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System Simulator > Turbo Decoder with PCCC (TDECPCCC)
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 Notes1. This model is used for Turbo Decoder with Parallel Concatenated Convolutional Code (PCCC). K is number of information bits in each code block 2. Turbo Coder Structure: Fig.1 shows the diagram of Turbo Coder with PCCC. The encoder consists of two recursive systematic convolutional (RSC) encoders with rate 1/2 which are separated by an K-bit interleaver, together with an optional puncturing procedure. For details of the encoder, please refer to the model Turbo Coder with PCCC.
3. Iterative Turbo Decoder Structure: The
diagram of iterative Turbo Decoder with PCCC is shown in Fig.2. It should
be noted that the turbo decoder must insert zeros in the soft channel
output for these punctured bits. In addition, a Feedback Factor (
4. RSC Decoder: In this model, each of the five typical algorithms (BCJR-MAP, Max-Log-MAP, Log-MAP (Exact), Log-MAP and SOVA) can be chosen used in RSC Decoder, which is the core of the iterative Turbo Decoder. The five algorithms are described in [3]. Please refer to [3] for details. Netlist FormTDECPCCC:NAME n1 n2 n3 n4 [ALGORITHM=val] K=val L1=val L2=val G1=val G2=val G3=val G4=val + [PUNCTURING=val] [TERMINATION =val] [ITERATION=val] [TRELLIS_DEPTH=val] [A =val] + [EBN0R=val] [FEEDBACK =val] [RIN1=val] [RIN2=val] [RIN3=val] [ROUT=val] Netlist ExampleTDECPCCC:1 1 2 3 4 ALGORITHM = 3 K=636 L1=5 L2=5 G1=19 G2=31 G3=19 G4=31 + PUNCTURING=6 TERMINATION=3 ITERATION=6 TRELLIS_DEPTH =50 A=1.0 + EbN0R=2 FEEDBACK=0.8 References1. C. Berrou and A. Glavieux, “Near optimum error correcting coding and decoding: Turbo-codes,” IEEE Trans. Commun., vol. 44, no. 10, pp. 1261–1271, 1996. 2. J. Hagenauer, E. Offer, and L. Papke, “Iterative decoding of binary block and convolutional codes,” IEEE Trans. Inform. Theory, pp. 429–445, Mar. 1996. 3. J. P. Woodard and L. Hanzo, “Comparative Study of Turbo Decoding Techniques: An Overview,” IEEE Transactions on Vehicular Technology, vol. 49, no. 6, pp. 2208-2233, Nov. 2000. 4. D. Divsalar and F. Pollara, “Turbo codes for PCS applications,” Proc. 1995 Int. Conf. Comm., pp54-59. 5. L. R. Bahl, J. Cocke, F. Jelinek, and J. Raviv, “Optimal decoding of linear codes for minimizing symbol error rate,” IEEE Trans. Inform. Theory, vol. vol. IT-20, pp. 284–287, Mar. 1974. 6. W. Koch and A. Baier, “Optimum and sub-optimum detection of coded data disturbed by time-varying inter-symbol interference,” IEEE Globecom, pp. 1679–1684, Dec. 1990. 7. J. A. Erfanian, S. Pasupathy, and G. Gulak, “Reduced complexity symbol detectors with parallel structures for ISI channels,” IEEE Trans. Commun., vol. 42, pp. 1661–1671, 1994. 8. P. Robertson, E. Villebrun, and P. Hoeher, “A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain,” in Proc. Int. Conf. Communications, June 1995, pp. 1009–1013. 9. J. Hagenauer and P. Hoeher, “A Viterbi algorithm with soft-decision outputs and its applications,” IEEE Globecom, pp. 1680–1686, 1989. 10. J. Hagenauer, “Source-controlled channel decoding,” IEEE Trans. Commun., vol. 43, pp. 2449–2457, Sept. 1995.
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Ansoft Designer / Ansys Designer 在线帮助文档：
) is used to multiply
the extrinsic information for stability with a typical value 0.8.
