The DOI matches its Crossref record.
Channel Codes: Classical and Modern
Abstract
Channel coding lies at the heart of digital communication and data storage, and this detailed introduction describes the core theory as well as decoding algorithms, implementation details, and performance analyses. In this book, Professors Ryan and Lin provide clear information on modern channel codes, including turbo and low-density parity-check (LDPC) codes. They also present detailed coverage of BCH codes, Reed-Solomon codes, convolutional codes, finite geometry codes, and product codes, providing a one-stop resource for both classical and modern coding techniques. Assuming no prior knowledge in the field of channel coding, the opening chapters begin with basic theory to introduce newcomers to the subject. Later chapters then extend to advanced topics such as code ensemble performance analyses and algebraic code design. 250 varied and stimulating end-of-chapter problems are also included to test and enhance learning, making this an essential resource for students and practitioners alike.
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
“Short-Dot”: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products
Dutta, S., Cadambe, V., & Grover, P. (2019) · IEEE Transactions on Information Theory · doi:10.1109/tit.2019.2927558 · Cited by 313
Data retention in MLC NAND flash memory: Characterization, optimization, and recovery
Cai, Y., Luo, Y., Haratsch, E. F., Mai, K., & Mutlu, O. (2015) · 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA) · doi:10.1109/hpca.2015.7056062 · Cited by 274
Data storage using peptide sequences
Ng, C. C. A., Tam, W. M., Yin, H., Wu, Q., So, P.-K., Wong, M. Y.-M., Lau, F. C. M., & Yao, Z.-P. (2021) · Nature Communications · doi:10.1038/s41467-021-24496-9 · Cited by 41
Channel Coding for DTTB System
(2015) · Digital Terrestrial Television Broadcasting · doi:10.1002/9781119084365.ch03
Metadata
- Authors
- Ryan, W., & Lin, S.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2009-09-17
- OpenAlex
- W4256264271
- Cited by
- 778
- References
- 0
- FWCI
- 18.11
- Type
- book
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Channel Codes: Classical and Modern. https://app.cochat.ai/papers/10.1017/cbo9780511803253
- The paper
- Ryan, W., & Lin, S. (2009). Channel Codes: Classical and Modern. Cambridge University Press eBooks. https://doi.org/10.1017/cbo9780511803253
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.