author)
ID Brest, Janez (Author)
ID Bošković, Borko (Author) |
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| Files: |  1-s2.0-S1051200425003380-main.pdf (968,30 KB)
MD5: 4B68C3C58CA1C5BA79CA17FC2C20134C
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | The problem of finding aperiodic low auto-correlation binary sequences (LABS) presents a significant computational challenge, particularly as the sequence length increases. Such sequences have important applications in communication engineering, physics, chemistry, and cryptography. This paper introduces a dual-step algorithm for long binary sequences with high merit factors. The first step employs a parallel algorithm utilizing skew-symmetry and restriction classes to generate sequence candidates with merit factors above a predefined threshold. The second step uses a priority queue algorithm to refine these candidates further, searching the entire search space unrestrictedly. By combining GPU-based parallel computing and dual-step optimization, our approach has successfully identified best-known binary sequences for all lengths ranging from 450 to 527, with the exception of length 518, where the previous best-known merit factor value was matched with a different sequence. This hybrid method significantly outperforms traditional exhaustive and stochastic search methods, offering an efficient solution for finding long sequences with good merit factors. |
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| Keywords: | binary sequences, Golay's merit factor, autocorrelation, algorithms |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 13.05.2025 |
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| Publisher: | Elsevier Inc. |
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| Year of publishing: | 2025 |
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| Number of pages: | 9 str. |
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| Numbering: | Vol. 165, [article no.] 105316 |
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| PID: | 20.500.12556/DKUM-93000  |
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| UDC: | 004 |
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| ISSN on article: | 1095-4333 |
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| COBISS.SI-ID: | 236425219 |
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| DOI: | 10.1016/j.dsp.2025.105316 |
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| Copyright: | © 2025 The Author(s) |
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| Publication date in DKUM: | 30.05.2025 |
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| Views: | 240 |
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| Downloads: | 25 |
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| Metadata: |    |
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| Categories: | Misc.
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