Effect of spectrum occupancy on the performance of a real valued neural network based energy detector
In this paper, a newly proposed Real Valued Neural Network (RVNN) based Energy Detector (ED) is presented for Cognitive Radio (CR) application. With little available on the performance of EDs in varying spectrum occupancy conditions, we provide a study to understand how occupancy variation affec...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/38900/1/momoh2.pdf http://irep.iium.edu.my/38900/ http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6889586 |
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| Summary: | In this paper, a newly proposed Real Valued Neural
Network (RVNN) based Energy Detector (ED) is presented for
Cognitive Radio (CR) application. With little available on the performance of EDs in varying spectrum occupancy conditions, we provide a study to understand how occupancy variation affects the performance of a newly proposed RVNN based ED and other ED schemes. Other factors such as varying Signal to Noise Ratio (SNR) and model order values were also examined in this study and result analysis conducted using the Precision-detection statistics. Implication of results obtained indicate that
the RVNN based ED would perform optimum in high
occupancy and SNR conditions for a model order choice of
P = 20 . We also observed that the RVNN based ED would
provide better precision performance characteristics over the Periodogram, Welch and Multitaper based ED schemes
compared herein. Hence, the RVNN based ED suffices as a
favourable choice for CR application even under varying
occupancy conditions. |
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