The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Modulation and coding for linear Gaussian channels
Abstract
Shannon's determination of the capacity of the linear Gaussian channel has posed a magnificent challenge to succeeding generations of researchers. This paper surveys how this challenge has been met during the past half century. Orthogonal minimum-bandwidth modulation techniques and channel capacity are discussed. Binary coding techniques for low-signal-to-noise ratio (SNR) channels and nonbinary coding techniques for high-SNR channels are reviewed. Recent developments, which now allow capacity to be approached on any linear Gaussian channel, are surveyed. These new capacity-approaching techniques include turbo coding and decoding, multilevel coding, and combined coding/precoding for intersymbol-interference channels.
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
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Cited by
Capacity Limits of Optical Fiber Networks
Essiambre, R.-J., Kramer, G., Winzer, P. J., Foschini, G. J., & Goebel, B. (2010) · Journal of Lightwave Technology · doi:10.1109/jlt.2009.2039464 · Cited by 2,362
Foundations of MIMO Communication
Heath Jr, R. W., & Lozano, A. (2018) · Cambridge University Press eBooks · doi:10.1017/9781139049276 · Cited by 426
Kramer, G., Marić, I., & Yates, R. D. (2006) · Foundations and Trends® in Networking · doi:10.1561/1300000004 · Cited by 282
Spatial Deep Learning for Wireless Scheduling
Cui, W., Shen, K., & Yu, W. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904352 · Cited by 265
Metadata
- Authors
- Forney, G. D., & Ungerboeck, G.
- Venue
- IEEE Transactions on Information Theory
- Published
- 1998
- Volume
- 44
- Issue
- 6
- Pages
- 2384-2415
- ISSN
- 0018-9448 (ISSN-L)
- OpenAlex
- W2103604544
- Cited by
- 597
- References
- 1
- FWCI
- 7.77
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Modulation and coding for linear Gaussian channels. https://app.cochat.ai/papers/10.1109/18.720542
- The paper
- Forney, G. D., & Ungerboeck, G. (1998). Modulation and coding for linear Gaussian channels. IEEE Transactions on Information Theory. https://doi.org/10.1109/18.720542
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.