Duality between Multidimensional Convolutional Codes and Systems

Multidimensional convolutional codes generalize (one dimensional) convolutional codes and they correspond under a natural duality to multidimensional systems widely studied in the systems literature.Comment: 16 pages LaTe

Multidimensional quasi-cyclic and convolutional codes

We introduce multidimensional generalizations of quasi-cyclic codes and investigate their algebraic properties as well as their links to multidimensional convolutional codes. We call these generalized codes n-dimensional quasi-cyclic (QnDC) codes. We provide a concatenated structure for QnDC codes in the sense that they can be decomposed into shorter codes over extensions of their base eld. This structure allows us to prove that these codes are asymptotically good. Then, we extend the relation between quasi-cyclic and convolutional codes to multidimensional case. Lally has shown that the free distance of a convolutional code is lower bounded by the minimum distance of an associated quasi-cyclic code. We show that a QnDC code can be associated to a given nD convolutional code. Moreover, we prove that the relation between distances of convolutional and quasicyclic codes extend to a class of 1-generator 2D convolutional codes and the associated Q2DC codes. Along the way, an alternative new description of noncatastrophic polynomial encoders is given for 1-generator 1D convolutional codes and a su cient condition for noncatastrophic nD polynomial encoders is obtained for 1-generator nD convolutional codes

Punctured Trellis-Coded Modulation

In classic trellis-coded modulation (TCM) signal constellations of twice the cardinality are applied when compared to an uncoded transmission enabling transmission of one bit of redundancy per PAM-symbol, i.e., rates of $\frac{K}{K+1}$ when $2^{K+1}$ denotes the cardinality of the signal constellation. In order to support different rates, multi-dimensional (i.e., $\mathcal{D}$-dimensional) constellations had been proposed by means of combining subsequent one- or two-dimensional modulation steps, resulting in TCM-schemes with $\frac{1}{\mathcal{D}}$ bit redundancy per real dimension. In contrast, in this paper we propose to perform rate adjustment for TCM by means of puncturing the convolutional code (CC) on which a TCM-scheme is based on. It is shown, that due to the nontrivial mapping of the output symbols of the CC to signal points in the case of puncturing, a modification of the corresponding Viterbi-decoder algorithm and an optimization of the CC and the puncturing scheme are necessary.Comment: 5 pages, 10 figures, submitted to IEEE International Symposium on Information Theory 2013 (ISIT

On the Computation of EXIT Characteristics for Symbol-Based Iterative Decoding

In this paper we propose an efficient method for computing index-based extrinsic information transfer (EXIT) charts, which are useful for estimating the convergence properties of non-binary iterative decoding. A standard method is to apply <i>a priori</i> reliability information to the <i>a posteriori</i> probability (APP) constituent decoder and compute the resulting average extrinsic information at the decoder output via multidimensional histogram measurements. However, this technique is only reasonable for very small index lengths as the complexity of this approach grows exponentially with the index length. We show that by averaging over a function of the extrinsic APPs for a long block the extrinsic information can be estimated with very low complexity. In contrast to using histogram measurements this method allows to generate EXIT charts even for larger index alphabets. Examples for a non-binary serial concatenated code and for turbo trellis-coded modulation, resp., demonstrate the capabilities of the proposed approach