International Core Journal of Engineering 2020-26 | Page 165

analysis of detectors in different fading channels is also our
devoted goal. These problems need our further study.
A CKNOWLEDGEMENT
This work was supported in part by the National Natural
Science Foundation of China under Grant 61901408, in part
by the Natural Science Foundation of Jiangsu Province under
Grant BK20170512, in part by the Universities Natural
Science Research Project of Jiangsu Province under Grant
17KJB413003.
R EFERENCES
[1]
[2]
Fig. 5. Theoretical and Simulated for the ADT-gain
[3]
Fig. 5 shows the comparison of the average detection time
of CED and 3EED when N =500, 1000, 2000 samples.
Obviously, when the sample size is small, the performance
improvement is more obvious. As we all know, when there are
more observation samples, the signal is easier to detect. This
explains why the performance gain is smaller when N is larger.
Here we only talk about the SNR of interval [-13dB, -5dB],
because the detection probability is higher than 0.3 in this case,
the ADT gain in all processing cases is higher than 1. The
5IED method is strongly superior to CED, to allow faster
detection of PU signals. From the comparison of ADT, we
propose that the 5IED detection method has about 11% gain at
-9dB SNR, and even more for smaller SNR gain.
[4]
[5]
[6]
[7]
However, when the SNR is higher than -7dB, the proposed
method is almost the same as other detection methods. Finally,
as expected, the proposed method is suitable for signal
detection in low SNR scenarios. As shown in Fig. 5, the
simulated and theoretical curves do not overlap completely
according to (16) they are evaluated by the detection
probability values in Fig. 2. Therefore, the small gain in Fig. 2
reflects the major enhancement in the ADT gain in Fig. 5. At
low SNR, the distance is larger because the noise is amplified
and the estimation error increases.
[8]
[9]
[10]
[11]
[12]
V. C ONCLUSION AND F UTURE W ORK
This work designs a new spectrum sensing technology
called 5IED, and compared its performance, average detection
time and complexity with other energy detection algorithms.
The proposed method is further evaluated by Monte Carlo
simulation, including the effects of noise uncertainty. The
results show that the proposed algorithm has better detection
performance and robustness than 3EED under low SNR
conditions, and does not significantly increase the complexity
and detection time of the algorithm.
[13]
[14]
[15]
[16]
As a future research goal, there is a maximum number of
sensing items that can be used in the decision. A detector that
is more robust to noise uncertainties is sought. Performance
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