The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Spectral efficiency of CDMA with random spreading
Abstract
The CDMA channel with randomly and independently chosen spreading sequences accurately models the situation where pseudonoise sequences span many symbol periods. Furthermore, its analysis provides a comparison baseline for CDMA channels with deterministic signature waveforms spanning one symbol period. We analyze the spectral efficiency (total capacity per chip) as a function of the number of users, spreading gain, and signal-to-noise ratio, and we quantify the loss in efficiency relative to an optimally chosen set of signature sequences and relative to multiaccess with no spreading. White Gaussian background noise and equal-power synchronous users are assumed. The following receivers are analyzed: (a) optimal joint processing, (b) single-user matched filtering, (c) decorrelation, and (d) MMSE linear processing.
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
Interleave division multiple-access
Ping, L., Liu, L., Wu, K., & Leung, W. K. (2006) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2006.1618943 · Cited by 912
Foundations of MIMO Communication
Heath Jr, R. W., & Lozano, A. (2018) · Cambridge University Press eBooks · doi:10.1017/9781139049276 · Cited by 426
Network planning with deep reinforcement learning
Zhu, H., Gupta, V., Ahuja, S. S., Tian, Y., Zhang, Y., & Jin, X. (2021) · Proceedings of the 2021 ACM SIGCOMM 2021 Conference · doi:10.1145/3452296.3472902 · Cited by 112
Metadata
- Authors
- Verdu, S., & Shamai, S.
- Venue
- IEEE Transactions on Information Theory
- Published
- 1999-03
- Volume
- 45
- Issue
- 2
- Pages
- 622-640
- ISSN
- 0018-9448 (ISSN-L)
- OpenAlex
- W2136979582
- Cited by
- 885
- References
- 1
- FWCI
- 62.41
- 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: Spectral efficiency of CDMA with random spreading. https://app.cochat.ai/papers/10.1109/18.749007
- The paper
- Verdu, S., & Shamai, S. (1999). Spectral efficiency of CDMA with random spreading. IEEE Transactions on Information Theory. https://doi.org/10.1109/18.749007
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.
