2021
DOI: 10.3390/math9121410
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Minimal State-Space Representation of Convolutional Product Codes

Abstract: In this paper, we study product convolutional codes described by state-space representations. In particular, we investigate how to derive state-space representations of the product code from the horizontal and vertical convolutional codes. We present a systematic procedure to build such representation with minimal dimension, i.e., reachable and observable.

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“…The case we are interested in is that of submodules and I/S/O representations. I/S/O representations are useful because, among other reasons, they allow one to construct convolutional codes with desirable properties (such as observability and good distances), to define (algebraic) decoding algorithms, to study concatenated convolutional codes, and to study finite support 2D convolutional codes, periodically time-invariant convolutional codes and product convolutional codes ( [11][12][13][14][15][16][17]).…”
Section: Introductionmentioning
confidence: 99%
“…The case we are interested in is that of submodules and I/S/O representations. I/S/O representations are useful because, among other reasons, they allow one to construct convolutional codes with desirable properties (such as observability and good distances), to define (algebraic) decoding algorithms, to study concatenated convolutional codes, and to study finite support 2D convolutional codes, periodically time-invariant convolutional codes and product convolutional codes ( [11][12][13][14][15][16][17]).…”
Section: Introductionmentioning
confidence: 99%