Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 2,589

The DOI matches its Crossref record.

Implementation issues in spectrum sensing for cognitive radios

Cabric, D., Mishra, S.M., Brodersen, R.W.·2005·Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.

Abstract

There are new system implementation challenges involved in the design of cognitive radios, which have both the ability to sense the spectral environment and the flexibility to adapt transmission parameters to maximize system capacity while coexisting with legacy wireless networks. The critical design problem is the need to process multigigahertz wide bandwidth and reliably detect presence of primary users. This places severe requirements on sensitivity, linearity and dynamic range of the circuitry in the RF front-end. To improve radio sensitivity of the sensing function through processing gain we investigated three digital signal processing techniques: matched filtering, energy detection and cyclostationary feature detection. Our analysis shows that cyclostationary feature detection has advantages due to its ability to differentiate modulated signals, interference and noise in low signal to noise ratios. In addition, to further improve the sensing reliability, the advantage of a MAC protocol that exploits cooperation among many cognitive users is investigated.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/acssc.2004.1399240

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.

A few free checks a day · papers already verified open instantly · results are public

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

Cited by

See all 2,589

Metadata

Authors
Cabric, D., Mishra, S. M., & Brodersen, R. W.
Venue
Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.
Publisher
IEEE
Published
2005-03-31
Volume
1
Pages
772-776
ISSN
1058-6393 (ISSN-L)
OpenAlex
W2170524206
Cited by
2,589
References
0
FWCI
64.48
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: Implementation issues in spectrum sensing for cognitive radios. https://app.cochat.ai/papers/10.1109/acssc.2004.1399240
The paper
Cabric, D., Mishra, S. M., & Brodersen, R. W. (2005). Implementation issues in spectrum sensing for cognitive radios. Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004. https://doi.org/10.1109/acssc.2004.1399240

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.