Frequency compensated communications reception

• US 20070165752A1
• Filed: 02/15/2005
• Published: 07/19/2007
• Est. Priority Date: 02/20/2004
• Status: Active Grant

• Current Assignee: Vocalcomm Group LLC (Intellectual Ventures LLC)
• Original Assignee: Qinetiq Limited (QinetiQ Group PLC)
• Inventors: Warner, Edward
• Granted Patent: US 7,715,499 B2
• Time in Patent Office: Days
• US Class Current: 375/346
• CPC Class Codes: H04L 27/0014 Carrier regulation of chaotic carriers H04L27/001; for multicarrier receivers H04L27/2657

Abstract

Frequency compensated communications reception includes compensating for frequency offset in a received signal by constructing a reference signal for comparison with a training sequence in a received signal. The reference signal is formed from basis functions and the training sequence. It is obtained by minimising a cost function J constructed from an adaptively weighted combination of basis functions, the training sequence, the received signal and a constraint requiring non-zero signal power. Multi-element antenna signals are weighted with a beamforming weight vector w in J given by formula (I), where X is a matrix of received signal samples, C is a diagonal matrix containing elements of the training sequence, F is a matrix having columns defining basis functions, v is a vector of adaptive weights, index H indicates complex conjugate transpose and λ is a Lagrange multiplier constraining beamformer power. A single element antenna signal x is scaled in J given by formula (II), where α is a scaling factor, * indicates a complex conjugate, and x is a vector of received signal samples.

Claims

1-30. -30. (canceled)

31. An automated method for frequency compensated communications reception including compensating for frequency offset in a received signal by adaptively forming a combination of basis functions and a training sequence that collectively approximate to a desired frequency-shifted signal to be acquired.

32. A method according to claim 31 including constructing a reference signal or comparison training sequence that is an adaptively formed combination of basis functions and the training sequence.

33. A method according to claim 32 for acquiring a signal with a receiver having multiple antenna elements, the method including constructing the reference signal by minimising a cost function constructed from an adaptively weighted combination of basis functions, a training sequence and a received signal, together with a constraint to obtain non-trivial solutions.

34. A method according to claim 33 wherein the constraint requires non-zero signal power.

35. A method according to claim 33 wherein the cost function is J given by:

J=∥

Xw−

CFv∥

²+λ

(w^HX^HXw−

1), where x is a matrix of received signal samples, w is a vector of beamforming weights which are adaptive to minimise J, C is a diagonal matrix having elements of the training sequence on its diagonal, F is a matrix having columns defining respective basis functions, v is a vector of weights which are adaptive to minimise J, superscript index H indicates a complex conjugate transpose and λ

is a Lagrange multiplier for a term to constrain beamformer output power to be non-zero.

36. A method according to claim 35 including determining the adaptive weight vectors w and v at intervals from true estimates of a correlation matrix determined from multiple data vectors and from inverses of such estimates recursively updated to reflect successive new data vectors which are rows of the matrix X.

37. A method according to claim 36 including recursively updating inverse correlation matrices by:

a) forming a vector u(n) having a first element u₁(n) equal to √

{square root over (U_1,1(n))} and other elements u_p(n) (p=2 to M) which are respective ratios U_p,1(n)/u₁(n), U_p,1(n) is a pth element of a first column of a matrix U(n), the matrix U(n)≡

u(n)u^H(n)=x(n)x^H(n)−

x(n−

K+1)x^H(n−

K+1), x(n) is a most recent data vector and x(n−

K+1) is a least recent data vector involved in updating and x(n)x^H(n) and x(n−

K+1)x^H(n−

K+1) are correlation matrices;

b) premultiplying a previous inverse correlation matrix P(n−

1) by vector u^H(n) and postmultiplied by vector u(n) to form a product and adding the product to a forget factor to form a sum;

c) postmultiplying the previous inverse correlation matrix P(n−

1) by vector u(n) and dividing by the said sum to form a quotient; and

d) subtracting the quotient from the previous inverse correlation matrix P(n−

1) to provide a difference.

38. A method according to claim 32 for acquiring a signal with a receiver having a single antenna element, the method including constructing the reference signal by minimising a cost function constructed from an adaptively weighted combination of basis functions, a scaled received signal and a constraint requiring non-zero signal power.

39. A method according to claim 38 wherein the cost function is J given by:

J=∥

x−

CFv∥

², where x is a vector of received signal samples, and v, C and F are as defined earlier.

40. A method according to claim 38 wherein the cost function is J given by:

J=∥

α

x−

Gv∥

²+λ

(α

*x^Hxα

−

1), where a is a scaling factor, x is a vector of received signal samples, G is a matrix equal to CF and v, λ

, C, F and H are as defined earlier.

41. Apparatus for frequency compensated communications reception including means for compensating for frequency offset in a received signal by adaptively forming a combination of basis functions and a training sequence that collectively approximate to a desired frequency-shifted signal to be acquired.

42. Apparatus according to claim 41 including means for constructing a reference signal or comparison training sequence that is an adaptively formed combination of basis functions and the training sequence.

43. Apparatus according to claim 42 having a receiver with multiple antenna elements for acquiring the received signal, the apparatus including means for constructing the reference signal by minimising a cost function constructed from an adaptively weighted combination of basis functions, a training sequence and a received signal, together with a constraint to obtain non-trivial solutions.

44. Apparatus according to claim 43 wherein the constraint requires non-zero signal power.

45. Apparatus according to claim 43 wherein the cost function is J given by:

J=∥

Xw−

CFV∥

²+λ

(w^HX^HXw−

1), where x is a matrix of received signal samples, w is a vector of beamforming weights which are adaptive to minimise J, C is a diagonal matrix having elements of the training sequence on its diagonal, F is a matrix having columns defining respective basis functions, v is a vector of weights which are adaptive to minimise J, superscript index H indicates a complex conjugate transpose and λ

is a Lagrange multiplier for term to constrain beamformer output power to be non-zero.

46. Apparatus according to claim 45 including means for determining the adaptive weight vectors w and v at intervals from true estimates of a correlation matrix determined from multiple data vectors and from inverses of such estimates recursively updated to reflect successive new data vectors which are rows of the matrix X.

47. Apparatus according to claim 46 including means for recursively updating inverse correlation matrices by:

a) forming a vector u(n) having a first element u₁(n) equal to √

{square root over (U_1,1(n))} and other elements u_p(n) (p=2 to M) which are respective ratios U_p,1(n)/u₁(n), U_p,1(n) is a pth element of a first column of a matrix U(n), the matrix U(n)≡

u(n)u^H(n)=x(n)x^H(n)−

x(n−

K+1)x^H(n−

K+1), x(n) is a most recent data vector and x(n−

K+1) is a least recent data vector involved in updating and x(n)x^H(n) and x(n−

K+1)x^H(n−

K+1) are correlation matrices;

b) premultiplying a previous inverse correlation matrix P(n−

1) by vector u^H(n) and postmultiplied by vector u(n) to form a product and adding the product to a forget factor to form a sum;

c) postmultiplying the previous inverse correlation matrix P(n−

1) by vector u(n) and dividing by the said sum to form a quotient; and

d) subtracting the quotient from the previous inverse correlation matrix P(n−

1) to provide a difference.

48. Apparatus according to claim 42 having a receiver with a single antenna element for acquiring the received signal, the apparatus including means for constructing the reference signal by minimising a cost function constructed from an adaptively weighted combination of basis functions, a scaled received signal and a constraint requiring non-zero signal power.

49. Apparatus according to claim 48 wherein the cost function is J given by:

J=∥

x−

CFv∥

², where x is a vector of received signal samples, and v, C and F are as defined earlier.

50. Apparatus according to claim 48 wherein the cost function is J given by:

J=∥

α

x−

GV∥

²+λ

(α

*x^Hxα

−

1), where α

is a scaling factor, x is a vector of received signal samples, G is a matrix equal to CF and v, λ

, C, F and H are as defined earlier.

51. A computer software product comprising a computer readable medium containing computer readable instructions for controlling operation of computer apparatus for use in frequency compensated communications reception, wherein the computer readable instructions provide a means for controlling the computer apparatus to compensate for frequency offset in a received signal by adaptively forming a combination of basis functions and a training sequence that collectively approximate to a desired frequency-shifted signal to be acquired.

52. A computer software product according to claim 51 wherein the computer readable instructions provide a means for constructing a reference signal or comparison training sequence that is an adaptively formed combination of basis functions and the training sequence.

53. A computer software product according to claim 52 for use in processing received signals acquired by a receiver with multiple antenna elements, wherein the computer readable instructions provide a means for for constructing the reference signal by minimising a cost function constructed from an adaptively weighted combination of basis functions, a training sequence and a received signal, together with a constraint to obtain non-trivial solutions.

54. A computer software product according to claim 53 wherein the constraint requires non-zero signal power.

55. A computer software product according to claim 53 wherein the cost function is J given by:

J=∥

Xw−

CFv∥

²+λ

(w^HX^HXw−

1), where x is a matrix of received signal samples, w is a vector of beamforming weights which are adaptive to minimise j, C is a diagonal matrix having elements of the training sequence on its diagonal, F is a matrix having columns defining respective basis functions, v is a vector of weights which are adaptive to minimise J, superscript index H indicates a complex conjugate transpose and λ

is a Lagrange multiplier and the term which incorporates it is to constrain beamformer output power to be non-zero.

56. A computer software product according to claim 55 wherein the computer readable instructions provide a means for determining the adaptive weight vectors w and v at intervals from true estimates of a correlation matrix determined from multiple data vectors and from inverses of such estimates recursively updated to reflect successive new data vectors which are rows of the matrix X.

57. A computer software product according to claim 56 wherein the computer readable instructions provide a means for recursively updating inverse correlation matrices by:

a) forming a vector u(n) having a first element u₁(n) equal to √

{square root over (U_1,1(n))} and other elements u_p(n) (p=2 to M) which are respective ratios U_p,1(n)/u₁(n), U_p,1(n) is a pth element of a first column of a matrix U(n), the matrix U(n)≡

u(n)u^H(n)=x(n)x^H(n)−

x(n−

K+1)x^H(n−

K+1), x(n) is a most recent data vector and x(n−

K+1) is a least recent data vector involved in updating and x(n)x^H(n) and x(n−

K+1)x^H(n−

K+1) are correlation matrices;

b) premultiplying a previous inverse correlation matrix P(n−

1) by vector u^H(n) and postmultiplied by vector u(n) to form a product and adding the product to a forget factor to form a sum;

c) postmultiplying the previous inverse correlation matrix P(n−

1) by vector u(n) and dividing by the said sum to form a quotient; and

d) subtracting the quotient from the previous inverse correlation matrix P(n−

1) to provide a difference.

58. A computer software product according to claim 52 for use in processing received signals acquired by a receiver with a single antenna element, wherein the computer readable instructions provide a means for constructing the reference signal by minimising a cost function constructed from an adaptively weighted combination of basis functions, a scaled received signal and a constraint requiring non-zero signal power.

59. A computer software product according to claim 58 wherein the cost function is J given by:

J=∥

x−

CFv∥

², where x is a vector of received signal samples, and v, C and F are as defined earlier.

60. A computer software product according to claim 58 wherein the cost function is J given by:

J=∥

α

x−

Gv∥

²+λ

(α

*x^Hxα

−

1), where α

is a scaling factor, x is a vector of received signal samples, G is a matrix equal to CF and v, λ

, C, F and H are as defined earlier.