As shown in the figure, our proposed detector performs better for smaller values of p. For example, the probability of detection is 70% with p=0.33 while 37% with p=0.7, under the same GSNR = −10 dB.
For each type of point estimator considered here, in terms of RMSE the reference priors π r 1, π r 2, and π r 3 provide qualitatively similar results, with π r 1 and π r 3 being slightly better for smaller values of p and π r 2 being slightly better for larger values of p. In addition, the difference in performance of the three reference priors becomes smaller as the sample size increases.
However, is better than for small values of (recall that Corollary 1.3 gives the exact distance for ).
Eve tunes smaller than in the DNA case and we observe better results for small values of the identity proportion in Figure 5c.
Scheduling preforms better than random access for small values of p. This is due to transmission collision among multiple UEs under random access.
Also, it provides better tracking performance only for small values of k, in comparison with the BOC kn,n) ACF [12].
Equations (6), (9), and (11), show that a better SNR can be achieved for small values of R, S, q (d is also proportional to the square root of q), as well as N.
The technique by Tian et al. is only better than the two latter versions for small values of standard deviation.
The multiband method performs better than the spectral eigenface method for small values of the rank, but performs worse for larger values of the rank.
Figure 8 shows that unlike the symmetric model, the performance of SSB is better than FRCH and EJS, specially for small values of G.
