Performance Analysis of 3-D Turbo Codes
Artikel i vetenskaplig tidskrift, 2011
In this work, we consider the minimum distance properties and convergence thresholds of 3-D turbo codes (3D-TCs), recently introduced by Berrou et al. Here, we consider binary 3D-TCs while the original work of Berrou et al. considered double-binary codes. In the first part of the paper, the minimum distance properties are analyzed from an ensemble perspective, both in the finite-length regime and in the asymptotic case of large block lengths. In particular, we analyze the asymptotic weight distribution of 3D-TCs and show numerically that their typical minimum distance (dmin) may, depending on the specific parame- ters, asymptotically grow linearly with the block length, i.e., the 3D-TC ensemble is asymptotically good for some parameters. In the second part of the paper, we derive some useful upper bounds on the dmin when using quadratic permutation polynomial (QPP) interleavers with a quadratic inverse. Furthermore, we give examples of interleaver lengths where an upper bound appears to be tight. The best codes (in terms of estimated dmin) obtained by randomly searching for good pairs of QPPs for use in the 3D-TC are compared to a probabilistic lower bound on the dmin when selecting codes from the 3D-TC ensemble uniformly at random. This comparison shows that the use of designed QPP interleavers can improve the dmin significantly. For instance, we have found a (6144,2040) 3D-TC with an estimated dmin of 147, while the proba- bilistic lower bound is 69. Higher rates are obtained by puncturing nonsystematic bits, and optimized periodic puncturing patterns for rates 1/2, 2/3, and 4/5 are found by computer search. Finally, we give iterative decoding thresholds, computed from an extrinsic information transfer chart analysis, and present simulation results on the additive white Gaussian noise channel to compare the error rate performance to that of conventional turbo codes.
spectral shape function
hybrid concatenated codes
Asymptotic minimum distance analysis