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On the energy detection of unknown signals over fading channels

Digham, F.F., Alouini, M.-S., Simon, M.K.·2004·IEEE International Conference on Communications, 2003. ICC '03.

Abstract

This paper presents another look at the problem of energy detection of unknown signals over different fading channels. We start with the no diversity case and present some alternative closed-form expressions for the probability of detection (P/sub d/) to those recently reported in [V.I. Kostylev, May 2002]. We then investigate the system performance when different diversity schemes are employed. It is shown that there is not much improvement in the probability of detection when either the probability of false alarm (P/sub f/) exceeds 0/1 or the average signal-to-noise ratio (SNR) exceeds 20 dB. In addition, receiver operating characteristic (ROC) curves comparing the performance of equal-gain combining (EGC), selection combining (SC), and switch and stay combining (SSC) are presented. As an example, EGC introduces a gain of two orders of magnitude from the probability of miss perspective compared to the no diversity case while both SC and SSC introduce a gain of about one order of magnitude.

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Metadata

Authors
Digham, F. F., Alouini, M.-S., & Simon, M. K.
Venue
IEEE International Conference on Communications, 2003. ICC '03.
Publisher
IEEE
Published
2004-03-30
Volume
5
Pages
3575-3579
ISSN
1550-3607 (ISSN-L)
OpenAlex
W2164701487
Cited by
1,102
References
0
FWCI
5.71
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: On the energy detection of unknown signals over fading channels. https://app.cochat.ai/papers/10.1109/icc.2003.1204119
The paper
Digham, F. F., Alouini, M.-S., & Simon, M. K. (2004). On the energy detection of unknown signals over fading channels. IEEE International Conference on Communications, 2003. ICC '03. https://doi.org/10.1109/icc.2003.1204119

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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.