The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Spectrum-Sensing Algorithms for Cognitive Radio Based on Statistical Covariances
Open accessAbstract
Spectrum sensing, i.e., detecting the presence of primary users in a licensed spectrum, is a fundamental problem in cognitive radio. Since the statistical covariances of the received signal and noise are usually different, they can be used to differentiate the case where the primary user's signal is present from the case where there is only noise. In this paper, spectrum-sensing algorithms are proposed based on the sample covariance matrix calculated from a limited number of received signal samples. Two test statistics are then extracted from the sample covariance matrix. A decision on the signal presence is made by comparing the two test statistics. Theoretical analysis for the proposed algorithms is given. Detection probability and the associated threshold are found based on the statistical theory. The methods do not need any information about the signal, channel, and noise powerapriori. In addition, no synchronization is needed. Simulations based on narrow-band signals, captured digital television (DTV) signals, and multiple antenna signals are presented to verify the methods.
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
Sensing-Throughput Tradeoff for Cognitive Radio Networks
Liang, Y.-C., Zeng, Y., Peh, E. C. Y., & Hoang, A. T. (2008) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2008.060869 · Cited by 2,974
Self-citationCooperative Sensing among Cognitive Radios
Mishra, S. M., Sahai, A., & Brodersen, R. W. (2006) · International Conference on Communications · doi:10.1109/icc.2006.254957 · Cited by 1,452
Fundamental limits on detection in low SNR under noise uncertainty
Tandra, R., & Sahai, A. (2005) · 2005 International Conference on Wireless Networks, Communications and Mobile Computing · doi:10.1109/wirles.2005.1549453 · Cited by 602
A Cognitive Radio Approach for Usage of Virtual Unlicensed Spectrum
Mishra, S. M., Willkomm, D., Brodersen, R. W., & Adam Wolisz, A. (2005) · Cited by 571
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
Signature Based Spectrum Sensing Algorithms for IEEE 802.22 WRAN
Chen, H.-S., Gao, W., & Daut, D. G. (2007) · 2007 IEEE International Conference on Communications · doi:10.1109/icc.2007.1073 · Cited by 207
Cited by
Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances
Axell, E., Leus, G., Larsson, E. G., & Poor, H. V. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2183771 · Cited by 1,116
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., & Liang, Y.-C.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2009-05
- Volume
- 58
- Issue
- 4
- Pages
- 1804-1815
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2119137700
- Cited by
- 639
- References
- 10
- FWCI
- 44.44
- Type
- journal article
- Language
- en
- Open access
- green
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: Spectrum-Sensing Algorithms for Cognitive Radio Based on Statistical Covariances. https://app.cochat.ai/papers/10.1109/tvt.2008.2005267
- The paper
- Zeng, Y., & Liang, Y.-C. (2009). Spectrum-Sensing Algorithms for Cognitive Radio Based on Statistical Covariances. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2008.2005267
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.
