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A conventional modified UMP-BP algorithm computes the tentative decision and the parity-check equation after every iteration (Figure 6).

During each iteration (Figure 4), we train and evaluate models using candidate subsets of features and expand the set of selected features using the greedy approach described in Section 2.3.

pdf of in−phase equalizer output after the 1st iterationpdf of quadrature equalizer output after the 1st iterationpdf of in−phase equalizer output after the 4th iterationpdf of quadrature equalizer output after the 4th iteration Figure 15 SINR versus time.

Since we use an iterative method to solve the regression problem, interpretation of variability in the ensemble can be further analyzed by looking at the solution trajectory for Lasso (ω vs. Lasso iteration; Figure 5C).

Figures 1 and 2 show the desired profile, Figures 3 and 4 show the relative profile at the twentieth iteration, Figures 5 and 6 show the error curved surface, where (e_{ki} x,t)=y_{di} x,t -y_{ki} x,t -y_{ki} x),t(k=20).

A histogram for all 2000 iterations (Figure 4) suggests that tinf falls within a window of ~300 years.

These oscillation artifacts changed with the number of iterations (Figure 5).

The residual fraction in the three inserts of the NEMA phantom decreased with the number of iterations (Figure 1).

However, MMSE-IC and I-VBLAST performances are close to LC-K-Best decoder with four outer iterations (Figure 7b).

However, this gap is reduced to 0.2 dB at a BER level of 1×10−4 with four outer iterations (Figure 7b).

For 1, 10, 40, and 50 iterations, Figure 5 illustrates the evolution of Pdet compared with P e for L = 500.