Selection of a Received Sequence By Means of Metrics

• US 20090323868A1
• Filed: 06/21/2007
• Published: 12/31/2009
• Est. Priority Date: 06/22/2006
• Status: Active Grant

• Current Assignee: MediaTek, Inc.
• Original Assignee: MStar Semiconductor Incorporated (MediaTek, Inc.)
• Inventors: Alt, Daniel E.
• Granted Patent: US 8,379,775 B2
• Time in Patent Office: Days
• US Class Current: 375/343
• CPC Class Codes: H03M 13/3738 with judging correct decoding
• CPC Class Codes: H03M 13/41 using the Viterbi algorithm or Viterbi processors
• CPC Class Codes: H04L 1/0054 Maximum-likelihood or sequential decoding, e.g. Viterbi, Fano, ZJ algorithms
• CPC Class Codes: H04L 1/0059 Convolutional codes
• CPC Class Codes: H04L 1/0072 Error control for data other than payload data, e.g. control data

Abstract

A method of received data sequence assessment, the method comprising producing a correlation metric describing the fit to a received sequence of the most likely of a set of candidates for the received sequence and calculating an error metric indicative of discrepancies between the received sequence and its most likely candidate.

Claims

1. A method of received data sequence assessment, the method comprising:

producing a correlation metric describing a fit to a received sequence of a most likely of a set of candidates for the received sequence and calculating an error metric indicative of discrepancies between the received sequence and its most likely candidate.

2. A method according to claim 1, further comprising producing for each of one or more further received sequences:

a correlation metric describing the fit to the respective received sequence of the most likely of a set of candidates for the respective received sequence, and

an error metric indicative of discrepancies between the respective received sequence and its most likely candidate.

3. A method according to claim 2, further comprising determining which of the received sequences has the best correlation metric and opting to perform signal processing operations on a signal associated with that received sequence if that sequence has a satisfactory error metric, wherein a received sequence has a satisfactory error metric if its error metric betters a threshold.

4. A method according to claim 2, further comprising determining which of the received sequences have satisfactory error metrics and then determining which of the received sequences having satisfactory error metrics has the best correlation metric and opting to perform signal processing operations on a signal associated with that received sequence, wherein a received sequence has a satisfactory error metric if its error metric betters a threshold.

5. A method according to claim 3, wherein correlation metrics are compared to one another in a manner avoiding bias due to differing power levels in communications providing the received sequences.

6. A method according to claim 5, wherein bias avoidance is achieved by comparing correlation metrics that are normalised in terms of the estimated signal strength of the underlying received sequences.

7. A method according to claim 6, wherein the normalisation is achieved through multiplying the correlation metrics to be compared by each other'"'"'s corresponding received sequence'"'"'s estimated signal strength.

8. A method of received data sequence assessment, the method comprising producing for each of a plurality of received data sequences a respective correlation metric, each correlation metric describing a fit to its respective received sequence of a most likely of a set of candidates, normalising the correlation metrics, determining the received sequence with the best correlation metric and opting to perform signal processing operations on a signal associated with that received sequence if that sequence'"'"'s correlation metric betters a threshold.

9. A method of received data sequence assessment, the method comprising producing for each of a plurality of received data sequences a respective correlation metric, each correlation metric describing a fit to its respective received sequence of a most likely of a set of candidates, normalising the correlation metrics, determining the best of the correlation metrics that better a threshold and opting to perform signal processing operations on a signal associated with that received sequence.