Detecting a data frame
View Patent ↗A detector ( 109 ) and corresponding method is configured for detecting presence of a data frame. The detector includes a first correlator ( 123 ) that is configured to provide a first plurality of correlations corresponding to similarity between data as received and first information, e.g., an ordered set of coefficients, that denotes the data frame and a second correlator ( 125 ) that is configured to provide a second plurality of correlations corresponding to similarity between the data as received and second information, e.g., another ordered set of coefficients, that denotes the data frame, where the second information includes a portion of the first information. Further included is decision logic ( 129 ) coupled to the first correlator and the second correlator, where the decision logic is configured to provide an indication ( 111 ) that the data frame is present based on the first plurality of correlations and the second plurality of correlations.
1. A detector configured for detecting presence of a data frame, the detector comprising:
a first correlator configured to provide a first plurality of correlations corresponding to similarity between data as received and first information that denotes the data frame;
a second correlator configured to provide a second plurality of correlations corresponding to similarity between the data as received and second information that denotes the data frame, the second information including a portion of the first information; and
decision logic coupled to the first correlator and the second correlator, the decision logic configured to provide an indication that the data frame is present where the indication is based on the first plurality of correlations and the second plurality of correlations.
2. The detector of claim 1 wherein the first correlator further comprises:
a buffer with a multiplicity of stages configured to store a corresponding multiplicity of samples of the data as received;
a multiplicity of multipliers configured to multiply individual samples of the multiplicity of samples by a corresponding portion of the first information to provide partial correlations; and
a summer configured to sum the partial correlations to provide a sum corresponding to a correlation of the multiplicity of samples and at least a portion of the first information.
3. The detector of claim 1 wherein the second correlator further comprises:
a buffer with a multiplicity of stages configured to store a corresponding multiplicity of samples of the data as received;
a multiplicity of multipliers configured to multiply individual samples of the multiplicity of samples by a corresponding portion of the second information to provide partial correlations; and
a summer configured to sum the partial correlations to provide a sum corresponding to a correlation of the multiplicity of samples and at least a portion of the second information.
4. The detector of claim 1 wherein the first correlator and the second correlator collectively further comprise:
a first in first out (FIFO) buffer with a multiplicity of stages configured to store a corresponding multiplicity of samples of the data as received and to shift the samples whenever a new sample is loaded into the FIFO buffer;
a multiplicity of multipliers configured to first multiply each sample of the multiplicity of samples by a corresponding first coefficient of a first multiplicity of coefficients included with the first information to provide first partial correlations and second multiply the each sample of the multiplicity of samples by a corresponding second coefficient of a second multiplicity of coefficients included with the second information to provide second partial correlations, wherein the first multiply to provide the first partial correlations and the second multiply to provide the second partial correlations occur whenever the new sample is loaded into the buffer; and
a summer configured to sum the first partial correlations to provide a first sum corresponding to a correlation of the multiplicity of samples and the first multiplicity of coefficients and to sum the second partial correlations to provide a second sum corresponding to a correlation of the multiplicity of samples and the second multiplicity of coefficients, wherein the first sum and the second sum are provided whenever a new sample is loaded into the buffer.
5. The detector of claim 4 wherein the multiplicity of multipliers further comprises:
a first group of multipliers configured to provide the first partial correlations; and
a second group of multipliers configured to provide the second partial correlations; and
wherein the summer further comprises:
a first summer configured to provide the first sum; and
a second summer configured to provide the second sum.
6. The detector of claim 5 wherein:
the first group of multipliers is coupled to the first multiplicity of coefficients and the second group of multipliers is coupled to the second multiplicity of coefficients, wherein the first information further comprises the first multiplicity of coefficients with the second multiplicity of coefficients appended thereto and the second information further comprises the second multiplicity of coefficients with the first multiplicity of coefficients appended thereto; and
the first summer is configured to provide the first sum to a second FIFO buffer and a third summer and the second summer is configured to provide the second sum to a third FIFO buffer and a fourth summer, an output of the second FIFO buffer coupled to the fourth summer and an output of the third FIFO buffer coupled to the fourth summer, wherein the third summer is configured to provide the first plurality of correlations and the fourth summer is configured to provide the second plurality of correlations.
7. The detector of claim 1 wherein the first correlator and the second correlator collectively further comprise:
a first in first out (MO) buffer with a multiplicity of stages configured to store a corresponding multiplicity of samples of the data as received and to shift the samples whenever a new sample is loaded into the FIFO buffer;
a multiplicity of multipliers configured to first multiply each sample of a first portion of the multiplicity of samples by a corresponding first coefficient of a first multiplicity of coefficients included with the first information to provide first partial correlations, second multiply each sample of a second portion of the multiplicity of samples by a corresponding second coefficient of a second multiplicity of coefficients included with the second information to provide second partial correlations, and third multiply each sample of a third portion of the multiplicity of samples by a corresponding third coefficient of a third multiplicity of coefficients included with each of the first information and the second information to provide third partial correlations, wherein the first multiply to provide the first partial correlations, the second multiply to provide the second partial correlations, and the third multiply to provide the third partial correlations occur whenever the new sample is loaded into the buffer; and
a summer configured to sum the first partial correlations and the third partial correlations to provide a first sum corresponding to a correlation of the multiplicity of samples and the first information and to sum the second partial correlations and the third partial correlations to provide a second sum corresponding to a correlation of the multiplicity of samples and the second information, wherein the first sum and the second sum are provided whenever a new sample is loaded into the buffer.
8. The detector of claim 1 wherein the decision logic further comprises a peak detector configured to find first correlation peaks from the first plurality of correlations that are separated by a predetermined time period and second correlation peaks from the second plurality of correlations that are separated by the predetermined time period.
9. The detector of claim 8 wherein the decision logic further comprises a comparator for determining whether any of the first correlation peaks and the second correlation peaks satisfy a threshold.
10. The detector of claim 8 wherein the decision logic further comprises a window function for determining whether the first correlation peaks and the second correlation peaks fall within a time window around the predetermined time period.
11. A method of detecting presence of a data frame in data samples as received, the method comprising:
correlating, in a correlator, the data samples with a first ordered set of coefficients corresponding to a known symbol sequence over a plurality of symbol periods to provide a sequence of first correlations over each symbol period, the known symbol sequence associated with the data frame being present;
correlating the data samples with a second ordered set of coefficients over a portion of the plurality of symbol periods to provide a sequence of second correlations over the each symbol period, the second ordered set of coefficients including a portion of the first ordered set of coefficients;
selecting a strongest first correlation from the sequence of first correlations for each symbol period and a strongest second correlation from the sequence of second correlations for each symbol period; and
determining whether the data frame is present based on the strongest first correlation with associated first location index and the strongest second correlation with associated second location index for each symbol period.
12. The method of claim 11 further comprising correlating the data samples with one or more additional ordered sets of coefficients over the portion of the plurality of symbol periods to provide one or more additional sequences of additional correlations over the each symbol period, the one or more additional ordered sets of coefficients including a portion of the first ordered set of coefficients.
13. The method of claim 11 wherein the correlating the data samples with a second ordered set of coefficients further comprises correlating the data samples with a second ordered set of coefficients where the second ordered set of coefficients comprises a later portion of the first ordered set of coefficients for a first symbol combined with an earlier portion of the first ordered set of coefficients for a second symbol.
14. The method of claim 11 wherein the correlating the data samples with a second ordered set of coefficients further comprises correlating the data samples with a second ordered set of coefficients where the second ordered set of coefficients comprises the first ordered set of coefficients as circularly shifted.
15. The method of claim 11 further comprising sequentially shifting the data samples through a first in first out (FIFO) buffer with a multiplicity of stages and wherein:
the correlating the data samples with a first ordered set of coefficients further comprises correlating data samples within the FIFO buffer with at least a portion of the first ordered set of coefficients to provide a first correlation corresponding to the sequence of first correlations; and
the correlating the data samples with a second ordered set of coefficients further comprises correlating the data samples within the FIFO buffer with at least a portion of the second ordered set of coefficients to provide a second correlation corresponding to the sequence of second correlations.
16. The method of claim 11 wherein the determining whether the data frame is present further comprises determining whether a magnitude of the strongest first correlation satisfies a threshold and whether a magnitude of the strongest second correlation satisfies a threshold for one or more of the each symbol period.
17. The method of claim 11 wherein the determining whether the data frame is present further comprises determining whether the timing between successive strongest first correlations and successive strongest second correlations is within a timing window about a symbol period.
18. The method of claim 17 wherein the determining whether the data frame is present further comprises counting the occurrences of successive strongest first correlations and successive strongest second correlations being within the timing window and deciding that the data frame is present when the number of occurrences satisfies a count threshold.
19. A detector configured for detecting presence of a data frame and providing symbol synchronization, the detector comprising:
a first correlator configured to provide a first sequence of correlations between samples of data as received and a first symbol that denotes the data frame;
a second correlator configured to provide a second sequence of correlations between the samples of data as received and a second symbol that denotes the data frame, wherein the second symbol includes a portion of the first symbol; and
a decision function, coupled to the first correlator and the second correlator, configured to provide a presence indication when the data frame is present based on the first sequence of correlations and the second sequence of correlations.
20. The detector of claim 19 further comprising one or more additional correlators configured to provide one or more additional sequences of correlations between the samples of data as received and one or more additional symbols that denote the data frame, wherein the one or more additional symbols include a portion of the first symbol.
21. The detector of claim 20 wherein the first and second correlators corresponds to first and second despreaders and the data frame further corresponds to a data frame in a direct sequence spread spectrum (DSSS) system.