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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 />

<strong>Richardson</strong>-<strong>Lucy</strong> with Progressive Thresholding<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 />

Progressive thresholding<br />

⎧<br />

⎨ ν + 1<br />

s (q) (x, y) ⇐ ν<br />

⎩<br />

s (q) (x, y)<br />

if s (q) (x, y) ≥ 1/2 + ν + T (q)<br />

if s (q) (x, y) ≤ 1/2 + ν − T (q)<br />

elsewhere<br />

where q = r + 1, T : {0, 1, 2, . . .} → [0, 1/2] is a monotone<br />

decreasing function of iteration.<br />

Hard-thresholding<br />

(24)<br />

(25)<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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