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M=2 and ε=10 % Fig. 15 Graph of the PSNR losses of the proposed technique vs. the relative entropy of the estimated preference distribution with respect to perfect estimation p c=50%%.

M=2 and ε=10 % Table 13 PSNR losses of the proposed technique and relative entropy of the estimated preference distribution with respect to the perfect estimation p c=50%%.

Section 5 depicts the performance of our algorithm and finally shows that the estimated couple ( σ ̂ 2, H ̂ n ) is close to the perfect estimation one (σ 2,H n ).

The average ΔPSNR with respect to the perfect estimation is reported in Table 13, together with the relative entropy of the estimated distribution with respect to the real one.

The perfect estimation of the disturbance and state is shown.

A significant variance is visible with a small channel estimation error (or perfect estimation).

Figure 8 Proposed method BER versus SNR and comparison with the perfect estimation, perfect case, and LS.

This metric gives scores in the [ 0,100] range, with 0 corresponding to perfect estimation.

If is the perfect estimation of, the peak of is, and due to channel energy normalization.

In this work, it is assumed that perfect estimation of BEM coefficients has been established.

Note that for perfect estimation the scatter plot should be a 45◦ line.