Signal processing method and apparatus for obtaining diversity gain in wireless communication system equipping antenna array
View Patent ↗A signal processing method and apparatus for obtaining a diversity gain in the communication system equipping the antenna array when a background noise is a space white noise or a non white noise is disclosed. The signal processing method and apparatus includes steps and functions of calculating a covariance matrix, estimating at least two time invariance vectors by performing an eigen-disintegration operation, estimating a channel vector and outputting a beam-forming vector. The present invention can improve the performance of the antenna array by using at least two time invariance vectors and it also increase a communication quality and quantity of the communication system, if the present invention is implemented to the wireless communication system.
1. A signal processing method for obtaining a diversity gain in a communication system equipping an antenna array when a background noise is a spatial white noise vector, comprising the steps of:
a) computing a covariance matrix of an input signal vector;
b) performing a eigen-division of the covariance matrix and estimating a time invariance vector forming a signal sub-space according to a matrix rank (r) of the covariance matrix by selecting at least two eigenvectors having comparatively larger eigenvalue; and
c) estimating a channel vector by receiving the time invariance vector from the step b) and a channel coefficient computed by using a pre-known symbol.
2. The signal processing method in recited as claim 1 , further comprising the step of d) outputting a beam-forming vector by adding weight values of the channel vectors from the step c).
3. The signal processing method in recited as claim 2 , wherein in the step b), the time invariance vectors are estimated according to matrix coefficient r of the covariance matrix by choosing r eigenvectors from the largest eigenvector to r th largest eigenvector among the eigenvectors corresponding to each eigenvalue of the covariance matrix.
4. The signal processing method in recited as claim 1 , wherein the pre-known-symbol used for computing the channel coefficient in the step c) is a pilot symbol s[l].
5. The signal processing method in recited as claim 4 , wherein the channel vector in the step c) is computed by accumulating at least two time invariance vectors and a pilot signal vector, herein the pilot signal vector is computed by performing a smoothing process on a time for reducing an affection of noise included in the vector of the pilot signal vector.
6. A signal processing method for obtaining a diversity gain in a communication system equipping an antenna array when a background noise is a spatial white noise vector, comprising the steps of:
a) calculating a covariance matrix of an input signal vector defined according to an equation as
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wherein N is a smoothing factor;
b) performing a eigen-division of the covariance matrix and estimating plural number (r) of time invariance vectors ({e m });
c) calculating an instance coefficient ĥ m [l] of a pre-known pilot signal s[l] by accumulating an eigenvector and an input signal vector according to an equation as ĥ m [l]=ê m H y[l]s*[l] (m=1,2, . . . ,r) and estimating the channel coefficients {overscore (h m )}[l](m=1,2, . . . ,r) in a channel coefficient estimating means by using the instant coefficient, wherein y[l] is the pilot vector and s[l] is a pilot signal; and
d) estimating a channel vector by using the channel coefficient computed in the step c) and the eigenvector corresponding to the eigenvalue of the input signal vector based on an equation as
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7. The signal processing method as recited in claim 6 , further comprising the step of outputting a sum of weight values by using the channel vector computed in the step c) according to an equation as
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8. The signal processing method as recited in claim 6 , wherein in the step b), the time invariance vectors are estimated according to matrix coefficient r of the covariance matrix by choosing r largest eigenvectors from the largest eigenvector to r th largest eigenvector among the eigenvectors corresponding to each eigenvalue of the covariance matrix.
9. A signal processing method for obtaining a diversity gain in a communication system equipping an antenna array when a background noise is a non-white noise, comprising the steps of:
a) transforming the non-white noise of an input signal to a white noise;
b) calculating a covariance matrix of the white noise;
c) performing an eigen-division of the covariance matrix and estimating a time invariance vector forming a signal sub-space according to a matrix rank (r) of the covariance by selecting at least two eigenvectors, which have comparatively larger value, among the eigenvectors corresponding each eigenvalue of the covariance matrix; and
d) estimating a channel vector by receiving the time invariance vector from the step b) and a channel coefficient computed by using a pre-known symbol.
10. The signal processing method as recited in claim 9 , further comprising the step of e) outputting a beam-forming vector by adding weight values of the channel vectors.
11. The signal processing method as recited in claim 10 , wherein the step a) includes the steps of:
a1) estimating the covariance matrix of the non-white noise of an input signal;
a2) performing a matrix factorization of the covariance matrix of the non-white noise according to an equation as {circumflex over (R)} n =V H V, wherein {circumflex over (R)} n is the noise covariance matrix and V is a factorized matrix; and
a3) transforming the covariance matrix of the non-white noise to the white noise by using the factorized matrix according to an equation as z[l]=(V H ) −1 y[l].
12. The signal processing method as recited in claim 10 , wherein in the step c), the time invariance vectors are estimated according to a matrix coefficient r of the covariance matrix by choosing r largest eigenvectors (from the largest eigenvector to r th largest eigenvector) among the eigenvectors corresponding to each eigenvalue of the covariance matrix.
13. The signal processing method as recited in claim 12 , wherein the channel vector in the step d) is computed by accumulating at least two time invariance vectors and a pilot signal vector, herein the pilot signal vector is computed by performing a smoothing process on a time for reducing an affection of noise included the pilot signal vector.
14. The signal processing method as recited in claim 9 , wherein the pre-known symbol used for calculating the channel coefficient in the step d) is a pilot symbol s[l].
15. A signal processing method for obtaining a diversity gain in a communication system equipping an antenna array when a background noise is a non-white noise vector, comprising the steps of:
a) estimating the covariance matrix of the non-white noise of an input signal, performing a matrix factorization and transforming to a white noise after performing the matrix factorization based on an equation as z[l]=(V H ) −1 y[l];
b) calculating the covariance matrix based on z[l] according to an equation as
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c) performing an eigen-division of the covariance matrix and estimating plural number (r) of time invariance vectors û m (m=1,2, . . . ,L,)forming a signal sub-space based on a matrix rank (r);
d) calculating an instant coefficient by accumulating the eigenvector of the pre-known pilot signal s[l] and the input signal vector according to an equation as ĝ m [l]=u m H z[{circumflex over (l)}]s*[l] (m=1,2, . . . ,r), wherein s[l] is the pilot signal and z[l] is the input signal vector, which is transformed to the white noise;
e) estimating a channel coefficient {overscore (g m )}[l] (m=1,2, . . . ,r) based on the instant coefficient; and
f) estimating a channel vector by using the channel coefficient {overscore (g m )}[l] (m=1,2, . . . ,r) computed in the step c) and the eigenvector û m (m=1,2, . . . ,L) corresponding to the eigenvalue of the input signal vector, which is transformed to the white noise based on an equation as
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16. The signal processing method as recited in claim 15 , further comprising the step of f) outputting a sum of weight values by using the channel vector computed in the step e), wherein the sum of weight values is computed by an equation as ŝ[l]=ĝ H [l]z[l].
17. The signal processing method as recited in claim 16 , wherein the step a) includes the steps of:
a1) estimating a covariance matrix of the non-white noise of an input signal;
a2) performing a matrix factorization of the non-white noise of the covariance matrix, wherein the matrix factorization is performed based on an equation as {circumflex over (R)} n =V H V, wherein {circumflex over (R)} n is the noise covariance matrix and V is a factorized matrix; and
a3) transforming the non-white noise of the covariance matrix to a white noise by using the factorized matrix, wherein the non-white noise of the covariance matrix is transformed according to an equation as z[l]=(V H ) −1 y[l].
18. The signal processing method as recited in claim 16 , wherein the time invariance vector û m (m=1,2, . . . ,L,) estimated in the step c) are chosen according to matrix coefficient r of the covariance matrix by choosing r largest eigenvectors (from the largest eigenvector to r th largest eigenvector) among the eigenvectors corresponding to each eigenvalue of the covariance matrix.
19. A signal processing apparatus for obtaining a diversity gain in a communication system equipping an antenna array when a background noise is a spatial white noise vector, comprising:
a) covariance matrix estimating means for obtaining an input signal vector by sampling a baseband signal of an input signal, which is received through a plurality of antenna elements, and calculating a covariance matrix of the input signal vector;
b) eigenvalue division means for performing an eigen-division of the covariance matrix;
c) eigenvector estimating means for estimating a time invariance vector forming a signal sub-space according to a matrix rank (r) of the covariance matrix by selecting at least two eigenvectors having comparatively larger eigenvalues;
d) channel vector estimating means for estimating a channel vector by receiving the time invariance vector and the channel coefficient computed by using the pre-known symbol; and
e) calculating means for outputting a beam-forming vector by adding weight values of the channel vector.
20. The signal processing apparatus as recited in claim 19 , wherein the time invariance vectors estimated according to matrix coefficient r of the covariance matrix by choosing r largest eigenvectors (from the largest eigenvector to r th largest eigenvector) among the eigenvectors corresponding to each eigenvalue of the covariance matrix.
21. A signal processing apparatus for obtaining a diversity gain in a communication system equipping an antenna array when a background noise is a non-white noise vector, comprising:
a) covariance matrix estimating means for obtaining a input signal vector of a non-white noise by sampling baseband signals of input signals received through a plurality of antenna elements, transforming the non-white noise to a white noise and calculating a covariance matrix of the white noise;
b) eigenvalue division means for performing an eigen-division of the covariance matrix;
c) eigenvector estimating means for estimating a time invariance vector forming a signal sub-space according to a matrix rank (r) of the covariance matrix by selecting at least two larger eigenvectors corresponding to each eigenvalue of the covariance matrix;
d) channel vector estimating means for estimating a channel vector by receiving the time invariance vector and the channel coefficient computed by using the pre-known symbol; and
e) calculating means for outputting a beam-forming vector by adding weight values of the channel vector.
22. The signal processing apparatus as recited in claim 21 , wherein the time invariance vectors estimated in the eigenvector estimating means according to matrix coefficient r of the covariance matrix by choosing r largest eigenvectors from the largest eigenvector to r th largest eigenvector among the eigenvectors corresponding to each eigenvalue of the covariance matrix.
23. The signal processing apparatus as recited in claim 22 , wherein in the covariance matrix estimating means, the matrix factorization of the non-white noise of the covariance matrix according to an equation as {circumflex over (R)} n =V H V, wherein {circumflex over (R)} n is the noise covariance matrix and V is a factorized matrix and then transforms the non-white noise of the covariance matrix to the white noise by using the factorized matrix according to an equation as z[l]=(V H ) −1 y[l].
24. A computer readable recording medium storing instructions for executing the signal processing method in case of that a background noise is a space white noise vector, comprising the functions of:
a) computing a covariance matrix of an input signal vector;
b) performing a eigen-division of the covariance matrix and estimating a time invariance vector forming a signal sub-space according to a matrix rank (r) of the covariance by selecting at least two eigenvectors, which have comparatively larger valued among the eigenvalues corresponding eigenvectors of the covariance matrix;
c) estimating a channel vector by receiving the time invariance vector from the step b) and a channel coefficient computed by using a pre-known symbol; and
d) outputting a beam-forming vector by adding weights of the channel vectors from the step c).
25. A computer readable recording medium storing instructions for executing the signal processing method in case of that a background noise is a non-white noise vector, comprising the functions of:
a) transforming the non-white noise of an input signal to a white noise;
b) calculating a covariance matrix of the white noise;
c) performing a eigen-division of the covariance matrix and estimating time invariance vectors forming a signal sub-space according to a matrix rank (r) of the covariance by selecting at least two eigenvectors, which have comparatively larger value, among the eigenvalues corresponding eigenvectors of the covariance matrix; and
d) estimating a channel vector by receiving the time invariance vector from the step b) and a channel coefficient computed by using a pre-known symbol.