The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
A Wavelet Approach to Wideband Spectrum Sensing for Cognitive Radios
Abstract
In cognitive radio networks, the first cognitive task preceding any form of dynamic spectrum management is the sensing and identification of spectrum holes in wireless environments. This paper develops a wavelet approach to efficient spectrum sensing of wideband channels. The signal spectrum over a wide frequency band is decomposed into elementary building blocks of subbands that are well characterized by local irregularities in frequency. As a powerful mathematical tool for analyzing singularities and edges, the wavelet transform is employed to detect and estimate the local spectral irregular structure, which carries important information on the frequency locations and power spectral densities of the subbands. Along this line, a couple of wideband spectrum sensing techniques are developed based on the local maxima of the wavelet transform modulus and the multi-scale wavelet products. The proposed sensing techniques provide an effective radio sensing architecture to identify and locate spectrum holes in the signal spectrum
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
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
Spectrum estimation and harmonic analysis
Thomson, D. J. (1982) · Proceedings of the IEEE · doi:10.1109/proc.1982.12433 · Cited by 4,418
Singularity detection and processing with wavelets
Mallat, S., & Hwang, W. L. (1992) · IEEE Transactions on Information Theory · doi:10.1109/18.119727 · Cited by 3,863
Energy detection of unknown deterministic signals
Urkowitz, H. (1967) · Proceedings of the IEEE · doi:10.1109/proc.1967.5573 · Cited by 3,210
Characterization of signals from multiscale edges
Mallat, S., & Zhong, S. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.142909 · Cited by 3,200
Spectrum agile radios: utilization and sensing architectures
Sai Shankar, N., Cordeiro, C. D. M., & Challapali, K. (2005) · doi:10.1109/dyspan.2005.1542631 · Cited by 384
Analysis of multiscale products for step detection and estimation
Sadler, B. M., & Swami, A. (1999) · IEEE Transactions on Information Theory · doi:10.1109/18.761341 · Cited by 204
Cited by
A survey of spectrum sensing algorithms for cognitive radio applications
Yucek, T., & Arslan, H. (2009) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2009.090109 · Cited by 4,801
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
Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications
Ali, A., & Hamouda, W. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2631080 · Cited by 624
A Survey on Machine-Learning Techniques in Cognitive Radios
Bkassiny, M., Li, Y., & Jayaweera, S. K. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.100412.00017 · Cited by 580
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
Metadata
- Authors
- Tian, Z., & Giannakis, G. B.
- Venue
- 2006 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications
- Publisher
- IEEE
- Published
- 2006-06
- Pages
- 1-5
- OpenAlex
- W2054019480
- Cited by
- 560
- References
- 8
- FWCI
- 15.15
- 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: A Wavelet Approach to Wideband Spectrum Sensing for Cognitive Radios. https://app.cochat.ai/papers/10.1109/crowncom.2006.363459
- The paper
- Tian, Z., & Giannakis, G. B. (2006). A Wavelet Approach to Wideband Spectrum Sensing for Cognitive Radios. 2006 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications. https://doi.org/10.1109/crowncom.2006.363459
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.
