The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Near Shannon limit performance of low density paritycheck codes
Abstract
The authors report the empirical performance of Gallager's low density parity check codes on Gaussian channels. It is shown that performance substantially better than that of standard convolutional and concatenated codes can be achieved; indeed the performance is almost as close to the Shannon limit as that of Turbo codes.
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
Cites
Low-Density Parity-Check Codes
Gallager, R. G. (1963) · The MIT Press eBooks · doi:10.7551/mitpress/4347.001.0001 · Cited by 4,401
Good error-correcting codes based on very sparse matrices
MacKay, D. J. C. (1999) · IEEE Transactions on Information Theory · doi:10.1109/18.748992 · Cited by 3,684
Self-citation(1988) · doi:10.1016/b978-0-08-051489-5.50008-4 · Cited by 1,429
Good codes based on very sparse matrices
MacKay, D. J. C., & Neal, R. M. (1995) · Lecture Notes in Computer Science · doi:10.1007/3-540-60693-9_13 · Cited by 451
Self-citation
Cited by
Secure quantum key distribution with realistic devices
Xu, F., Ma, X., Zhang, Q., Lo, H.-K., & Pan, J.-W. (2020) · Reviews of Modern Physics · doi:10.1103/revmodphys.92.025002 · Cited by 1,543
On the design of low-density parity-check codes within 0.0045 dB of the Shannon limit
Chung, S.-Y., Forney, G. D., Richardson, T. J., & Urbanke, R. (2001) · IEEE Communications Letters · doi:10.1109/4234.905935 · Cited by 1,539
Quantum Low-Density Parity-Check Codes
Breuckmann, N. P., & Eberhardt, J. N. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.040101 · Cited by 328
LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks
Jiang, Y., Kim, H., Asnani, H., Kannan, S., Oh, S., & Viswanath, P. (2020) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2020.2988577 · Cited by 87
Improved read/write cost tradeoff in DNA-based data storage using LDPC codes
Chandak, S., Tatwawadi, K., Lau, B., Mardia, J., Kubit, M., Neu, J., Griffin, P., Wootters, M., Weissman, T., & Ji, H. (2019) · 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton) · doi:10.1109/allerton.2019.8919890 · Cited by 80
Metadata
- Authors
- MacKay, D. J. C., & Neal, R. M.
- Venue
- Electronics Letters
- Published
- 1996-08-29
- Volume
- 32
- Issue
- 18
- Pages
- 1645-1646
- ISSN
- 0013-5194 (ISSN-L)
- 1350-911X
- OpenAlex
- W4256648168
- Cited by
- 1,923
- References
- 4
- FWCI
- 8.69
- 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.