Method and apparatus for pilot estimation using a prediction error method with a kalman filter and a Gauss-Newton algorithm

• US 7,286,506 B2
• Filed: 09/30/2002
• Issued: 10/23/2007
• Est. Priority Date: 06/05/2002
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Abrishamkar, Farrokh, Kreutz-Delgado, Kenneth
• Primary Examiner(s): Jain; Raj K.
• Application Number:
  US10/262,044
• Publication Number:
  US 20030227887A1
• Time in Patent Office: 1,849 Days
• Field of Search: None
• US Class Current: 370/332
• CPC Class Codes: H04B 1/7101 with estimation filters
• CPC Class Codes: H04B 2201/70701 featuring pilot assisted reception

Abstract

A system is disclosed for use in a wireless communication system to provide an estimated pilot signal. The system includes a receiver and a front-end processing and despreading component in electronic communication with the receiver for despreading a CDMA signal. A pilot estimation component is in electronic communication with the front-end processing and despreading component for estimating an original pilot signal using a Kalman filter to produce a pilot estimate. A demodulation component is in electronic communication with the pilot estimation component and the front-end processing and despreading component for providing demodulated data symbols. The Kalman filter is configured by an offline system identification process that calculates parameters using a prediction error method and a Gauss-Newton algorithm and generates state estimates using the Kalman filter. The calculating and generating are iteratively performed to train the Kalman filter for real-time operation.

Claims

1. In a wireless communication system, a method for estimating an original pilot signal, the method comprising:

receiving a CDMA signal;

despreading the CDMA signal;

obtaining an original pilot signal from the CDMA signal, wherein the original pilot signal contains a first quantity of noise and a first quantity of fading;

estimating the original pilot signal using a Kalman filter and the original pilot signal to produce a pilot estimate, wherein the pilot estimate contains a second quantity of noise which is less than the first quantity of noise and a second quantity of fading which is less than the first quantity of fading, wherein the Kalman filter is determined through use of a Gauss-Newton algorithm; and

using the pilot estimate instead of the original pilot signal to acquire the timing of one or more channels included in the CDMA signal.

2. The method as in claim 1, wherein the CDMA signal is transmitted on a downlink and wherein the downlink comprises a pilot channel.

3. The method as in claim 1, wherein the CDMA signal is transmitted on an uplink and wherein the uplink comprises a pilot channel.

4. The method as in claim 1, further comprising demodulating the pilot estimate.

5. In a wireless communication system, a method for estimating an original pilot signal, the method comprising:

receiving a CDMA signal;

despreading the CDMA signal;

obtaining a pilot signal from the CDMA signal;

configuring a Kalman filter by an offline system identification process, wherein the offline system identification process comprises;

providing training samples; and

calculating parameters for the Kalman filter using a prediction error method and a Gauss-Newton algorithm and generating a state estimate using the Kalman filter, wherein the calculating and generating are iteratively performed until the Kalman filter converges; and

estimating the original pilot signal using the Kalman filter and the obtained pilot signal to produce a pilot estimate, wherein the Kalman filter is determined through use of the Gauss-Newton algorithm.

6. The method as in claim 5, wherein the parameters are calculated according to the following:

$\mathrm{\Delta <br><br>}<br><br>\widehat{\mathrm{\theta <br><br>}}={\left(\frac{1}{N}<br><br>\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br><br><br>{\mathrm{\psi <br><br>}}_{k-1}^T<br><br>{\mathrm{\psi <br><br>}}_{k-1}\right)}^{-1}<br><br>\left(\frac{1}{N}<br><br>\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br>{\mathrm{\psi <br><br>}}_{k-1}^T<br><br>e_k\right),$

wherein Δ

{circumflex over (θ

)} is a parameter update, ψ

is a filtered regressor, e is an innovator, and k is an iteration sequence.

7. The method as in claim 6, wherein the prediction error method is based on an innovations representation model of the pilot signal.

8. The method as in claim 6, wherein the prediction error method finds optimum model parameters by minimizing a function of a one-step prediction error.

9. The method as in claim 8, wherein the Gauss-Newton algorithm is used in finding a numerical solution for the function.

10. The method as in claim 9, wherein the parameters are adjusted when {circumflex over (d)}<

1 according to the following;

{circumflex over (θ

)}←

{circumflex over (θ

)}+α

Δ

{circumflex over (θ

)}.

11. In a mobile station for use in a wireless communication system, a method for estimating an original pilot signal, the method comprising:

receiving a CDMA signal;

despreading the CDMA signal;

obtaining an original pilot signal from the CDMA signal, wherein the original pilot signal contains a first quantity of noise and a first quantity of fading;

estimating the original pilot signal using a Kalman filter and the original pilot signal to produce a pilot estimate, wherein the pilot estimate contains a second quantity of noise which is less than the first quantity of noise and a second quantity of fading which is less than the first quantity of fading, wherein the Kalman filter is determined through use of a Gauss-Newton algorithm; and

using the pilot estimate instead of the original pilot signal to acquire the timing of one or more channels included in the CDMA signal.

12. The method as in claim 11, wherein the CDMA signal is transmitted on a downlink and wherein the downlink comprises a pilot channel.

13. The method as in claim 11, further comprising demodulating the pilot estimate.

14. In a mobile station for use in a wireless communication system, a method for estimating an original pilot signal, the method comprising:

receiving a CDMA signal;

despreading the CDMA signal;

obtaining a pilot signal from the CDMA signal;

configuring a Kalman filter by an offline system identification process, wherein the offline system identification process comprises;

providing training samples; and

calculating parameters for the Kalman filter using a prediction error method and a Gauss-Newton algorithm and generating a state estimate using the Kalman filter, wherein the calculating and generating are iteratively performed until the Kalman filter converges; and

estimating the original pilot signal using the Kalman filter and the obtained pilot signal to produce a pilot estimate, wherein the Kalman filter is determined through use of a Gauss-Newton algorithm.

15. The method as in claim 14, wherein the parameters are calculated according to the following:

$\mathrm{\Delta <br><br>}<br><br>\widehat{\mathrm{\theta <br><br>}}={\left(\frac{1}{N}<br><br>\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br><br><br>{\mathrm{\psi <br><br>}}_{k-1}^T<br><br>{\mathrm{\psi <br><br>}}_{k-1}\right)}^{-1}<br><br>\left(\frac{1}{N}<br><br>\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br>{\mathrm{\psi <br><br>}}_{k-1}^T<br><br>e_k\right),$

wherein Δ

{circumflex over (θ

)} is a parameter update, ψ

is a filtered regressor, e is an innovator, and k is an iteration sequence.

16. The method as in claim 15, wherein the prediction error method is based on an innovations representation model of the pilot signal.

17. The method as in claim 15, wherein the prediction error method finds optimum model parameters by minimizing a function of the one-step prediction error.

18. The method as in claim 17, wherein the Gauss-Newton algorithm is used in finding a numerical solution for the function.

19. The method as in claim 18, wherein the parameters are adjusted when {circumflex over (d)}<

1 according to the following;

{circumflex over (θ

)}←

{circumflex over (θ

)}+α

Δ

{circumflex over (θ

)}.

20. A mobile station for use in a wireless communication system wherein the mobile station is configured to estimate an original pilot signal, the mobile station comprising:

an antenna for receiving a CDMA signal;

a receiver in electronic communication with the antenna;

a front-end processing and despreading component in electronic communication with the receiver for despreading the CDMA signal and obtaining an original pilot signal from the CDMA signal, wherein the original pilot signal contains a first quantity of noise and a first quantity of fading;

a pilot estimation component in electronic communication with the front-end processing and despreading component for estimating the original pilot signal using a Kalman filter and the original pilot signal to produce a pilot estimate, the pilot estimate contains a second quantity of noise which is less than the first quantity of noise and a second quantity of fading which is less than the first quantity of fading, the pilot estimate is used instead of the original pilot signal to acquire the timing of one or more channels included in the CDMA signal, wherein the Kalman filter is determined through use of a Gauss-Newton algorithm; and

a demodulation component in electronic communication with the pilot estimation component and the front-end processing and despreading component for providing demodulated data symbols to the mobile station.

21. The mobile station as in claim 20, wherein the receiver receives the CDMA signal transmitted on a downlink and wherein the downlink comprises a pilot channel.

22. A mobile station for use in a wireless communication system wherein the mobile station is configured to estimate an original pilot signal, the mobile station comprising:

an antenna for receiving a CDMA signal;

a receiver in electronic communication with the antenna;

a front-end processing and despreading component in electronic communication with the receiver for despreading the CDMA signal and obtaining a pilot signal from the CDMA signal;

a Kalman filter configured by an offline system identification process, wherein the offline system identification process comprises;

providing training samples; and

calculating parameters for the Kalman filter using a prediction error method and the Gauss-Newton algorithm and generating a state estimate using the Kalman filter, wherein the calculating and generating are iteratively performed until the Kalman filter converges;

a pilot estimation component in electronic communication with the front-end processing and despreading component for estimating the original pilot signal using the Kalman filter and the obtained pilot signal to produce a pilot estimate, wherein the Kalman filter is determined through use of a Gauss-Newton algorithm; and

a demodulation component in electronic communication with the pilot estimation component and the front-end processing and despreading component for providing demodulated data symbols to the mobile station.

23. The mobile station as in claim 22, wherein the parameters are calculated according to the following:

$\mathrm{\Delta <br><br>}<br><br>\widehat{\mathrm{\theta <br><br>}}={\left(\frac{1}{N}<br><br>\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br><br><br>{\mathrm{\psi <br><br>}}_{k-1}^T<br><br>{\mathrm{\psi <br><br>}}_{k-1}\right)}^{-1}<br><br>\left(\frac{1}{N}<br><br>\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br>{\mathrm{\psi <br><br>}}_{k-1}^T<br><br>e_k\right),$

wherein Δ

{circumflex over (θ

)} is a parameter update, ψ

is a filtered regressor, e is an innovator, and k is an iteration sequence.

24. The mobile station as in claim 23, wherein the prediction error method is based on an innovations representation model of the pilot signal.

25. The mobile station as in claim 23, wherein the prediction error method finds optimum model parameters by minimizing a function of the one-step prediction error.

26. The mobile station as in claim 25, wherein the Gauss-Newton algorithm is used in finding a numerical solution for the function.

27. The method as in claim 26, wherein the parameters are adjusted when {circumflex over (d)}<

1 according to the following;

{circumflex over (θ

)}←

{circumflex over (θ

)}+α

Δ

{circumflex over (θ

)}.

28. A method for offline system identification to configure a Kalman filter for real-time use in a wireless communication system to estimate a pilot signal, the method comprising:

providing training samples;

initializing parameters; and

until the Kalman filter converges, iteratively performing the following steps;

calculating new parameters using a prediction error method and a Gauss-Newton algorithm; and

generating a new state estimate using the Kalman filter; and

using the converged parameters to produce an estimate of an original pilot signal, wherein the original pilot signal contains a first quantity of noise and a first quantity of fading and the estimate of the original pilot signal contains a second quantity of noise which is less than the first quantity of noise and a second quantity of fading which is less than the first quantity of fading.

29. A method for offline system identification to configure a Kalman filter for real-time use in a wireless communication system to estimate a pilot signal, the method comprising:

providing training samples;

initializing parameters; and

until the Kalman filter converges, iteratively performing the following steps;

calculating new parameters using a prediction error method and a Gauss-Newton algorithm, wherein the parameters are calculated according to the following;

$\mathrm{\Delta <br><br>}<br><br>\widehat{\mathrm{\theta <br><br>}}={\left(\frac{1}{N}<br><br>\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br><br><br>{\mathrm{\psi <br><br>}}_{k-1}^T<br><br>{\mathrm{\psi <br><br>}}_{k-1}\right)}^{-1}<br><br>\left(\frac{1}{N}<br><br>\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br>{\mathrm{\psi <br><br>}}_{k-1}^T<br><br>e_k\right),$

wherein Δ

{circumflex over (θ

)} is a parameter update, ψ

is a filtered regressor, e is an innovator, and k is an iteration sequence; and

generating a new state estimate using the Kalman filter.

30. The method as in claim 29, wherein the prediction error method is based on an innovations representation model of the pilot signal.

31. The method as in claim 29, wherein the prediction error method finds optimum model parameters by minimizing a function of a one-step prediction error.

32. The method as in claim 31, wherein the Gauss-Newton algorithm is used in finding a numerical solution for the function.

33. The method as in claim 32, wherein the parameters are adjusted when {circumflex over (d)}<

1 according to the following;

{circumflex over (θ

)}←

{circumflex over (θ

)}+α

Δ

{circumflex over (θ

)}.

34. A mobile station for use in a wireless communication system wherein the mobile station is configured to estimate an original pilot signal, the mobile station comprising:

means for receiving a CDMA signal;

means for despreading the CDMA signal;

means for obtaining an original pilot signal from the CDMA signal, wherein the original pilot signal contains a first quantity of noise and a first quantity of fading;

means for estimating the original pilot signal using a Kalman filter and the original pilot signal to produce a pilot estimate, wherein the pilot estimate contains a second quantity of noise which is less than the first quantity of noise and a second quantity of fading which is less than the first quantity of fading, wherein the Kalman filter is determined through use of a Gauss-Newton algorithm; and

means for using the pilot estimate instead of the original pilot signal to acquire the timing of one or more channels included in the CDMA signal.

35. The mobile station as in claim 34, wherein the CDMA signal is transmitted on a downlink and wherein the downlink comprises a pilot channel.

36. The mobile station as in claim 34, further comprising means for demodulating the pilot estimate.

37. A mobile station for use in a wireless communication system wherein the mobile station is configured to estimate an original pilot signal, the mobile station comprising:

means for receiving a CDMA signal;

means for despreading the CDMA signal;

means for obtaining a pilot signal from the CDMA signal;

means for configuring a Kalman filter by an offline system identification process, wherein the offline system identification process comprises;

providing training samples; and

calculating parameters for the Kalman filter using a prediction error method and a Gauss-Newton algorithm and generating a state estimate using the Kalman filter, wherein the calculating and generating are iteratively performed until the Kalman filter converges; and

means for estimating the original pilot signal using the Kalman filter and the obtained pilot signal to produce a pilot estimate, wherein the Kalman filter is determined through use of a Gauss-Newton algorithm.

38. The mobile station as in claim 37, wherein the parameters are calculated according to the following:

$\mathrm{\Delta <br><br>}<br><br>\widehat{\mathrm{\theta <br><br>}}={\left(\frac{1}{N}<br><br>\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br><br><br>{\mathrm{\psi <br><br>}}_{k-1}^T<br><br>{\mathrm{\psi <br><br>}}_{k-1}\right)}^{-1}<br><br>\left(\frac{1}{N}<br><br>\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br>{\mathrm{\psi <br><br>}}_{k-1}^T<br><br>e_k\right),$

wherein Δ

{circumflex over (θ

)} is a parameter update, ψ

is a filtered regressor, e is an innovator, and k is an iteration sequence.

39. The mobile station as in claim 38, wherein the prediction error method is based on an innovations representation model of the pilot signal.

40. The mobile station as in claim 39, wherein the prediction error method finds optimum model parameters by minimizing a function of the one-step prediction error.

41. The mobile station as in claim 40, wherein the Gauss-Newton algorithm is used in finding a numerical solution for the function.

42. The method as in claim 41, wherein the parameters are adjusted when {circumflex over (d)}<

1 according to the following;

{circumflex over (θ

)}←

{circumflex over (θ

)}+α

Δ

{circumflex over (θ

)}.