Method and apparatus for parallel decoding of turbo encoded data

• US 20020071505A1
• Filed: 09/12/2001
• Published: 06/13/2002
• Est. Priority Date: 09/12/2000
• Status: Active Grant

• Current Assignee: Broadcom Corporation (Broadcom, Inc.)
• Original Assignee: Broadcom Corporation (Broadcom, Inc.)
• Inventors: Shen, Ba-Zhong, Jones, Christopher R., Tran, Hau Thien, Cameron, Kelly B.
• Granted Patent: US 7,158,589 B2
• Time in Patent Office: Days
• US Class Current: 375/341
• CPC Class Codes: H03M 13/151 using error location or error correction polynomials
• CPC Class Codes: H03M 13/1515 Reed-Solomon codes
• CPC Class Codes: H03M 13/25 Error detection or forward error correction by signal space coding, i.e. adding redundancy in the signal constellation, e.g. Trellis Coded Modulation [TCM] modulation codes H03M13/31
• CPC Class Codes: H03M 13/258 with turbo codes, e.g. Turbo Trellis Coded Modulation [TTCM]
• CPC Class Codes: H03M 13/27 using interleaving techniques
• CPC Class Codes: H03M 13/2757 Interleaver with an interleaving rule not provided for in the subgroups H03M13/2703 - H03M13/2753
• CPC Class Codes: H03M 13/29 combining two or more codes or code structures, e.g. product codes, generalised product codes, concatenated codes, inner and outer codes
• CPC Class Codes: H03M 13/2966 Turbo codes concatenated with another code, e.g. an outer block code
• CPC Class Codes: H03M 13/2978 Particular arrangement of the component decoders
• CPC Class Codes: H03M 13/3905 Maximum a posteriori probability [MAP] decoding or approximations thereof based on trellis or lattice decoding, e.g. forward-backward algorithm, log-MAP decoding, max-log-MAP decoding
• CPC Class Codes: H03M 13/3922 Add-Compare-Select [ACS] operation in forward or backward recursions
• CPC Class Codes: H03M 13/3927 Log-Likelihood Ratio [LLR] computation by combination of forward and backward metrics into LLRs
• CPC Class Codes: H03M 13/3972 using sliding window techniques or parallel windows
• CPC Class Codes: H03M 13/3988 for rate k/n convolutional codes, with k>1, obtained by convolutional encoders with k inputs and n outputs
• CPC Class Codes: H03M 13/4107 implementing add, compare, select [ACS] operations
• CPC Class Codes: H03M 13/6505 Memory efficient implementations
• CPC Class Codes: H03M 13/6561 Parallelized implementations
• CPC Class Codes: H04L 1/0041 Arrangements at the transmitter end
• CPC Class Codes: H04L 1/005 Iterative decoding, including iteration between signal detection and decoding operation
• CPC Class Codes: H04L 1/006 Trellis-coded modulation
• CPC Class Codes: H04L 1/0065 : Serial concatenated codes
• CPC Class Codes: H04L 1/0066 : Parallel concatenated codes
• CPC Class Codes: H04L 1/0068 : by puncturing
• CPC Class Codes: H04L 1/0071 : Use of interleaving interleaving per se H03M13/27

Abstract

A method and apparatus for parallel decoding of turbo encoded data. The method includes multiple Soft In Soft Out (SISO) modules arranged in parallel such that each module supplies an input to one SISO and takes an input from another SISO data encoded for multiple parallel SISOs is received by a receiver and decoded in the abovementioned parallel configuration.

Claims

1. A method for parallel decoding of modulo N turbo encoded data, the method comprising:

accepting samples of modulo N turbo encoded data;

calculating metric values for each sample;

accepting the metric values into each of N soft input soft output (SISO) modules;

deinterleaving extrinsic values to form a priori values;

accepting a priori values into each of N soft input soft output (SISO) modules; and

performing decoding calculations within each of the SISOs thereby forming the extrinsic values;

2. The method of claim 1 wherein accepting samples of modulo N turbo encoded data comprises accepting an inphase (I) value and quadrature (Q) value representing samples of the turbo encoded data.

3. The method of claim 1 wherein calculating metric values for each sample comprises calculating the distance between the sample and each possible transmitted constellation point.

4. The method of claim 1 wherein calculating metric values for each sample comprises calculating the Euclidean distance squared between the sample and each possible transmitted constellation point.

5. The method of claim 4 wherein calculating the Euclidean distance squared comprises:

determining a first distance, in the I direction, between the between the sample and a possible transmitted constellation point in the I direction;

squaring the first distance to form the first distance squared;

determining a second distance, in the Q direction, between the between the sample and a possible transmitted constellation point in the Q direction;

squaring the second distance to form the second distance squared; and

adding the first distance squared to the second distance squared to form the Euclidean distance squared.

6. The method of claim 1 wherein accepting the a priori values into each of N soft input soft output (SISO) modules further comprises accepting a value representing an equal likelihood that the sample point represents each valid constellation point on a first iteration of the decoder.

7. The method of claim 1 wherein deinterleaving extrinsic values to form a priori values comprises performing a deinterleaving using an inverse interleaver of an interleaver used to produce a corresponding interleaving;

8. The method of claim 1 wherein performing decoding calculations within each of the SISOs thereby forming the extrinsic values comprises performing a MAP (maximum a posteriori) decoding.

9. The method of claim 1 wherein performing decoding calculations within each of the SISOs thereby forming the extrinsic values comprises performing a SOVA (soft output Viterbi algorithm) decoding.

10. The method of claim 1 wherein accepting samples of modulo N turbo encoded data comprises accepting samples of odd-even encoded data.

13. The apparatus as in claim 1 wherein the metric calculator comprises a calculator that calculates the distance between the sample and each possible transmitted constellation point.

11. An apparatus for parallel decoding of modulo N turbo encoded data, the apparatus comprising:

an input queue that accepts samples of modulo N turbo encoded data;

a metric calculator that accepts samples of modulo N turbo encoded data from the input queue and calculates the metric values for each sample;

N soft input soft output (SISO) modules that accept the metric values and an a priori values and produce an extrinsic value;

N modulo N deinterleavers that accept an extrinsic value and deinterleave it to form an a priori value.

12. The apparatus as in claim 11 wherein the input queue that accepts samples of modulo N turbo encoded data further comprises an input queue that accepts an inphase (I) value and quadrature (Q) values representing samples of the turbo encoded data.

14. The apparatus as in claim 11 wherein the metric calculator is an Euclidean distance squared calculator that calculates the distance between the sample and each possible transmitted constellation point.

15. The apparatus as in claim 11 wherein the deinterleaver comprises an inverse of the modulo N interleaver used to decode the turbo encoded data.

16. The apparatus as in claim 11 wherein the SISO decoder is a MAP (maximum a posteriori) decoder.

17. The apparatus as in claim 11 wherein the SISO decoder is a SOVA (soft output Viterbi algorithm) decoder.

19. The apparatus as in claim 18 further comprising a conditional points processor that accepts samples of modulo N turbo encoded data representing a block of turbo encoded data and produces conditional points.

20. The apparatus as in claim 19 further comprising an output processor that accepts a priori values from the SISO means and conditional points from the conditional processor and computes output tuples.

18. An apparatus for parallel decoding of modulo N turbo encoded data, the apparatus comprising:

means for accepting samples of modulo N turbo encoded data representing a block of turbo encoded data;

means for calculating metrics metric that accepts samples of modulo N turbo encoded data from the sample accepting means and calculates the metric values for each sample;

N means for accepting a soft input and producing a soft output (SISO means) that accept the metric values and an a priori values and produce an extrinsic value;

N modulo N deinterleaving means that accept an extrinsic value and deinterleave it to form an a priori value.