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. 2023 Jun 7;23(12):5403.
doi: 10.3390/s23125403.

Modified Gini Index Detector for Cooperative Spectrum Sensing over Line-of-Sight Channels

Dayan Adionel Guimarães  1
Affiliations

Modified Gini Index Detector for Cooperative Spectrum Sensing over Line-of-Sight Channels

Dayan Adionel Guimarães. Sensors (Basel). .

Abstract

Recently, the Gini index detector (GID) has been proposed as an alternative for data-fusion cooperative spectrum sensing, being mostly suitable for channels with line-of-sight or dominant multi-path components. The GID is quite robust against time-varying noise and signal powers, has the constant false-alarm rate property, can outperform many the state-of-the-art robust detectors, and is one of the simplest detectors developed so far. The modified GID (mGID) is devised in this article. It inherits the attractive attributes of the GID, yet with a computational cost far below the GID. Specifically, the time complexity of the mGID obeys approximately the same run-time growth rate of the GID, but has a constant factor approximately 23.4 times smaller. Equivalently, the mGID takes approximately 4% of the computation time spent to calculate the GID test statistic, which brings a huge reduction in the latency of the spectrum sensing process. Moreover, this latency reduction comes with no performance loss with respect to the GID.

Keywords: Gini index detector; cognitive radio; dynamic spectrum access; dynamic spectrum sharing; spectrum sensing.

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Conflict of interest statement

The author declare no conflict of interest.

Figures

Figure 1
Figure 1
Factor ln[(1+ρ)/(1−ρ)]/2ρ, in dB, as a function of ρ.
Figure 2
Figure 2
Random realizations of |ri| and |qi| for m=3.
Figure 3
Figure 3
Run-time computation measurements of the GID and mGID test statistics.
Figure 4
Figure 4
Empirical PDFs of the test statistics TGID and TmGID under H0 and H1: (a) average noise variance σ¯2=1.9408×10−6; (b) average noise variance σ¯2=4.4921×10−5.
Figure 5
Figure 5
CSS topology for m=10 SUs, normalized coverage radius r=1 m, FC at (x,y)=(0,0) m, and PU transmitter at (x,y)=(1,1) m.
Figure 6
Figure 6
Pd versus μK, for m=6, SNR=−13 dB, η=2.5, r=1 km, n=250, ρ=0.5, and σK=0 dB.
Figure 7
Figure 7
Pd versus ρ, for m=6, SNR=−13 dB, η=2.5, r=1 km, n=250, μK=20 dB, and σK=0 dB: (a) correct SNR model; (b) incorrect SNR model.
Figure 8
Figure 8
Pd versus m for SNR=−14 dB, η=2.5, r=1 km, n=250, ρ=0.5, μK=20 dB, and σK=0 dB.
Figure 9
Figure 9
Pd versus SNR for m=6, η=2.5, r=1 km, n=250, ρ=0.5, μK=20 dB, and σK=0 dB.
Figure 10
Figure 10
Pd versus (x,y) coordinates of the PU transmitter, x=y, for m=6, SNR=−14 dB, η=2.5, r=1 km, n=250, ρ=0.5, μK=20 dB, and σK=0 dB.
Figure 11
Figure 11
Pd versus η for m=6, SNR=−13 dB, r=1 km, n=250, ρ=0.5, μK=20 dB, and σK=0 dB.
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