Pseudo noise sequence acquisition in spread spectrum systems

1. A method for signal analysis, executed by a decoder, the method comprising:

digitizing, by the decoder, a signal modulated by a pseudo noise (PN) sequence;

dividing, by the decoder, the digitized signal into a plurality of sample blocks; and

estimating, by the decoder, a PN phase embedded in a sample block of the plurality of sample blocks using an iterative message passing algorithm (iMPA) executed on a primary graphical model augmented with an auxiliary model, wherein the primary graphical model and the auxiliary model are each represented by a respective generator polynomial, and wherein the primary graphical model and the auxiliary model each represent the PN sequence.

2. The method of claim 1 wherein the estimated PN phase is made available to a user.

3. The method of claim 1 wherein the primary graphical model is represented by a generator polynomial, g(D)=D 22 +D 1 +D 0 , and the auxiliary model is represented by g(D)=D 22×2^n +D 2 ×2^n ×D 0 , wherein n is any integer greater than or equal to 0.

4. The method of claim 1 wherein the primary model and the auxiliary model are based on a same generator polynomial.

5. The method of claim 1 wherein the iMPA uses a forward backward algorithm.

6. The method of claim 1 wherein the auxiliary model generates a same maximal-length sequence (m-sequence) as an m-sequence generated by the primary graphical model.

7. The method of claim 1 further comprising receiving the signal from a source in a spread spectrum system.

8. The method of claim 1 further comprising storing the digitized signal.

9. The method of claim 1 further comprising storing each sample block of the plurality of sample blocks.

10. The method of claim 1 further comprising extrapolating the estimated PN phase over the plurality of sample blocks.

11. The method of claim 10 further comprising statistically comparing the extrapolated sequence with the digitized signal.

12. The method of claim 11 wherein the statistical comparison is correlation.

13. The method of claim 12 wherein the estimated PN phase is considered satisfactory if a correlation value is greater than a threshold.

14. A system comprising:

an analog-to-digital converter configured to digitize a signal;

a channel metric access unit to divide the signal into a plurality of sample blocks; and

hardware architecture configured to estimate a PN phase embedded in a sample block of the plurality of sample blocks using an iterative message passing algorithm (iMPA) executed on a primary graphical model augmented with an auxiliary model, wherein the primary graphical model and the auxiliary model are each represented by a respective generator polynomial, and wherein the primaryaphical model and the auxiliary model each represent the PN sequence.

15. The system of claim 14 further comprising a receiver configured to receive the signal from a source in a spread spectrum system.

16. The system of claim 14 further comprising:

a first storage unit configured to store the digitized signal; and

a second storage unit configured to store each sample block of the plurality of sample blocks.

17. The system of claim 14 further comprising an extrapolation unit configured to extrapolate the estimated PN phase over the plurality of sample blocks.

18. The system of claim 17 further comprising a verification unit configured to statistically compare the extrapolated sequence with the digitized signal, wherein the verification unit is further configured to perform correlation on the extrapolated sequence and the digitized signal.

19. The system of claim 14 , wherein the primary graphical model is represented by the generator polynomial, g(D)=D 22 +D 1 +D 0 , and the auxiliary model represented by g(D)=D 22×2^n +D 2×2^n +D 0 , wherein n is any integer greater than or equal to 0.

20. The system of claim 14 , wherein the auxiliary model generates a same maximal-length sequence (m-sequence) as an m-sequence generated by the primary graphical model.