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The Richardson-Lucy Algorithm Based Demodulation Algorithms for ...

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Introduction<br />

Two-dimensional ISI Channel Model<br />

Wiener <strong>Based</strong> <strong>Demodulation</strong> <strong>Algorithm</strong>s<br />

<strong>Richardson</strong>-<strong>Lucy</strong> <strong>Based</strong> <strong>Demodulation</strong> <strong>Algorithm</strong>s<br />

Comparison of Wiener and <strong>Richardson</strong>-<strong>Lucy</strong> <strong>Based</strong> <strong>Algorithm</strong>s<br />

Conclusions<br />

Blind and Nonblind <strong>Richardson</strong>-<strong>Lucy</strong> <strong>Algorithm</strong>s<br />

<strong>Richardson</strong>-<strong>Lucy</strong> <strong>Algorithm</strong> <strong>for</strong> AWGN Channel<br />

<strong>Richardson</strong>-<strong>Lucy</strong> <strong>Based</strong> <strong>Demodulation</strong> <strong>Algorithm</strong>s<br />

Blind <strong>Demodulation</strong> with Progressive Thresholding<br />

BER Per<strong>for</strong>mance<br />

Blind <strong>Richardson</strong>-<strong>Lucy</strong> <strong>Algorithm</strong> – Blind Deconvolution of<br />

Nonnegative Images<br />

Initialize s(x, y) and h(x, y)<br />

Blind <strong>Richardson</strong>-<strong>Lucy</strong> iteration<br />

Estimate h(x, y)<br />

[<br />

]<br />

h (r+1) (x, y) = h(r) (x, y) v(x, y)<br />

∑<br />

(x,y) s(x, y) h (r) (x, y) ∗ ∗s(x, y) ∗ ∗s(−x, −y) (12)<br />

Estimate s(x, y)<br />

[<br />

]<br />

s (r+1) (x, y) = s (r) v(x, y)<br />

(x, y)<br />

s (r) (x, y) ∗ ∗h(x, y) ∗ ∗h(−x, −y)<br />

Nonnegative everything.<br />

(13)<br />

Zhijun Zhao and Richard E. Blahut<br />

<strong>The</strong> <strong>Richardson</strong>-<strong>Lucy</strong> <strong>Algorithm</strong> <strong>Based</strong> <strong>Demodulation</strong> <strong>Algorithm</strong>

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