Concurrent process for blind deconvolution of digital signals
Abstract
This process has the objective to eliminate the problem of intersymbol interference in digital signals, which is caused by the dispersive effect of any practical transmission channel. The solution for the problem of superposition of propagation rays and its dynamic variation in the transmission channel of a digital system will be one of the hardest challenges for the technological improvement in this area for the next few years. In mobile communications, this undesirable phenomena is characterized by parameters such as delay spread, angle spread and Doppler spread, which determine the level and dynamics of superposition between the system symbols, occurring as a consequence of the transmission of information through the channel. The devices that are likely to have their performance improved by the use of the Concurrent Process for Blind Deconvolution of Digital Signals includes but is not limited to: Spatial-Temporal Processing (used, for example, in Smart Antennas; Smart Sensors, etc . . . ); Digital telecommunication systems in general (cellular telephony, digital television, digital radio, etc . . . ); telemetry systems, remote sensing systems, geodesic localization/measurement systems (GPS, etc . . . ), navigation aid systems, seismic survey systems by wave refraction/reflection, magnetic media storage systems, RADAR systems, SONAR systems.
Claims
exact text as granted — not AI-modified1 . SYSTEM FOR BLIND DECONVOLUTION OF DIGITAL SIGNALS USING THE PROCESS CLAIMED IN 2 characterized for the schematic shown in FIG. 2 and to the output signal y from said system be a sum of the output signals of two filters, denominate herein V and W, in which the vector V =[V 0 V 1 . . . V L−1 ] T is adjusted for any gradient based algorithm that minimize a cost function J D , that measure dispersion, while the vector W =[W 0 W 1 . . . W L−1 ] T is adjusted for any gradient based algorithm that minimize a cost function J Q , or any other equivalent function that measure the distance of the output y from the nearest digital alphabet symbol, denominated herein Q{y}, where the operator Q{.} represents quantization of the symbols of the digital alphabet A; for any block z −1 introduce a delay of one sample and E{.} is the operator that restore the statistical median of the argument, while the operator {.} T results in the transposition of the vector/matrix argument and the operator |.| return the Euclidian norm of the argument; for the connection beetwen J D e J Q is obtained by means of a non linear function that inhibit the process of J Q when the minimization process of J D do not simultaneously minimize J Q .
2 . CONCURRENT PROCESS FOR BLIND DECONVOLUTION OF DIGITAL SIGNALS FOR THE SYSTEM OF THE claim 1 characterized for the following procedures: Inicialize the vectors W e V : W = 0+j0 and
V
k
=
{
0
+
j
0
,
k
=
0
,
1
,
…
L
-
1
,
k
≠
ξ
1
+
j
0
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k
=
ξ
j={square root}{square root over (−1)}, L is the dimension of the vectors V e W and ξ, 0<ξ<L−1, is the index of the vector V to be inicialize with the value 1+j0; Inicialize the indexer of samples obtained fot fractionary sampling of the channel: i=1; Inicialize the regression indexer of the channel: n=0; Obtain the n regressor of the transmition channel r (n)[6][1][7]: r k (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 transmition channel with i=1, 3, . . . N a −1 changing as n=0, 1, . . . , N r −1, N a is the total number of samples that will be obtained for fractionary sampling of the transmition channel,
N
r
=
[
N
a
-
L
-
1
2
]
+
1
is the total number of regressors to be obtained of the transmition channel and T is the time gap between the symbols generated in the transmitter, └.┘ is the operator that results in the nearest whole smaller than the argument; Obtain the output of the system in the instant n: y(n)= W T (n)· r (n)+ V T (n)· r (n); update 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; update the vector W W (n+1)= W (n)+η w [1−D Q (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
D
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controls the non-linear function and {tilde over (y)}(n)= V T (n+1)· r (n)+ W T (n)· r (n); Increase indexers: i=i+2 n=n+1; Test end of loop: if L+i>N a END, in any other case repeat steps 4 to 9.Join the waitlist — get patent alerts
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