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Paper Title
Machine Learning Techniques for improving Spectrum Sensing in Satellite Communication System: Review
Authors
Sakshi sirohiya , Amit Baghel
Keywords
Terms such as the Maximum a posteriori (MAP), higher order moments (HOM), hypothesis testing, false alarm probability, probability of detection have been used to describe spectrum sensing and cognitive satellite communication.
Abstract
Several spectrum sensing detection strategies
have been proposed in recent years, including special range
detection calculations based on the maximum a posterior
(MAP) rule, energy detection, and others. Spectrum sensing
plays an important role in enabling cognitive radio (CR)
technologies for the next generation of wireless
communication systems. All of these methods require
setting thresholds, as well as prior knowledge of the noise
distribution. Cooperative spectrum sensing is used to
improve the sensing performance. When GEO
(geostationary) and NGEO (non-geostationary) resultant
systems coexist on the same recurrence, the nongeostationary system should not create a harmful barrier to
the GEO. Several sensing methods have been proposed in
recent years, including specific spectrum sensing
algorithms based on the maximum a posterior (MAP) rule,
energy detection, and others. The machine learning
calculations in this paper are the most advanced for
spectrum detection. With 2000 dataset measurements, we
can also calculate location probability, false alarm
probability, response time, miss detection, throughput and
accuracy.
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How To Cite
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IJRTI — journal style
"Machine Learning Techniques for improving Spectrum Sensing in Satellite Communication System: Review", IJRTI - International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.7, Issue 3, page no.73 - 76, March-2022, Available :https://ijrti.org/papers/IJRTI2203013.pdf
APA — 7th edition
sirohiya, S., & Baghel, A. (2022). Machine Learning Techniques for improving Spectrum Sensing in Satellite Communication System: Review. International Journal for Research Trends and Innovation, 7(3), 73 - 76. https://ijrti.org/viewpaperforall.php?paper=IJRTI2203013
MLA — 9th edition
sirohiya, Sakshi, and Amit Baghel. "Machine Learning Techniques for improving Spectrum Sensing in Satellite Communication System: Review." International Journal for Research Trends and Innovation, vol. 7, no. 3, 2022, pp. 73 - 76, https://ijrti.org/viewpaperforall.php?paper=IJRTI2203013.
Chicago — 17th, bibliography
sirohiya, Sakshi, and Amit Baghel. "Machine Learning Techniques for improving Spectrum Sensing in Satellite Communication System: Review." International Journal for Research Trends and Innovation 7, no. 3 (2022): 73 - 76. https://ijrti.org/viewpaperforall.php?paper=IJRTI2203013.
Harvard — author–date
sirohiya, S. and Baghel, A. (2022) 'Machine Learning Techniques for improving Spectrum Sensing in Satellite Communication System: Review', International Journal for Research Trends and Innovation, 7(3), pp. 73 - 76. Available at: https://ijrti.org/viewpaperforall.php?paper=IJRTI2203013
IEEE — numbered reference
S. sirohiya and A. Baghel, "Machine Learning Techniques for improving Spectrum Sensing in Satellite Communication System: Review," IJRTI, vol. 7, no. 3, pp. 73 - 76, Mar. 2022.
Vancouver — biomedical
sirohiya S, Baghel A. Machine Learning Techniques for improving Spectrum Sensing in Satellite Communication System: Review. IJRTI. 2022 Mar;7(3):73 - 76.
AMA — 11th edition
sirohiya S, Baghel A. Machine Learning Techniques for improving Spectrum Sensing in Satellite Communication System: Review. IJRTI. 2022;7(3):73 - 76. Accessed at https://ijrti.org/viewpaperforall.php?paper=IJRTI2203013
BibTeX — Zotero, Mendeley, LaTeX
@article{IJRTI2203013,
author = {Sakshi sirohiya and Amit Baghel},
title = {Machine Learning Techniques for improving Spectrum Sensing in Satellite Communication System: Review},
journal = {International Journal for Research Trends and Innovation},
volume = {7},
number = {3},
pages = {73 - 76},
year = {2022},
month = {March},
issn = {2456-3315},
url = {https://ijrti.org/viewpaperforall.php?paper=IJRTI2203013}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - sirohiya, Sakshi
AU - Baghel, Amit
TI - Machine Learning Techniques for improving Spectrum Sensing in Satellite Communication System: Review
T2 - International Journal for Research Trends and Innovation
JA - IJRTI
VL - 7
IS - 3
PY - 2022
SN - 2456-3315
UR - https://ijrti.org/viewpaperforall.php?paper=IJRTI2203013
SP - 73
EP - 76
ER -
Issue
Volume 7 Issue 3,
March-2022
Pages : 73 - 76
Other Publication Details
Paper Reg. ID
IJRTI_181731
Published Paper ID
IJRTI2203013
Downloads
205,630
Research Area
Electronics & Communication Engg.Â
Country
Sagar, Madhya Pradesh, India
Published
March 2022
Published Paper PDF
https://ijrti.org/papers/IJRTI2203013.pdf
Published Paper URL
https://ijrti.org/viewpaperforall.php?paper=IJRTI2203013
About Publisher
International Journal for Research Trends and Innovation
Published by IJRTI (JW Publication)
2456-3315
ISSN
10.57
Impact Factor
2016
ESTD Year
Open
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Impact Factor 10.57 calculated by Google Scholar and Semantic Scholar.
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The content, data and findings in this article are based on the authors’ research and have been
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Declarations
Funding
No external funding was received for this study.
Conflict of Interest
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank the reviewers and the editorial board of International Journal for Research Trends and Innovation for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.
