The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Maximum Eigenvalue Detection: Theory and Application
Abstract
Channel sensing, i.e., detecting the presence of primary users, is a fundamental problem in cognitive radio. Energy detection is optimal for detecting independent and identically distributed (iid) signals, but not optimal for detecting correlated signals. In this paper, a method is proposed based on the sample covariance matrix calculated from a limited number of received signal samples. The maximum eigenvalue of the sample covariance matrix is used as the test statistic. Since the covariance matrix catches the correlations among the signal samples, the proposed method is better than the energy detection for correlated signals. For iid signals, the method approaches to the energy detection. The random matrix theory is used to analyze the method and set the threshold. Similar to energy detection, the methods do not need any information of the source signal and the channel as a priori. Also, no synchronization is needed. Simulations based on wireless microphone signals and iid signals are presented to verify the method.
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
Cognitive radio: brain-empowered wireless communications
Haykin, S. (2005) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2004.839380 · Cited by 11,916
Fundamentals of Statistical Signal Processing: Estimation Theory
Sengijpta, S. K. (1995) · Technometrics · doi:10.1080/00401706.1995.10484391 · Cited by 11,076
Cognitive radio: making software radios more personal
Mitola, J., & Maguire, G. Q. (1999) · IEEE Personal Communications · doi:10.1109/98.788210 · Cited by 9,135
Energy detection of unknown deterministic signals
Urkowitz, H. (1967) · Proceedings of the IEEE · doi:10.1109/proc.1967.5573 · Cited by 3,210
Random Matrix Theory and Wireless Communications
Tulino, A. M., & Verdú, S. (2004) · Foundations and Trends™ in Communications and Information Theory · doi:10.1561/0100000001 · Cited by 2,034
On the distribution of the largest eigenvalue in principal components analysis
Johnstone, I. M. (2001) · The Annals of Statistics · doi:10.1214/aos/1009210544 · Cited by 1,949
On orthogonal and symplectic matrix ensembles
Tracy, C. A., & Widom, H. (1996) · Communications in Mathematical Physics · doi:10.1007/bf02099545 · Cited by 786
On Orthogonal and Symplectic Matrix Ensembles
Tracy, C. A., & Widom, H. (1995) · Cited by 728
METHODOLOGIES IN SPECTRAL ANALYSIS OF LARGE DIMENSIONAL RANDOM MATRICES, A REVIEW
Bai, Z. D. (2008) · Advances in Statistics · doi:10.1142/9789812793096_0015 · Cited by 627
Spectral Correlation of Modulated Signals: Part II--Digital Modulation
Gardner, W., Brown, W., & Chen, C.-K. (1987) · IEEE Transactions on Communications · doi:10.1109/tcom.1987.1096816 · Cited by 408
Cited by
Cooperative Communications for Cognitive Radio Networks
Ben Letaief, K., & Zhang, W. (2009) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2015716 · Cited by 1,153
Signal Processing in Cognitive Radio
Ma, J., Li, G. Y., & Juang, B. H. (2009) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2015707 · Cited by 533
Cognitive Radio Techniques Under Practical Imperfections: A Survey
Sharma, S. K., Bogale, T. E., Chatzinotas, S., Ottersten, B., Le, L. B., & Wang, X. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2452414 · Cited by 221
Metadata
- Authors
- Zeng, Y., Koh, C. L., & Liang, Y.-C.
- Venue
- 2008 IEEE International Conference on Communications
- Publisher
- IEEE
- Published
- 2008
- Pages
- 4160-4164
- ISSN
- 1550-3607 (ISSN-L)
- OpenAlex
- W2110704086
- Cited by
- 324
- References
- 11
- FWCI
- 10.31
- Type
- conference paper
- 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: Maximum Eigenvalue Detection: Theory and Application. https://app.cochat.ai/papers/10.1109/icc.2008.781
- The paper
- Zeng, Y., Koh, C. L., & Liang, Y.-C. (2008). Maximum Eigenvalue Detection: Theory and Application. 2008 IEEE International Conference on Communications. https://doi.org/10.1109/icc.2008.781
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.
