Method and apparatus for joint source-channel map decoding

• US 20050275573A1
• Filed: 05/02/2005
• Published: 12/15/2005
• Est. Priority Date: 05/06/2004
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Raveendran, Vijayalakshmi R.
• Granted Patent: US 7,882,421 B2
• Time in Patent Office: Days
• US Class Current: 341/120
• CPC Class Codes: H03M 13/2927 Decoding strategies
• CPC Class Codes: H03M 13/293 with erasure setting
• CPC Class Codes: H03M 13/2936 comprising an outer Reed-Solomon code and an inner convolutional code
• CPC Class Codes: H03M 13/2957 Turbo codes and decoding
• CPC Class Codes: H03M 13/3916 for block codes using a trellis or lattice
• CPC Class Codes: H03M 13/45 Soft decoding, i.e. using symbol reliability information H03M13/41 takes precedence
• CPC Class Codes: H03M 13/63 Joint error correction and other techniques H03M13/31 and H03M13/33 take precedence
• CPC Class Codes: H03M 13/6312 Error control coding in combination with data compression
• CPC Class Codes: H03M 13/6318 using variable length codes
• CPC Class Codes: H04L 1/0045 Arrangements at the receiver end
• CPC Class Codes: H04L 1/0055 MAP-decoding
• CPC Class Codes: H04L 1/0057 Block codes H04L1/0061, H04L1/0064 take precedence
• CPC Class Codes: H04L 1/0065 Serial concatenated codes
• CPC Class Codes: H04L 1/0066 Parallel concatenated codes

Abstract

A method for error correction that includes receiving a bitstream, the bitstream comprising one or more bits, determining if the bitstream has one or more corrupt bits, determining one or more hypotheses representing an error pattern, and assigning a probability to each of the hypotheses, wherein the probability is determined based on one or more reference data.

Claims

1. A method for error correction, comprising:

receiving a bitstream, the bitstream comprising one or more bits;

determining if the bitstream has one or more corrupt bits;

determining one or more hypotheses representing an error pattern; and

assigning a probability to each of the hypotheses, wherein the probability is determined based on one or more reference data.

2. The method of claim 1, further comprising:

decoding uncorrupt bits; and

wherein the reference data comprises one or more of the decoded uncorrupt bits.

3. The method of claim 1, further comprising:

determining an a priori probability distribution function based on measuring channel conditions; and

wherein the reference data comprises the a priori probability distribution function.

4. The method of claim 1, further comprising:

decoding uncorrupt bits in one or more frames; and

wherein the reference data comprises one or more of the decoded uncorrupt bits.

5. The method of claim 1, further comprising:

decoding uncorrupt bits in a frame containing the corrupt bits; and

wherein the reference data comprises one or more of the decoded uncorrupt bits.

6. The method of claim 1, further comprising:

decoding uncorrupt bits;

determining that one of the hypotheses leads to a syntax or semantic failure within the uncorrupt bits; and

assigning a probability of zero to the hypothesis leading to the failure.

7. The method of claim 1, wherein determining the hypotheses further comprises:

determining the hypotheses for a group of bits making up a coded symbol.

8. The method of claim 1, wherein determining the hypotheses further comprises:

determining the hypotheses for a group of bits making up a coded symbol, wherein the coded symbol is chosen from the group consisting of a macroblock type, a quantization parameter, an intra prediction direction, a motion vector, a DC coefficient and an AC coefficient.

9. The method of claim 1, further comprising:

decoding uncorrupt bits in one or more slices; and

wherein the reference data comprises one or more of the decoded uncorrupt bits.

10. The method of claim 1, further comprising:

determining a probability distribution for an incorrectly received symbol using information chosen from the group consisting of a bit-error-streak distance, an error burst length, an average number of bit errors for erased packets, neighboring bytes with no bit errors, user data fields and error characteristics of received packets; and

wherein the reference data comprises the probability distribution.

11. The method of claim 1, further comprising:

receiving the bitstream over a wireless network.

27. The electronic device of claim 1, further configured to decode uncorrupt bits in a frame containing the corrupt bits, and wherein the reference data comprises one or more of the decoded uncorrupt bits.

12. An apparatus for error correction, comprising:

means for receiving a bitstream, the bitstream comprising one or more bits;

means for determining if the bitstream has one or more corrupt bits;

means for determining one or more hypotheses representing an error pattern; and

means for assigning a probability to each of the hypotheses, wherein the probability is determined based on one or more reference data.

13. The apparatus of claim 12, further comprising:

means for decoding uncorrupt bits; and

wherein the reference data comprises one or more of the decoded uncorrupt bits.

14. The apparatus of claim 12, further comprising:

means for determining an a priori probability distribution function based on measuring channel conditions; and

wherein the reference data comprises the a priori probability distribution function.

15. The apparatus of claim 12, further comprising:

means for decoding uncorrupt bits in one or more frames; and

wherein the reference data comprises one or more of the decoded uncorrupt bits.

16. The apparatus of claim 12, further comprising:

means for decoding uncorrupt bits in a frame containing the corrupt bits; and

wherein the reference data comprises one or more of the decoded uncorrupt bits.

17. The apparatus of claim 12, further comprising:

means for decoding uncorrupt bits;

means for determining that one of the hypotheses leads to a syntax or semantic failure within the uncorrupt bits; and

means for assigning a probability of zero to the hypothesis leading to the failure.

18. The apparatus of claim 12, wherein determining the hypotheses further comprises:

means for determining the hypotheses for a group of bits making up a coded symbol.

19. The apparatus of claim 12, wherein determining the hypotheses further comprises:

means for determining the hypotheses for a group of bits making up a coded symbol, wherein the coded symbol is chosen from the group consisting of a macroblock type, a quantization parameter, an intra prediction direction, a motion vector, a DC coefficient and an AC coefficient.

20. The apparatus of claim 12, further comprising:

means for decoding uncorrupt bits in one or more slices; and

wherein the reference data comprises one or more of the decoded uncorrupt bits.

21. The apparatus of claim 12, further comprising:

means for determining a probability distribution for an incorrectly received symbol using information chosen from the group consisting of a bit-error-streak distance, an error burst length, an average number of bit errors for erased packets, neighboring bytes with no bit errors, user data fields and error characteristics of received packets; and

wherein the reference data comprises the probability distribution.

22. The apparatus of claim 12, further comprising:

means for receiving the bitstream over a wireless network.

23. An electronic device for error correction, the electronic device configured to receive a bitstream, the bitstream comprising one or more bits, to determine if the bitstream has one or more corrupt bits, to determine one or more hypotheses representing an error pattern, and to assign a probability to each of the hypotheses, wherein the probability is determined based on one or more reference data.

24. The electronic device of claim 23, further configured to decode uncorrupt bits, and

wherein the reference data comprises one or more of the decoded uncorrupt bits.

25. The electronic device of claim 23, further configured to determine an a priori probability distribution function based on measuring channel conditions, and wherein the reference data comprises the a priori probability distribution function.

26. The electronic device of claim 23, further configured to decode uncorrupt bits in one or more frames, and wherein the reference data comprises one or more of the decoded uncorrupt bits.

28. The electronic device of claim 23, further configured to decode uncorrupt bits, to determine that one of the hypotheses leads to a syntax or semantic failure within the uncorrupt bits, and to assign a probability of zero to the hypothesis leading to the failure.

29. The electronic device of claim 23, further configured to determine the hypotheses for a group of bits making up a coded symbol.

30. The electronic device of claim 23 further configured to determine the hypotheses for a group of bits making up a coded symbol, wherein the coded symbol is chosen from the group consisting of a macroblock type, a quantization parameter, an intra prediction direction, a motion vector, a DC coefficient and an AC coefficient.

31. The electronic device of claim 23, further configured to decode uncorrupt bits in one or more slices, and wherein the reference data comprises one or more of the decoded uncorrupt bits.

32. The electronic device of claim 23, further configured to determine a probability distribution for an incorrectly received symbol using information chosen from the group consisting of a bit-error-streak distance, an error burst length, an average number of bit errors for erased packets, neighboring bytes with no bit errors, user data fields and error characteristics of received packets, and wherein the reference data comprises the probability distribution.

33. The electronic device of claim 23, further configured to receive the bitstream over a wireless network.

34. A computer readable medium embodying means for causing a computer to execute a method comprising:

receiving a bitstream, the bitstream comprising one or more bits;

determining if the bitstream has one or more corrupt bits;

determining one or more hypotheses representing an error pattern; and

assigning a probability to each of the hypotheses, wherein the probability is determined based on one or more reference data.