CONCURRENT PROCESS FOR BLIND DECONVOLUTION OF DIGITAL SIGNALS
First Claim
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1. A system for blind deconvolution of digital signals, the system having the following form:
- in which;
A) Output signal y from the system is a sum of output signals of two filters, V and w;
B) Vector V=[V0 V1 . . . VL−
1]T is adjusted for any gradient based algorithm that minimizes a cost function JD, that measures dispersion, C) Vector W=[W0 W1 . . . WL−
1]T is adjusted for any gradient based algorithm that minimize a cost function JQ, or any other equivalent function that measures the distance of the output y from the nearest digital alphabet symbol, denominated as Q{y}, where the operator Q{.} represents quantization of the symbols of the digital alphabet A;
D) Any block z−
1 introduces a delay of one sample;
E) E{.} is the operator that restores a statistical median o f the argument;
F) Operator {.}T results in the transposition of a vector/matrix argument;
G) Operator |.| returns the Euclidian norm of the vector/matrix argument; and
H) A connection between JD and JQ is obtained by means of a non linear function that inhibits the process of JQ when the minimization process of JD does not simultaneously minimize JQ.
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Abstract
A process and system for eliminating intersymbol interference in digital signals, which is caused by the dispersive effect of any practical transmission channel.
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Citations
2 Claims
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1. A system for blind deconvolution of digital signals, the system having the following form:
-
in which;
A) Output signal y from the system is a sum of output signals of two filters, V and w;
B) Vector V=[V0 V1 . . . VL−
1]T is adjusted for any gradient based algorithm that minimizes a cost function JD, that measures dispersion,C) Vector W=[W0 W1 . . . WL−
1]T is adjusted for any gradient based algorithm that minimize a cost function JQ, or any other equivalent function that measures the distance of the output y from the nearest digital alphabet symbol, denominated as Q{y}, where the operator Q{.} represents quantization of the symbols of the digital alphabet A;
D) Any block z−
1 introduces a delay of one sample;
E) E{.} is the operator that restores a statistical median o f the argument;
F) Operator {.}T results in the transposition of a vector/matrix argument;
G) Operator |.| returns the Euclidian norm of the vector/matrix argument; and
H) A connection between JD and JQ is obtained by means of a non linear function that inhibits the process of JQ when the minimization process of JD does not simultaneously minimize JQ.
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2. A system for blind deconvolution of digital signals, the system having the following form:
-
in which;
A) Output signal y from the system is a sum of output signals of two filters, V and W;
B) Vector V=[V0 V1 . . . VL−
1]T is adjusted for any gradient based algorithm that minimizes a cost function JD, that measures dispersion,C) Vector W=[W0 W1 . . . WL−
1]T is adjusted for any gradient based algorithm that minimize a cost function JQ, or any other equivalent function that measures the distance of the output signal y from the nearest digital alphabet symbol, denominated as Q{y}, where the operator Q{.} represents quantization of the symbols of the digital alphabet A;
D) Any block z−
1 introduces a delay of one sample;
E) E{.} is the operator that restores a statistical median of the argument;
F) Operator {.}T results in the transposition of a vector/matrix argument;
G) Operator |.| returns the Euclidian norm of the vector/matrix argument; and
H) A connection between JD and JQ is obtained by means of a non linear function that inhibits the process of JQ when the minimization process of JD does not simultaneously minimize JQ, wherein the system is carried out by a concurrent process for blind deconvolution of digital signals, comprising the steps of;
a) Initializing the vectors W and V;
W=0+j0 and
where j=√
{square root over (−
1)}, L is the dimension of the vectors V and W, and ξ
, 0<
ξ
<
L−
1, is the index of the vector V to be initialized with the value 1+j0;
b) Initializing the indexer of samples obtained for fractional sampling of the channel;
i=1;
c) Initializing the regression indexer of the channel;
n=0;
d) Obtaining the n regressor of the transmission channel r(n);
rk(n)=u(L−
1−
k+i), k=0, 1, . . . , L−
1, where the u is the sequence of samples received for the fractionary sampling T/2 of the transmission channel with i=1, 3, . . . Na−
1 changing as n=0, 1, . . . , Nr−
1, Na is the total number of samples that will be obtained for fractionary sampling of the transmission channel,
is the total number of regressors to be obtained of the transmission channel and T is the time gap between the symbols generated in the transmitter, └
.┘
is the operator that results in the nearest whole number smaller than the argument;
e) Obtaining the output of the system in the instant n;
y(n)=WT(n)·
r(n)+VT(n)·
r(n);
f) Updating the vector V;
V(n+1)=V(n)+η
v·
y(n)(γ
−
|y(n)|2)·
r*(n), where η
v is the adaptation step of the vector V, 0<
η
v<
<
1.0;
g) updating the vector W(n+1)=W(n)+η
w[1−
DQ(n)][Q{y(n)}−
y(n)]r*(n), where η
w is the adaptation step of the vector W, 0<
η
w<
<
1.0, and
controls the non-linear function and {tilde over (y)}(n)=VT(n+1)·
r(n)+WT(n)·
r(n);
h) Increasing indexer l to i+2 and n to n+1; and
i) Testing end of loop and if L+i>
Na, then ending the process and in any other case repeating steps d) to i).
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Specification