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 />
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>