The DOI matches its Crossref record.
Sieves for low autocorrelation binary sequences
Abstract
The value2e^{2}is calculated by approximations as a conjectured asymptotic limit for the best central to sidelobe energy ratio of very long low autocorrelation binary sequences. The same bound is calculated by approximation for skewsymmetric sequences, hence it is concluded that the requirement of skewsymmetry constitutes a powerful and effective sieve for the search for near-optimal sequences. A second sieve based on the use of complementary sequences is discussed and shown to be quite effective for sequence lengths up to 31, and its investigation for longer lengths by means of computers is suggested. A third and a fourth sieve, based on the selection of restricted classes of complementary sequences, are also discussed.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Optimization Methods for Designing Sequences With Low Autocorrelation Sidelobes
Song, J., Babu, P., & Palomar, D. P. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2425808 · Cited by 382
Shaydulin, R., Li, C., Chakrabarti, S., DeCross, M., Herman, D., Kumar, N., Larson, J., Lykov, D., Minssen, P., Sun, Y., et al. (2024) · Science Advances · doi:10.1126/sciadv.adm6761 · Cited by 120
“(14) In LABS, the energy E sidelobe ( s ) grows as N 2 , so γ ∗ decreases as 1 N .”
MethodFinding low autocorrelation binary sequences with memetic algorithms
Gallardo, J. E., Cotta, C., & Fernández, A. J. (2009) · Applied Soft Computing · doi:10.1016/j.asoc.2009.03.005 · Cited by 59
Bošković, B., Brglez, F., & Brest, J. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.02.024 · Cited by 31
Metadata
- Authors
- Golay, M.
- Venue
- IEEE Transactions on Information Theory
- Published
- 1977-01
- Volume
- 23
- Issue
- 1
- Pages
- 43-51
- ISSN
- 0018-9448 (ISSN-L)
- 1557-9654
- OpenAlex
- W2123547620
- Cited by
- 225
- References
- 0
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
