Systems and Methods for Advanced Iterative Decoding and Channel Estimation of Concatenated Coding Systems

• US 20140153625A1
• Filed: 12/03/2012
• Published: 06/05/2014
• Est. Priority Date: 12/03/2012
• Status: Active Grant

• Current Assignee: Ln2 Db LLC
• Original Assignee: Digital PowerRadio, LLC
• Inventors: Vojcic, Branimir R., Shayegh, Farnaz, Dogan, Hakan, Lee, Wookwon
• Granted Patent: US 9,191,256 B2
• Time in Patent Office: Days
• US Class Current: 375/224
• CPC Class Codes: G06F 11/1625 in communications, e.g. transmission, interfaces
• CPC Class Codes: H03M 13/09 Error detection only, e.g. using cyclic redundancy check [CRC] codes or single parity bit
• CPC Class Codes: H03M 13/1108 Hard decision decoding, e.g. bit flipping, modified or weighted bit flipping
• CPC Class Codes: H03M 13/1111 Soft-decision decoding, e.g. by means of message passing or belief propagation algorithms
• CPC Class Codes: H03M 13/112 with correction functions for the min-sum rule, e.g. using an offset or a scaling factor
• CPC Class Codes: H03M 13/1131 Scheduling of bit node or check node processing
• CPC Class Codes: H03M 13/114 Shuffled, staggered, layered or turbo decoding schedules
• CPC Class Codes: H03M 13/1154 Low-density parity-check convolutional codes [LDPC-CC]
• CPC Class Codes: H03M 13/1191 Codes on graphs other than LDPC codes
• CPC Class Codes: H03M 13/1515 Reed-Solomon codes
• CPC Class Codes: H03M 13/23 using convolutional codes, e.g. unit memory codes
• CPC Class Codes: H03M 13/255 with Low Density Parity Check [LDPC] codes
• CPC Class Codes: H03M 13/27 using interleaving techniques
• CPC Class Codes: H03M 13/2792 Interleaver wherein interleaving is performed jointly with another technique such as puncturing, multiplexing or routing
• CPC Class Codes: H03M 13/2906 using block codes H03M13/2957 takes precedence
• CPC Class Codes: H03M 13/2933 using a block and a convolutional code H03M13/2957 takes precedence
• CPC Class Codes: H03M 13/2948 Iterative decoding H03M13/2957 takes precedence
• CPC Class Codes: H03M 13/3707 Adaptive decoding and hybrid decoding, e.g. decoding methods or techniques providing more than one decoding algorithm for one code
• CPC Class Codes: H03M 13/373 with erasure correction and erasure determination, e.g. for packet loss recovery or setting of erasures for the decoding of Reed-Solomon codes
• CPC Class Codes: H03M 13/3746 with iterative decoding
• CPC Class Codes: H03M 13/3784 : for soft-output decoding of block codes
• CPC Class Codes: H03M 13/3938 : Tail-biting H03M13/2996 takes precedence
• CPC Class Codes: H03M 13/45 : Soft decoding, i.e. using symbol reliability information H03M13/41 takes precedence
• CPC Class Codes: H03M 13/451 : using a set of candidate code words, e.g. ordered statistics decoding [OSD]
• CPC Class Codes: H03M 13/453 : wherein the candidate code words are obtained by an algebraic decoder, e.g. Chase decoding
• CPC Class Codes: H03M 13/612 : Aspects specific to channel or signal-to-noise ratio estimation H03M13/63 takes precedence
• CPC Class Codes: H03M 13/616 : Matrix operations, especially for generator matrices or check matrices, e.g. column or row permutations
• CPC Class Codes: H03M 13/6325 : Error control coding in combination with demodulation
• CPC Class Codes: H03M 13/6337 : Error control coding in combination with channel estimation
• CPC Class Codes: H03M 13/6362 : by puncturing
• CPC Class Codes: H03M 13/6541 : DVB-H and DVB-M
• CPC Class Codes: H03M 13/6552 : DVB-T2
• CPC Class Codes: H03M 13/658 : Scaling by multiplication or division
• CPC Class Codes: H04B 17/309 : Measuring or estimating channel quality parameters
• CPC Class Codes: H04L 1/004 : by using forward error control H04L1/0618 takes precedence; coding, decoding or code conversion, for error detection or correction H03M13/00
• CPC Class Codes: H04L 1/0045 : Arrangements at the receiver end
• CPC Class Codes: H04L 1/005 : Iterative decoding, including iteration between signal detection and decoding operation
• CPC Class Codes: H04L 1/0057 : Block codes H04L1/0061, H04L1/0064 take precedence
• CPC Class Codes: H04L 1/0059 : Convolutional codes
• CPC Class Codes: H04L 1/0065 : Serial concatenated codes
• CPC Class Codes: H04L 1/0071 : Use of interleaving interleaving per se H03M13/27
• CPC Class Codes: H04L 25/022 : of frequency response
• CPC Class Codes: H04L 25/0222 : Estimation of channel variability, e.g. coherence bandwidth, coherence time, fading frequency
• CPC Class Codes: H04L 25/0224 : using sounding signals
• CPC Class Codes: H04L 25/0236 : using estimation of the other symbols
• CPC Class Codes: H04L 27/261 : Details of reference signals
• CPC Class Codes: H04L 27/2649 : Demodulators

Abstract

Systems and methods for decoding block and concatenated codes are provided. These include advanced iterative decoding techniques based on belief propagation algorithms, with particular advantages when applied to codes having higher density parity check matrices. Improvements are also provided for performing channel state information estimation including the use of optimum filter lengths based on channel selectivity and adaptive decision-directed channel estimation. These improvements enhance the performance of various communication systems and consumer electronics. Particular improvements are also provided for decoding HD Radio signals, including enhanced decoding of reference subcarriers based on soft-diversity combining, joint enhanced channel state information estimation, as well as iterative soft-input soft-output and list decoding of convolutional codes and Reed-Solomon codes. These and other improvements enhance the decoding of different logical channels in HD Radio systems.

Claims

1. A communication system with adaptive pilot symbol structure comprising:

a. a measurement device in a receiver that measures one or more signal attributes of a signal received over a first communication channel;

b. a processing device that analyzes said measurements of one or more received signal attributes to determine a preferred pilot symbol structure, wherein the preferred pilot symbol structure is selected from at least two possible pilot symbol structures; and

c. a transmitting device that conveys via a second communication channel to a transmitter of said received signal that was measured in the receiver an indicator of preferred pilot structure.

2. The system of claim 1 wherein the one or more signal attributes comprise time selectivity of the first communication channel.

3. The system of claim 1 wherein the one or more signal attributes is selected from the group consisting of signal-to-noise ratio, received signal power and received signal strength indicator.

4. The system of claim 1 wherein the one or more signal attributes include at least time selectivity and an indication of signal strength.

5. The system of claim 1 wherein the processing device determines said preferred pilot symbol structure using said measurements of one or more signal attributes and one or more look-up tables.

6. The system of claim 1 wherein the processing device determines said preferred pilot symbol structure using said measurements of one or more signal attributes and one or more thresholds.

7. The system of claim 1 wherein said indicator consists of one or more bits transmitted as a separate data field.

8. The system of claim 1 wherein said indicator is combined with another indicator into an integrated indicator supporting adaptive communication between the transmitter and the receiver over the said first communication channel.

9. A method for estimating channel response in a receiver of a multicarrier communication system comprising:

a. selecting filter lengths for time and frequency domain channel estimation based on estimated time selectivity and frequency selectivity of said channel response wherein said time selectivity and frequency selectivity are estimated using at least one of known pilot symbols and unknown data symbols;

b. estimating said channel response using said selected filter lengths by using available pilot symbols and a first set of data symbols, the first set of data symbols being selected as a fraction of data symbols that are more reliable than a remaining fraction of data symbols in a second set of data symbols that are considered less reliable; and

c. with respect to symbol positions corresponding to said second set of data symbols, estimating the channel response by interpolation based on estimated values of channel response obtained from said pilot symbols and said first set of data symbols.

10. The method of claim 9 wherein said time or frequency selectivity is estimated using a level crossing rate estimate of said channel response.

11. The method of claim 9 wherein said time or frequency selectivity is estimated using a rate of change estimate of said channel response.

12. The method of claim 9 wherein said selected filter lengths for channel response estimation are different for time and frequency domain processing.

13. The method of claim 9 wherein a criterion for selecting said fraction of more reliable data symbols is based on at least one of the following:

a. the channel response at a given symbol position is larger than a first threshold; and

b. the signal-to-noise ratio or the signal-to-noise plus interference ratio at a given symbol position is larger than a second threshold.

14. The method of claim 13 wherein at least one of said first and second thresholds is selected based on said estimated channel response time selectivity or frequency selectivity.

15. A method for estimating noise power by first obtaining a channel response estimate using the method of claim 9, the method for estimating noise power further comprising:

a. estimating noise samples on the positions of said known pilot symbols by subtracting from received noisy symbol samples the product of the corresponding estimated channel response and the pilot symbol values;

b. estimating noise samples on the positions of said first set of data symbols by subtracting from the received noisy symbol samples the product of the corresponding estimated channel response and the estimated values of the first set of data symbols;

c. estimating the power of said estimated noise samples by either calculating the squared magnitude of said estimated noise samples, or by squaring real and imaginary parts of said estimated noise samples; and

d. filtering said estimated noise power over time with a first filter length and over frequency with a second filter length.

16. The method of claim 15 wherein said first filter length is selected according to time selectivity of noise and/or interference and where said second filter length is selected according to frequency selectivity of noise and/or interference.

17. The method of claim 9 wherein said selected filter lengths are used to smooth said channel response.

18. The method of claim 17 wherein said selected filter lengths for smoothing of said channel response are different for time and frequency domain processing.

19. A method for iterative channel state estimation in a multicarrier communication system with forward error correction wherein an initial channel response estimate and an initial noise power estimate are obtained using the method of claim 15, the method for iterative channel response estimation further comprising at least one iteration of the following steps:

a. performing forward error correction decoding using said initial channel response estimate to obtain improved estimates of coded bits;

b. obtaining estimates of data symbols using said improved estimates of coded bits; and

c. calculating an updated channel response estimate using a shorter filter length for at least one filter and a larger fraction of said data symbols.

20. The method of claim 19 further comprising performing another iteration of forward error correction decoding and obtaining updated estimates of data symbols based on said updated channel response estimate, and calculating a further updated channel response estimate based on one or both of further decreasing the length of one or more filters and increasing the fraction of data symbols.

21. A method for estimating channel response in a receiver of a single carrier communication system comprising:

a. selecting a filter length for time domain channel estimation based on estimated time selectivity of said channel response wherein the said time selectivity is estimated using at least one of known pilot symbols and unknown data symbols;

b. estimating said channel response using said selected filter length by using available pilot symbols and a first set of data symbols, the first set of data symbols being selected as a fraction of data symbols that are more reliable than a remaining fraction of data symbols in a second set of data symbols that are considered less reliable; and

c. with respect to symbol positions corresponding to said second set of data symbols, estimating the channel response by interpolation based on estimated values of channel response obtained from said pilot symbols and said first set of data symbols.

22. The method of claim 21 wherein said time selectivity is estimated using a level crossing rate estimate of said channel response.

23. The method of claim 21 wherein the said time selectivity is estimated using a rate of change estimate of said channel response.

24. The method of claim 21 wherein a criterion for selecting the said fraction of more reliable data symbols is based on at least one of the following:

a. the channel response at a given symbol position is larger than a first threshold; and

b. the signal-to-noise ratio or the signal-to-noise plus interference ratio at a given symbol position is larger than a second threshold.

25. The method of claim 24 wherein at least one of said first and second thresholds is selected based on said estimated channel response time selectivity.

26. A method for estimating noise power by first obtaining a channel response estimate using the method of claim 21, the method for estimating noise power further comprising:

a. estimating noise samples on the positions of said known pilot symbols by subtracting from the received noisy symbol samples the product of the corresponding estimated channel response and the pilot symbol values;

b. estimating noise samples on the positions of said first set of data symbols by subtracting from the received noisy symbol samples the product of the corresponding estimated channel response and the estimated values of the first set of data symbols;

c. estimating the power of said estimated noise samples by either calculating the squared magnitude of said estimated noise samples, or by squaring real and the imaginary parts of said estimated noise samples; and

d. filtering said estimated noise power over time with a selected filter length.

27. The method of claim 26 wherein said selected filter length is selected accordingly to time selectivity of noise and/or interference.

28. The method of claim 21 wherein said selected filter length is used to smooth said channel response.

29. A method for iterative channel state estimation in a single carrier communication system with forward error correction wherein an initial channel response estimate and an initial noise power estimate are obtained using the method of claim 26, the method for iterative channel response estimation further comprising at least one iteration of the following steps:

a. performing forward error correction decoding using said initial channel response estimate to obtain improved estimates of coded bits;

b. obtaining estimates of data symbols using said improved estimates of coded bits; and

c. calculating an updated channel response estimate using a shorter filter length for at least one filter and a larger fraction of said data symbols.

30. The method of claim 29 further comprising performing another iteration of forward error correction decoding and obtaining updated estimates of data symbols based on said updated channel response estimate, and calculating a further updated channel response estimate based on one or both of further decreasing the length of one or more filters and increasing the fraction of data symbols.

31. A method for generating check-to-variable messages during an iteration in decoding of codes represented by a parity check matrix, the method comprising the following steps for at least one variable node:

a. calculating a check-to-variable message Mcv(i,j) from check node i to variable node j;

b. identifying two smallest absolute values, Min₁ and Min₂, in a set of variable-to-check messages Mvc(i,k), where k≠

j, excluding the message from variable j to check node i, Mvc(i,j);

c. calculating a scaling factor α

=1−

β

·

Min₁/Min₂, where is a non-negative number such that 0≦

β

≦

1; and

d. scaling the check-to-variable message Mcv(i,j) as Mcv(i,j)=α

·

Mcv(i,j).

32. The method of claim 31 wherein the check-to-variable message Mcv(i,j) from check node i to variable node j is calculated using a min-sum algorithm.

33. The method of claim 31 wherein the check-to-variable message Mcv(i,j) from check node i to variable node j is calculated using a sum-product algorithm.

34. A method for modifying variable-to-check messages during an iteration in decoding of codes represented by a parity check matrix, the method comprising the following steps for at least one variable node:

a. calculating variable-to-check messages Mvc(i,j)^(n-1) and Mvc(i,j)⁽ⁿ⁾ from variable node j to check node i, respectively, in iterations (n−

1) and (n), where n≧

2;

b. comparing the positive or negative signs of variable-to-check messages Mvc(i,j)^(n-1) and Mvc(i,j)⁽ⁿ⁾ calculated in step a; and

c. if said signs in step b) are different, generate modified variable-to-check message {tilde over (M)}vc(i,j)⁽ⁿ⁾ according to {tilde over (M)}vc(i,j)⁽ⁿ⁾=g·

Mvc(i,j)⁽ⁿ⁾+(1−

g)·

Mvc(i,j)^(n-1), where 0.5<

g≦

1.

35. The method of claim 34 wherein the variable-to-check messages Mvc(i,j)^(n-1) and Mvc(i,j)⁽ⁿ⁾ from variable node j to check node i are calculated using a min-sum algorithm.

36. The method of claim 34 wherein the variable-to-check messages Mvc(i,j)^(n-1) and Mvc(i,j)⁽ⁿ⁾ from variable node j to check node i are calculated using a sum-product algorithm.

37. A method for simple greedy scheduling of check equation updates, for at least M equations, where 1<

M≦

N−

K, during an iteration in decoding of codes represented by a parity check matrix with N−

K parity check rows and N columns, the method comprising the following steps;

a. for each check node i, out of M check nodes of the parity check matrix, calculating Val_i=Min₁+Min₂, i=1, 2, . . . , L where L≧

1 and where Min₁ and Min₂ are the two smallest values in the set of absolute values of variable-to-check messages

{|Mvc(i,;

)|} where index i corresponds to the set of check nodes;

b. sorting the set {Val_i} calculated in step a in decreasing order to obtain an ordering vector I={I₁, I₂, . . . , I_M}, such that I₁ is the index of a check node with the largest value Val, I₂ is the index of a check node with the next largest value Val and I_M is the index of a check node with the smallest value Val; and

c. updating M check node equations, according to the ordering vector, I={I₁, I₂, . . . , I_M} calculated in step b, by calculating and propagating corresponding check-to-variable messages.

38. The method of claim 37 wherein the variable-to-check messages Mvc(i,j) are calculated using a min-sum algorithm.

39. The method of claim 37 wherein the variable-to-check messages Mvc(i,j) are calculated using a sum-product algorithm.

40. A method for simple greedy scheduling of check equation updates, for at least M equations, 1<

M≦

N−

K, during an iteration in decoding of codes represented by a parity check matrix with N−

K parity check rows and N columns, comprising the following steps;

a. for the set of M′

≦

M of non-updated check nodes out of M check nodes of the parity check matrix, calculating Val_i=Min₁+Min₂, i=1, 2, . . . , L where L≧

1 and where Min₁ and Min₂ are the two smallest values in the set of absolute values of variable-to-check messages {|Mvc(i,;

)|} where index i corresponds to the set of non-updated check nodes;

b. sorting the set {Val_i} calculated in step a in decreasing order to obtain an ordering vector I={I₁, I₂, . . . , I_M}, such that I₁ is the index of a check node with the largest value Val, I₂ is the index of a check node with the next largest value Val and I_L is the index of a check node with the smallest value Val;

c. updating L check node equations selected in step b according to the ordering vector, I={I₁, I₂, . . . , I_L} calculated in step b, by calculating and propagating corresponding check-to-variable messages and updating variable-to-check messages for all variables that receive check-to-variable messages in this step; and

d. repeating steps a, b and c until all check nodes are updated by calculating and propagating corresponding check-to-variable messages.

41. The method of claim 40 wherein the check-to-variable messages {Mcv(i,j)} are calculated using a min-sum algorithm.

42. The method of claim 40 wherein the check-to-variable messages {Mcv(i,j)} are calculated using a sum-product algorithm.

43. A method for decoding codes represented by a parity check matrix of dimension (N−

K)×

N, comprising;

a. generating P>

1 parity check matrices with N−

K sparse columns wherein up to N−

K sparse columns contain only a single entry equal to 1 per column, wherein sparse columns of each of P parity check matrices correspond to different subsets of N−

K bit log-likelihood ratios of N−

K+R least reliable bit log-likelihood ratios, wherein R≧

P is a configurable integer;

b. decoding of channel log-likelihood ratios using said P parity check matrices with sparse columns to produce updated log-likelihood ratios, employing soft-input soft-output message passing decoding until a desired number of iterations is reached or until said decoding using at least one of said P matrices produces a valid codeword; and

c. on the condition that decoding using said P parity check matrices produces no valid codeword, performing additional decoding that is based at least in part on algebraic decoding of the sequences of said updated log-likelihood ratios.

44. The method of claim 43 wherein said soft-input soft-output message passing decoding comprises the following steps for at least one variable node:

a. calculating a check-to-variable message Mcv(i,j) from check node i to variable node j;

b. identifying two smallest absolute values, Min₁ and Min₂, in a set of variable-to-check messages Mvc(i, k), where k≠

j, excluding the message from variable j to check node i, Mvc(i,j);

c. calculating the scaling factor α

=1−

β

·

Min₁/Min₂, where is a non-negative number such that 0≦

β

≦

1; and

d. scaling the check-to-variable message Mcv(i,j) as Mcv(i,j)=α

·

Mcv(i,j).

45. The method of claim 43 wherein said soft-input soft-output message passing decoding comprises simple greedy scheduling of check equation updates, for at least M equations, 1<

M≦

N−

K, during an iteration in decoding of codes represented by a parity check matrix with N−

K parity check rows and N columns, comprises the following steps;

a. for the set of M′

≦

M of non-updated check nodes out of M check nodes of the parity check matrix, calculating Val_i=Min₁+Min₂, i=1, 2, . . . , L where L≧

1 and where Min₁ and Min₂ are the two smallest values in the set of absolute values of variable-to-check messages {|Mvc(i,;

)|} where index i corresponds to the set of non-updated check nodes;

b. sorting the set {Val_i} calculated in step a in decreasing order to obtain an ordering vector I={I₁, I₂, . . . , I_L}, such that I₁ is the index of a check node with the largest value Val, I₂ is the index of a check node with the next largest value Val and I_L is the index of a check node with the smallest value Val;

c. updating L check node equations selected in step b according to the ordering vector, I={I₁, I₂, . . . , I_L} calculated in step b, by calculating and propagating corresponding check-to-variable messages and updating variable-to-check messages for all variables that received check-to-variable messages in this step; and

d. repeating steps a, b and c until all check nodes are updated by calculating and propagating corresponding check-to-variable messages.

46. The method of claim 43 wherein said additional decoding is based on error and erasure decoding.

47. The method of claim 43 wherein said additional decoding is based on error only decoding.

48. The method of claim 43 wherein said soft-input soft-output message passing decoding is based on best graph belief propagation.

49. The method of claim 43 wherein said soft-input soft-output message passing decoding is based on belief propagation.

50. The method of claim 43 wherein during the course of iterations of said soft-input soft-output message passing decoding, one or more of P parity check matrices are updated one or more times based on updated bit log-likelihood ratios, wherein up to N−

K sparse columns contain only a single entry equal to 1 per column, and wherein sparse columns of said one or more P parity check matrices correspond to different subsets of N−

K bit log-likelihood ratios of N−

K+R least reliable bit log-likelihood ratios.

51. The method of claim 43 including storing updated P sequences of bit log-likelihood ratios obtained after IT≧

1 iterations of said soft-input soft-output message passing decoding using P parity check matrices and on the condition that no valid codeword is produced using said additional decoding, further comprising the following steps;

a. identifying Q≦

N−

K disagreement positions on hard decisions of said P sequences of bit log-likelihood ratios;

b. generating P new parity check matrices such that the sparse columns include N−

K columns corresponding to said Q≦

N−

K disagreement positions and, on the condition that Q<

N−

K, up to N−

K−

Q sparse columns corresponding to different combinations of N−

K−

Q of N−

K−

Q+R least reliable said updated P sequences of bit log-likelihood ratios, wherein R≧

P is a configurable integer;

c. decoding said updated log-likelihood ratios using said P new parity check matrices to produce further updated log-likelihood ratios, employing soft-input soft-output message passing decoding until a desired number of iterations is reached or until decoding using at least one of said P new parity check matrices produces a valid codeword; and

d. on the condition that said decoding using said P parity check matrices produces no valid codeword, performing additional decoding that is based at least in part on algebraic decoding of the sequences of said further updated log-likelihood ratios.

52. A method for decoding a hybrid digital radio signal, the signal being an OFDM signal, the method comprising:

a. performing initial channel state information estimation using distorted modulated symbols obtained from the received OFDM signal, wherein said modulated symbols belong to a plurality of OFDM symbol intervals and a plurality of OFDM subcarriers;

b. producing soft estimates of convolutional code coded bits using soft-input soft-output decoding of one or more convolutional codes associated with one or more logical channels carried in a radio frame of the OFDM signal;

c. performing at least one additional iteration of channel state information estimation by using at least some of said soft convolutional code coded bits estimates; and

d. performing enhanced decoding of at least one of a PSD PDU, a PIDS PDU, audio packet data, a MPS PDU header and AAS PDU using improved channel state information obtained by said at least one additional iteration.

53. The method of claim 52 wherein the initial channel state information estimation comprises:

a. estimating unknown symbols on the reference subcarriers carrying system control data sequence bits;

b. selecting filter lengths for time and frequency domain channel estimation based on estimated time selectivity and frequency selectivity of said channel response wherein said time selectivity and frequency selectivity are estimated by using known symbols on the reference subcarriers and at least one of the estimated unknown symbols on the reference subcarriers and estimated at least a fraction of unknown data symbols on data subcarriers;

c. estimating the channel response using said selected filter lengths by using the known symbols on the reference subcarriers and at least one of the estimated unknown symbols on the reference subcarriers and a fraction of estimated data symbols in a first set that are more reliable than a remaining fraction of data symbols in a second set; and

d. with respect to symbol positions corresponding to said second set, estimating the channel response by interpolation based on estimated values of channel response obtained from said pilot symbols and said first set.

54. The method of claim 53 wherein at least one of estimation filter lengths used in said at least one additional iteration of channel state information estimation is selected based on said estimated time selectivity or frequency selectivity of said channel response.

55. The method of claim 54 wherein at least one of the estimation filter lengths is shorter than at least one of the corresponding estimation filter length used in the initial channel state information estimation.

56. The method of claim 53 wherein estimating unknown symbols on the reference subcarriers comprises:

a. soft diversity combining of control data sequence symbols that carry the same symbol values;

b. differentially decoding said soft diversity combined symbols to obtain the corresponding soft decoded control data sequence bits; and

c. reconstructing control data sequence symbols from said decoded control data sequence bits.

57. The method of claim 53 wherein estimating unknown symbols on the reference subcarriers comprises:

a. soft diversity combining of control data sequence symbols that carry the same symbol values;

b. differentially decoding said soft diversity combined symbols to obtain the corresponding soft decoded control data sequence bits;

c. decoding said soft decoded control data sequence bits to obtain improved decoded control data sequence bits using single parity check code bits wherein;

i. the least reliable soft decoded control data sequence bit, in the parity bit protected field of two or more bits including the parity bit, is flipped on the condition that parity does not check, and

ii. no bit is flipped, in the parity bit protected field of two or more bits including the parity bit, on the condition that parity checks; and

d. reconstructing control data sequence symbols from said improved decoded control data sequence bits.

58. The method of claim 52 wherein said soft estimates of convolutional code coded bits are log-likelihood ratios of said convolutional code coded bits.

59. The method of claim 52 wherein said soft-input soft-output decoding of said one or more convolutional codes is based on a tail-biting Log-MAP algorithm.

60. The method of claim 52 wherein said soft-input soft-output decoding of said one or more convolutional codes is based on a tail-biting Max-Log-MAP algorithm.

61. The method of claim 52 wherein said enhanced decoding of at least one of said PSD PDU, PIDS PDU and audio packet data comprises at least one, or both of, list soft-input soft-output convolutional decoding and soft-input soft-output CRC decoding, and wherein the list soft-input soft-output convolutional decoding produces a sequence of soft convolutional code information bits and a list that includes at least some of most likely sequences of hard decisions of convolutional code information bits.

62. The method of claim 61 wherein said list soft-input soft-output convolutional decoding is selected from the group consisting of:

a. list Log-MAP decoding;

b. list Max-Log-MAP decoding; and

c. list soft-output Viterbi decoding.

63. The method of claim 52 wherein enhanced decoding of a MPS PDU header comprises:

a. soft-input soft-output decoding of the convolutional code carrying said MPS PDU header to produce soft information bits outputs of the convolutional code; and

b. soft-input soft-output Reed-Solomon decoding of said soft information bits outputs of the convolutional code, on the condition that their respective hard decisions do not correspond to a codeword of a Reed-Solomon (96,88) code, and otherwise using hard decisions of said soft information bits output of the convolutional code as a decoded MPS PDU header without using soft-input soft-output Reed-Solomon decoding, on the condition that said hard decisions correspond to a codeword of said Reed-Solomon code.

64. The method of claim 52 wherein enhanced decoding of a MPS PDU header comprises:

a. list soft-input soft-output decoding of the convolutional code carrying said MPS PDU header to produce a sequence of soft convolutional code information bits and a list that includes at least some of most likely sequences of hard decisions of information bits outputs of the convolutional code; and

b. soft-input soft-output Reed-Solomon decoding of said soft information bits outputs of the convolutional code, on the condition that neither their respective hard decisions, nor any of said hard decision sequences on the list, correspond to a codeword of a Reed-Solomon (96,88) code, and otherwise using a hard decision sequence as a decoded MPS PDU header, on the condition that at least one of said sequences of hard decisions corresponds to a codeword of said Reed-Solomon code.

65. The method of claim 63 further including processing of soft information bits of the convolutional code before decoding by the soft-input soft-output Reed-Solomon decoder, the method further comprising:

a. improving estimates of some said soft information bits outputs of the convolutional code corresponding to the MPS PDU header by exploiting a deterministic or predictable relationship between some fields of the MPS PDU header in the present frame and in one or more previous frames, on the condition that there were no detected errors in the MPS PDU header in the previous frame, such that said some information bits outputs of the convolutional code corresponding to the present MPS PDU header are determined based on the corresponding convolutional code information bits of the MPS PDU header in the previous frame;

or

b. improving estimates of said soft information bits outputs of the convolutional code corresponding to audio packet locator fields of the MPS PDU header by exploiting a causal relationship between consecutive said audio packet locator fields of the MPS PDU header.

66. The method of claim 64 wherein said soft convolutional code information bits are processed before processing by the soft-input soft-output Reed-Solomon decoder, the method further comprising:

a. improving estimates of some said soft information bits outputs of the convolutional code corresponding to the MPS PDU header by exploiting a deterministic or predictable relationship between some fields of the MPS PDU header in the present frame and in one or more previous frames, on the condition that there were no detected errors in the MPS PDU header in the previous frame, such that said some information bits outputs of the convolutional code corresponding to the present MPS PDU header are determined based on the corresponding convolutional code information bits of the MPS PDU header in the previous frame;

or

b. improving estimates of said soft information bits outputs of the convolutional code corresponding to audio packet locator fields of the MPS PDU header by exploiting a causal relationship between consecutive said audio packet locator fields of the MPS PDU header.

67. The method of claim 52 wherein enhanced decoding of AAS PDU comprises:

a. one of a soft-input soft-output tail-biting decoding of a convolutional code and a list soft-input soft-output tail-biting decoding of a convolutional code; and

b. soft-input soft-output decoding of a Reed-Solomon (255,223) code.

68. The method of claim 67 wherein said soft-input soft-output decoding of said Reed-Solomon code operates on log-likelihood ratios of information bits outputs of the convolutional code using message passing and comprises:

a. generating P>

1 binary parity check matrices with at least 256 sparse columns wherein up to 256 sparse columns contain only a single entry equal to 1 per column, wherein sparse columns of each of P parity check matrices correspond to different subsets of up to 256 bit log-likelihood ratios of 256+R least reliable bit log-likelihood ratios;

b. decoding of said log-likelihood ratios using said P parity check matrices with sparse columns to produce updated log-likelihood ratios, until a desired number of iterations is reached or until said decoding using at least one of said P matrices produces a valid codeword; and

c. on the condition that decoding using said P parity check matrices produces no valid codeword, performing additional decoding that is based at least in part on algebraic decoding of the sequences of said updated log-likelihood ratios.

69. The method of claim 67 wherein said soft-input soft-output decoding of said Reed-Solomon code comprises simple greedy scheduling of check equation updates, for at least M equations, where 1<

M≦

256, during an iteration in decoding of codes represented by a parity check matrix with at least 256 parity check rows and at least 2040 columns, the method further comprising;

a. for the set of M′

≦

M of non-updated check nodes out of M check nodes of the parity check matrix, calculating values Val_i=Min₁+Min₂, i=1, . . . , L, where 1≦

L≦

M′ and

where Min₁ and Min₂ are the two smallest values in the set of absolute values of variable-to-check messages {|Mvc(i,;

)|} where index i corresponds to the set of non-updated check nodes;

b. sorting the calculated set {Val_i} in decreasing order to obtain an ordering vector I={I₁, I₂, . . . , I_L}, such that I₁ is the index of a check node with largest value Val, I₂ is the index of a check node with the next largest value Val and I_L is the index of a check node with the smallest value Val;

c. updating L check node equations according to the ordering vector, I={I₁, I₂, . . . , I_L} calculated in step b, by calculating and propagating corresponding check-to-variable messages and updating variable-to-check messages for all variables that received check-to-variable messages in this step; and

d. repeating steps a, b and c herein until all check nodes are updated by calculating and propagating corresponding check-to-variable messages.

70. The method of claim 67 wherein soft-input soft-output decoding of said Reed-Solomon code operates on log-likelihood ratios of information bits of the convolutional code using message passing and comprises the following steps for at least one variable node:

a. calculating a check-to-variable message Mcv(i,j) from check node i to variable node j;

b. identifying two smallest absolute values, Min₁ and Min₂, in a set of variable-to-check messages Mvc(i,k), where k≠

j, excluding the message from variable j to check node i, Mvc(i,j);

c. calculating the scaling factor α

=1−

β

·

Min₁/Min₂, where β

is a non-negative number such that 0≦

β

≦

1; and

d. scaling the check-to-variable message Mcv(i,j) as Mcv(i,j)=α

·

Mcv(i,j).

71. The method of claim 63 wherein soft-input soft-output decoding of said Reed-Solomon code operates on log-likelihood ratios of said information bits outputs of the convolutional code using message passing, the method further comprising:

a. generating P>

1 binary parity check matrices with at least 64 sparse columns wherein up to 64 sparse columns contain only a single entry equal to 1 per column, wherein sparse columns of each of P parity check matrices correspond to different subsets of up to 64 bit log-likelihood ratios of 64+R least reliable bit log-likelihood ratios;

b. decoding of said log-likelihood ratios using said P parity check matrices with sparse columns to produce updated log-likelihood ratios, until a desired number of iterations is reached or until said decoding using at least one of said P matrices produces a valid codeword; and

c. on the condition that decoding using said P parity check matrices produces no valid codeword, performing additional decoding that is based at least in part on algebraic decoding of the sequences of said updated log-likelihood ratios.

72. The method of claim 63 wherein soft-input soft-output decoding of said Reed-Solomon code comprises simple greedy scheduling of check equation updates, for at least M equations, 1<

M≦

64, during an iteration in decoding of codes represented by a parity check matrix with at least 64 parity check rows and at least 768 columns, the method further comprising;

a. for the set of M′

≦

M of non-updated check nodes out of M check nodes of the parity check matrix, calculating values Val_i=Min₁+Min₂, i=1, . . . , L, where 1≦

L≦

M′ and

where Min₁ and Min₂ are the two smallest values in the set of absolute values of variable-to-check messages {|Mvc(i,;

)|} where index i corresponds to the set of non-updated check nodes;

b. sorting the set {Val_i} calculated in step a in decreasing order to obtain an ordering vector I={I₁, I₂, . . . , I_L}, such that I₁ is the index of a check node with largest value Val, I₂ is the index of a check node with the next largest value Val and I_L is the index of a check node with the smallest value Val;

c. updating L check node equations according to the ordering vector, I={I₁, I₂, . . . , I_L} calculated in step b, by calculating and propagating corresponding check-to-variable messages and updating variable-to-check messages for all variables that received check-to-variable messages in this step; and

d. repeating steps a, b and c herein until all check nodes are updated by calculating and propagating corresponding check-to-variable messages.

73. The method of claim 63 wherein soft-input soft-output decoding of said Reed-Solomon code operates on log-likelihood ratios of information bits outputs of the convolutional code using message passing and comprises the following steps for at least one variable node:

a. calculating a check-to-variable message Mcv(i,j) from check node i to variable node j;

b. identifying two smallest absolute values, Min₁ and Min₂, in a set of variable-to-check messages Mvc(i,k), where k≠

j, excluding the message from variable j to check node i, Mvc(i,j);

c. calculating the scaling factor α

=1−

β

·

Min₁/Min₂, where β

is a non-negative number such that 0≦

β

≦

1; and

d. scaling the check-to-variable message Mcv(i,j) as Mcv(i,j)=α

·

Mcv(i,j).

74. The method of claim 64 wherein soft-input soft-output decoding of said Reed-Solomon code operates on log-likelihood ratios of information bits of the convolutional code using message passing and comprises:

a. generating P>

1 binary parity check matrices with at least 64 sparse columns wherein up to 64 sparse columns contain only a single entry equal to 1 per column, wherein sparse columns of each of P parity check matrices correspond to different subsets of up to 64 bit log-likelihood ratios of 64+R least reliable bit log-likelihood ratios;

b. decoding of said log-likelihood ratios using said P parity check matrices with sparse columns to produce updated log-likelihood ratios, until a desired number of iterations is reached or until said decoding using at least one of said P matrices produces a valid codeword; and

c. on the condition that decoding using said P parity check matrices produces no valid codeword, performing additional decoding that is based at least in part on algebraic decoding of the sequences of said updated log-likelihood ratios.

75. The method of claim 64 wherein soft-input soft-output decoding of said Reed-Solomon code comprises simple greedy scheduling of check equation updates, for at least M equations, 1<

M≦

64, during an iteration in decoding of codes represented by a parity check matrix with at least 64 parity check rows and at least 768 columns, the method further comprising;

a. for the set of M′

≦

M of non-updated check nodes out of M check nodes of the parity check matrix, calculating values Val_i=Min₁+Min₂, i=1, . . . , L, where 1≦

L≦

M′ and

where Min₁ and Min₂ are the two smallest values in the set of absolute values of variable-to-check messages {|Mvc(i,;

)|} where index i corresponds to the set of non-updated check nodes;

b. sorting the calculated set {Val_i} in decreasing order to obtain an ordering vector I={I₁, I₂, . . . , I_L}, such that I₁ is the index of a check node with largest value Val, I₂ is the index of a check node with the next largest value Val and I_L is the index of a check node with the smallest value Val;

c. updating L check node equations according to the ordering vector, I={I₁, I₂, . . . , I_L} calculated in step b, by calculating and propagating corresponding check-to-variable messages and updating variable-to-check messages for all variables that received check-to-variable messages in this step; and

d. repeating steps a, b and c herein until all check nodes are updated by calculating and propagating corresponding check-to-variable messages.

76. The method of claim 64 wherein soft-input soft-output decoding of said Reed-Solomon code operates on log-likelihood ratios of information bits of the convolutional code using message passing and comprises the following steps for at least one variable node:

a. calculating a check-to-variable message Mcv(i,j) from check node i to variable node j;

b. identifying two smallest absolute values, Min₁ and Min₂, in a set of variable-to-check messages Mvc(i,k), where k≠

j, excluding the message from variable j to check node i, Mvc(i,j);

c. calculating the scaling factor α

=1−

β

·

Min₁/Min₂, where β

is a non-negative number such that 0≦

β

≦

1; and

d. scaling the check-to-variable message Mcv(i,j) as Mcv(i,j)=α

·

Mcv(i,j).