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