Relative timing acquisition for a radio communications system

• US 6,801,589 B1
• Filed: 11/30/2000
• Issued: 10/05/2004
• Est. Priority Date: 11/30/2000
• Status: Expired due to Fees

• Current Assignee: Intel Corporation
• Original Assignee: ArrayComm LLC (Ygomi LLC)
• Inventors: Dogan, Mithat C.
• Primary Examiner(s): Chin, Stephen
• Assistant Examiner(s): Pathak, Sudhanshu C.
• Application Number:
  US09/727,177
• Time in Patent Office: 1,405 Days
• Field of Search: 375/347, 375/324, 375/326, 375/343, 375/267, 375/354, 375/356, 370/334, 370/310
• US Class Current: 375/343
• CPC Class Codes: H04B 7/0851 using training sequences or error signal minimizing error signal H04B7/0854
• CPC Class Codes: H04L 27/18 Phase-modulated carrier systems, i.e. using phase-shift keying H04L27/32 takes precedence

Abstract

A method and apparatus are provided that allows a coarse timing approximation to be determined from analyzing only a portion of a received burst. The coarse timing can be refined by focusing on the coarse timing approximation. According to one aspect of the present invention, the invention includes receiving a burst having a known repeating core training sequence, selecting an analysis window to be substantially the same size as a multiple of a single repetition of the core training sequence, and over sampling the received burst for the portion overlapping the analysis window. The invention further includes calculating a cross correlation vector for a portion of the samples with respect to a selected part of the training sequence, each cross correlation vector corresponding to a relative timing hypothesis and each cross correlation vector combining samples that occur at intervals within the analysis window, the intervals corresponding to the period of the repeating core training sequence, calculating a least squares fit for each hypothesis using the calculated cross correlation vectors; and selecting the combination of samples corresponding to the minimal least squares fit as the relative timing of the selected part of the repeating core training sequence.

Claims

1. A method comprising:

receiving a burst having a known repeating core training sequence;

selecting an analysis window to be substantially the same size as a multiple of a single repetition of the core training sequence;

sampling at least the portion of the received burst corresponding to the selected analysis window;

combining samples that occur at intervals within the analysis window, the intervals corresponding to the period of the repeating core training sequence; and

determining the relative timing of the received burst from the combined samples.

2. The method of claim 1, wherein combining the samples comprises adding the samples.

3. The method of claim 1, wherein determining the relative timing includes calculating cross correlations for a portion of the combined samples with respect to a selected part of the core training sequence.

4. The method of claim 3, wherein determining the relative timing comprises minimizing a cost function using the cross correlations.

5. The method of claim 3, wherein receiving a burst comprises receiving the burst at a plurality of antennas as a plurality of received signals, and wherein calculating cross correlations comprises calculating cross correlation vectors determined from the received signals.

6. The method of claim 5, wherein each cross correlation vector corresponds to a relative timing hypothesis and wherein determining the relative timing comprises calculating a least squares fit for each hypothesis using the calculated cross correlation vectors.

7. The method of claim 6, wherein calculating a least squares fit comprises comparing a hypothetical received sequence to the known sequence for each hypothesis.

8. The method of claim 3, wherein calculating cross correlations comprises calculating a cross correlation for a portion of evenly spaced ones of the samples.

9. The method of claim 1, wherein determining the relative timing comprises minimizing a cost function of the combined samples and selecting a combination of samples corresponding to the minimum cost.

10. The method of claim 9, wherein minimizing a cost function comprises minimizing a least squares cost function.

11. The method of claim 9, further comprising determining the fine timing of the selected samples by applying a timing estimation algorithm to an interpolated sequence of the selected samples.

12. The method of claim 9, further comprising refining the determined relative timing by:

selecting a sample proximate a sample of the selected combination of samples;

calculating cross correlations for the selected sample;

minimizing a second cost function for the cross correlations of the selected sample; and

selecting the sample corresponding to the minimal cost of the second cost function as the refined relative timing of a corresponding symbol of the repeating core training sequence.

13. The method of claim 12, wherein selecting a sample comprises selecting an immediately preceding sample.

14. The method of claim 12, wherein selecting a sample comprises identifying the combination of samples corresponding to the second cost function and selecting a sample between the first cost function selection and the second cost function selection.

15. A machine-readable medium having stored thereon data representing sequences of instructions which, when executed by a machine, cause the machine to perform operations comprising:

receiving a burst having a known repeating core training sequence;

selecting an analysis window to be substantially the same size as a multiple of a single repetition of the core training sequence;

sampling at least the portion of the received burst corresponding to the selected analysis window;

combining samples that occur at intervals within the analysis window, the intervals corresponding to the period of the repeating core training sequence; and

determining the relative timing of the received burst from the combined samples.

16. The medium of claim 15, wherein the instructions causing the machine to perform operations comprising combining the samples further comprise instructions for adding the samples.

17. The medium of claim 15, wherein the instructions causing the machine to perform operations comprising determining the relative timing further comprise instructions for calculating cross correlations for a portion of the combined samples with respect to a selected part of the core training sequence.

18. The medium of claim 17, wherein the instructions causing the machine to perform operations comprising determining the relative timing further comprise instructions for minimizing a least squares cost function using the cross correlations.

19. The medium of claim 15, further comprising instructions for determining the fine timing of the selected sample by applying a timing estimation algorithm to an interpolated sequence of the samples.

20. The medium of claim 15, further comprising instructions for refining the selected relative timing by:

selecting a combination of samples corresponding to the minimum cost;

selecting a sample proximate a sample of the selected combination of samples;

calculating cross correlations for the selected sample;

minimizing a second cost function for the cross correlations of the selected sample; and

selecting the sample corresponding to the minimal cost of the second cost function as the refined relative timing of a corresponding symbol of the repeating core training sequence.

21. An apparatus comprising:

means for receiving a burst having a known repeating core training sequence;

means for selecting an analysis window to be substantially the same size as a multiple of a single repetition of the core training sequence;

means for sampling at least the portion of the received burst corresponding to the selected analysis window;

means for combining samples that occur at intervals within the analysis window, the intervals corresponding to the period of the repeating core training sequence; and

means for determining the relative timing of the received burst from the combined samples.

22. The apparatus of claim 21, wherein the means for determining the relative timing includes means for calculating cross correlations for a portion of the combined samples with respect to a selected part of the core training sequence.

23. The apparatus of claim 22, wherein the means for determining the relative timing comprises means for minimizing a least squares cost function using the cross correlations.

24. The apparatus of claim 21, further comprising means for refining the selected relative timing by:

selecting a combination of samples corresponding to the minimum cost;

selecting a sample proximate a sample of the selected combination of samples;

calculating cross correlations for the selected sample;

minimizing a second cost function for the cross correlations of the selected sample; and

selecting the sample corresponding to the minimal cost of the second cost function as the refined relative timing of a corresponding symbol of the repeating core training sequence.