Method and apparatus for pilot estimation using prediction error method
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
H04B-015/00
H04K-001/00
H04L-027/30
H04L-027/26
출원번호
US-0279535
(2002-10-23)
발명자
/ 주소
Abrishamkar,Farrokh
Kreutz Delgado,Kenneth
출원인 / 주소
Qualcomm Incorporated
인용정보
피인용 횟수 :
1인용 특허 :
18
초록▼
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
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 pilot estimator that includes a Kalman filter to produce a pilot estimate. The Kalman filter is determined through use of a prediction error method based on an innovations representation of the original pilot signal. 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.
대표청구항▼
The invention claimed is: 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 a pilot signal from the CDMA signal; and estimating the original pilot signal using a pilot estim
The invention claimed is: 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 a pilot signal from the CDMA signal; and estimating the original pilot signal using a pilot estimator that includes a Kalman filter to produce a pilot estimate, wherein the Kalman filter is determined through use of a prediction error method based on an innovations representation of the original pilot signal, and wherein the Kalman filter is further configured to calculate the filtered estimate according to the following: where: {circumflex over (x)}k+ is the filtered estimate; {circumflex over (x)}k is a single-step predictor; ek is the approximate innovations; and {circumflex over (L)},창 are estimated parameters. 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 is configured by an offline system identification process. 6. The method as in claim 5, wherein the Kalman filter is configured for improved group delay. 7. The method as in claim 5, wherein the offline system identification process comprises: providing training samples; and calculating parameters using the prediction error method and pseudo linear regression and generating a state estimate using the Kalman filter, wherein the calculating and generating are iteratively performed until the Kalman filter converges. 8. The method as in claim 7, wherein the parameters are calculated according to the following: where: {circumflex over (θ)} represents the parameters; {circumflex over (φ)}k-1 is a state estimate from the Kalman filter; {circumflex over (φ)}k-1T is the transposed state estimate from the Kalman filter; and yk is the received pilot signal. 9. The method as in claim 5, wherein the offline system identification process comprises: providing training samples; and calculating parameters using the 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. 10. The method as in claim 9, wherein the parameters are calculated according to the following: where: Δ{circumflex over (θ)} represents the parameters; ψk-1 is a state estimate from the Kalman filter; ψk-1T is the transposed state estimate from the Kalman filter; and ek is the approximate innovations. 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 a pilot signal from the CDMA signal; and estimating the original pilot signal using a pilot estimator that includes a Kalman filter to produce a pilot estimate, wherein the Kalman filter is determined through use of a prediction error method based on an innovations representation of the original pilot signal, and wherein the Kalman filter is further configured to calculate the filtered estimate according to the following: where: {circumflex over (x)}k+ is the filtered estimate; {circumflex over (x)}k is a single-step predictor; ek is the approximate innovations; and {circumflex over (L)},창 are estimated parameters. 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. The method as in claim 11, wherein the Kalman filter is configured by an offline system identification process. 15. The method as in claim 11, wherein the Kalman filter is configured for improved group delay. 16. The method as in claim 14, wherein the offline system identification process comprises: providing training samples; and calculating parameters using the prediction error method and pseudo linear regression and generating a state estimate using the Kalman filter, wherein the calculating and generating are iteratively performed until the Kalman filter converges. 17. The method as in claim 16, wherein the parameters are calculated according to the following: where: {circumflex over (θ)} represents the parameters; {circumflex over (φ)}k-1 is a state estimate from the Kalman filter; {circumflex over (φ)}k-1T is the transposed state estimate from the Kalman filter; and yk is the received pilot signal. 18. The method as in claim 14, wherein the offline system identification process comprises: providing training samples; and calculating parameters using the 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. 19. The method as in claim 18, wherein the parameters are calculated according to the following: where: Δ{circumflex over (θ)} represents the parameters; ψk-1 is a state estimate from the Kalman filter; ψk-1T is the transposed state estimate from the Kalman filter; and ek is the approximate innovations. 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; a pilot estimation component in electronic communication with the front-end processing and despreading component for estimating the original pilot signal using a pilot estimator that includes a Kalman filter to produce a pilot estimate, wherein the Kalman filter is determined through use of a prediction error method based on an innovations representation of the original pilot signal, and wherein the Kalman filter is further configured to calculate the filtered estimate according to the following: where: {circumflex over (x)}k+ is the filtered estimate; {circumflex over (x)}k is a single-step predictor; ek is the approximate innovations; and {circumflex over (L)},창 are estimated parameters; 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. 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. The mobile station as in claim 20, wherein the Kalman filter is configured by an offline system identification process. 23. The mobile station as in claim 22, wherein the Kalman filter is configured for improved group delay. 24. The mobile station as in claim 22, wherein the offline system identification process comprises: providing training samples; and calculating parameters using the prediction error method and pseudo linear regression and generating a state estimate using the Kalman filter, wherein the calculating and generating are iteratively performed until the Kalman filter converges. 25. The mobile station as in claim 24, wherein the parameters are calculated according to the following: where: {circumflex over (θ)} represents the parameters; {circumflex over (φ)}k-1 is a state estimate from the Kalman filter; {circumflex over (φ)}k-1T is the transposed state estimate from the Kalman filter; and yk is the received pilot signal. 26. The mobile station as in claim 22, wherein the offline system identification process comprises: providing training samples; and calculating parameters using the 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. 27. The mobile station as in claim 26, wherein the parameters are calculated according to the following: where: Δ{circumflex over (θ)} represents the parameters; ψk-1 is a state estimate from the Kalman filter; ψk-1T is the transposed state estimate from the Kalman filter; and ek is the approximate innovations.
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이 특허에 인용된 특허 (18)
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