Next we want to show that with the following choice

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Unformatted text preview: D ≥ log m − D log(m − 1) − Hb (D ), (1) where Hb (·) is the binary entropy function. The second equality follows by the chain rule and since ˆ ˆ H (X |X ) = H (X, E |X ); the subsequent inequality since conditioning reduces entropy; and the last inequality from the rate distortion constraint, and by noting that the function t · log(m − 1) + Hb (t) − is nondecreasing in t in the interval t ∈ 0, mm 1 . ˆ Next we want to show that with the following choice of the conditional distribution on X PX |X (ˆ|x) = x ˆ 1 − D if x = x ˆ D else, m−1 (2) both inequalities in (1) are met with equality. Indeed, noting that PX (ˆ) = ˆx PX |X (ˆ|x)PX (x) = (1 − D ) x ˆ x D m−1 1 1 + =. m m−1 m m and that for x = x we have ˆ PX |X (x|x) = ˆ ˆ PX,X (x, x) ˆ ˆ PX (ˆ) ˆx = PX |X (ˆ|x)PX (x) x ˆ PX (ˆ) ˆx we obtain PE |X (0|x) = ˆ ˆ PX |X (x|x) = (m − 1) ˆ ˆ x =x ˆ = D1 m−1 m 1 m = D m−1 D = D. m−1 Since PE |X (0|x) does not depend on x it follows that PE (0) = D . Thus, the chosen distribution ˆ ˆ ˆ ˆ satisfies the distortion constraint; additionally E is independent of X and hence the first inequality in (1) holds with equality. Furthermore from the choice in (2) it follows that 1 m−1 PX |X,E (ˆ|x, 0) = x ˆ 0 for x = x ˆ for x = x. ˆ This can be verified by writing PX |X,E (ˆ|x, 0) = x ˆ = = c Amos Lapidoth, 2012 PX,X,E (ˆ, x, 0) x ˆ PX,E (x, 0) PX (x)PX |X (ˆ|x)PE |X,X (0|x, x) x ˆ ˆ ˆ PX (x)PE |X (0|x) PX |X (ˆ|x)PE |X,X (0|x, x) x ˆ ˆ ˆ PE |X (0|x) 4 , and by noting that PE |X,X (0|x, x) = ˆ ˆ 0 if x = x ˆ 1 if x = x ˆ and PE |X (0|x) = PX |X (ˆ|x)PE |X,X (0|x, x) x ˆ ˆ ˆ x ˆ = x =x ˆ Hence we can conclude that for D ≤ R(D ) = min m−1 m ˆ PX |X :E[d(X,X )]≤D ˆ c Amos Lapidoth, 2012 D · 1 = D. m−1 ˆ I (X ; X ) = log m − D log(m − 1) − Hb (D ). 5...
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This note was uploaded on 05/18/2013 for the course EE Informatio taught by Professor Amoslapidoth during the Fall '11 term at Swiss Federal Institute of Technology Zurich.

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