Claims
- 1. In a wireless communication system, a method comprising:
receiving a CDMA signal; despreading the CDMA signal; obtaining a pilot signal from the CDMA signal; and estimating an original pilot signal using a Kalman filter to produce a pilot estimate.
- 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. The method as in claim 1, wherein the Kalman filter was configured by an offline system identification process.
- 6. The method as in claim 5, wherein the offline system identification process comprises:
providing training samples; and calculating parameters using a maximum likelihood parameter estimation and generating a state estimate using the Kalman filter, wherein the calculating and generating are iteratively performed until the Kalman filter converges.
- 7. The method as in claim 6, wherein the maximum likelihood parameter estimation performs calculations including:
- 8. In a mobile station for use in a wireless communication system, a method comprising:
receiving a CDMA signal; despreading the CDMA signal; obtaining a pilot signal from the CDMA signal; and estimating an original pilot signal using a Kalman filter to produce a pilot estimate.
- 9. The method as in claim 8, wherein the CDMA signal is transmitted on a downlink and wherein the downlink comprises a pilot channel.
- 10. The method as in claim 8, further comprising demodulating the pilot estimate.
- 11. The method as in claim 8, wherein the Kalman filter was configured by an offline system identification process.
- 12. The method as in claim 11, wherein the offline system identification process comprises:
providing training samples; and calculating parameters using a maximum likelihood parameter estimation and generating a state estimate using the Kalman filter, wherein the calculating and generating are iteratively performed until the Kalman filter converges.
- 13. The method as in claim 12, wherein the maximum likelihood parameter estimation performs calculations including:
- 14. A mobile station for use in a wireless communication system, 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; a pilot estimation component 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; 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.
- 15. The mobile station as in claim 14, wherein the receiver receives the CDMA signal transmitted on a downlink and wherein the downlink comprises a pilot channel.
- 16. The mobile station as in claim 14, wherein the Kalman filter was configured by an offline system identification process.
- 17. The mobile station as in claim 16, wherein the offline system identification process comprises:
providing training samples; and calculating parameters using a maximum likelihood parameter estimation and generating a state estimate using the Kalman filter, wherein the calculating and generating are iteratively performed until the Kalman filter converges.
- 18. The-mobile station as in claim 17, wherein the maximum likelihood parameter estimation performs calculations including:
- 19. 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 has converged, iteratively performing the following steps:
calculating new parameters using a maximum likelihood parameter estimation; and generating a new state estimate using the Kalman filter.
- 20. The method as in claim 19, wherein the maximum likelihood parameter estimation performs calculations including:
- 21. A mobile station for use in a wireless communication system, 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; and means for estimating an original pilot signal using a Kalman filter to produce a pilot estimate.
- 22. The mobile station as in claim 21, wherein the CDMA signal is transmitted on a downlink and wherein the downlink comprises a pilot channel.
- 23. The mobile station as in claim 21, further comprising means for demodulating the pilot estimate.
- 24. The mobile station as in claim 21, wherein the Kalman filter was configured by an offline system identification process.
- 25. The mobile station as in claim 24, wherein the offline system identification process comprises:
means for providing training samples; and means for calculating parameters using a maximum likelihood parameter estimation and means for generating a state estimate using the Kalman filter, wherein the calculating and generating are iteratively performed until the Kalman filter converges.
- 26. The mobile station as in claim 25, wherein the maximum likelihood parameter estimation performs calculations including:
RELATED APPLICATIONS
[0001] 1. Claim of Priority under 35 U.S.C. §119(e)
[0002] The present Application for Patent claims priority of U.S. Provisional Application No. 60/386,840, Jun. 5, 2002, assigned to the assignee hereof and hereby expressly incorporated by reference herein.
[0003] 2. Reference to Co-Pending Applications for Patent
[0004] The present invention is related to the following Applications for Patent in the U.S. Patent & Trademark Office:
[0005] “Method and Apparatus for Pilot Estimation Using a Wiener Filter” by Farrokh Abrishamkar et al, having Attorney Docket No. 020099, filed concurrently herewith and assigned to the assignee hereof, and which is expressly incorporated by reference herein;
[0006] “Method And Apparatus For Pilot Estimation Using A Prediction Error Method With A Kalman Filter And Pseudo-Linear Regression”, by Farrokh Abrishamkar et al., having Attorney Docket No. 020201, filed concurrently herewith and assigned to the assignee hereof;
[0007] “Method And Apparatus For Pilot Estimation Using A Prediction Error Method With A Kalman Filter And A Gauss-Newton Algorithm”, by Farrokh Abrishamkar et al., having Attorney Docket No. 020205, filed concurrently herewith and assigned to the assignee hereof; and
[0008] “Method And Apparatus For Pilot Estimation Using An Adaptive Prediction Error Method With a Kalman Filter and A Gauss-Newton Algorithm,” by Farrokh Abrishamkar et al., having Attorney Docket No. 020232, filed concurrently herewith and assigned to the assignee hereof.
Provisional Applications (1)
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Number |
Date |
Country |
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60386840 |
Jun 2002 |
US |