By Aliazam Abbasfar
This publication introduces faster blunders correcting inspiration in an easy language, together with a normal thought and the algorithms for interpreting turbo-like code. It provides a unified framework for the layout and research of faster codes and LDPC codes and their interpreting algorithms. an immense concentration is on excessive velocity faster deciphering, which ambitions purposes with info premiums of numerous hundred million bits consistent with moment (Mbps).
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Additional resources for Turbo-like Codes: Design for High Speed Decoding
This process is considered as one iteration. It takes several iterations to obtain u c1 SISO I c2 SISO c1 u c2 SISO I SISO Fig. 21 Message passing between the constituent codes of turbo codes 22 3 High-speed Turbo Decoders Fig. 22 The iterative decoding structure r1 a0 aN SISO b0 bN x y Interleaver yI xI a0 aN SISO b0 bN r2 very good performance for bit decisions. Turbo codes are designed such that the probability density of the extrinsics is shifted toward higher values in each iteration. This phenomenon is known as DE, which is also used to compute the capacity of codes using the iterative decoding.
BR I BR I' OC 1 OC 1' ... OC I OC I' Int Int-1 ... Int Int-1 Fig. 7 Interleaver Design 33 the memory access problem due to its memoryless structure, which is the main problem in the parallelization of turbo decoder. The scheduling diagram of the parallel decoder is shown in Figure 35. 2 Interleaver Design Algorithm The structure of the interleaver can be best understood by organizing the bits in a matrix with each row having the bits processed in a SISO. Each column of this matrix is a vector that is computed at a time.
Y M N −2 y M N −1 a(M−1)N a(M−1)N +1 . . a M N −2 a M N −1 a N x(M−1)N x(M−1)N +1 . . x M N −2 x M N −1 Fig. 5 Parallel Turbo Decoder 25 yN−1 yN−2 · · · y1 y0 Backword: bN bN−1 · · · b2 b1 b0 y0 y1 · · · yN−2 yN−1 Forword: a0 a1 · · · aN−2 aN−1 aN x0 x1 · · · xN−2 xN−1 Output: Fig. 26 Timing diagram of the parallel SISOs in vector notation beta variables that are computed in forward and backward recursion of the BCJR algorithm. In the first iteration there is no prior knowledge available about the state probabilities.