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Information Theory and Applications Workshop (ITA)

Asymptotic ensemble enumerators for protograph-based generalized LDPC codes: Computational complexity

Shadi Abu‐Surra, W.E. Ryan, D. Divsalar

Published Jan 1, 2008• 6 citations
Publisher

Abstract

In earlier work, we presented a method for finding ensemble weight enumerator for protograph-based generalized LDPC (G-LDPC) codes, and leveraged this method to find ensemble stopping set enumerator and ensemble trapping set enumerator. The method is conceptually simple, but when the dimensionality of the constraint nodes (number of their code-words) grows, it becomes difficult to handle the computational complexity, which rise while evaluating these enumerators. To deal with this difficulty, we posed a conjecture, which greatly reduce the computational complexity. Trails to proof this conjecture showed that the proof is a challenging problem. Also, proving it will strengthen the theory of enumerating protograph-based G-LDPC code ensembles. Which in turn helps in predicating the average performances for codes drawn from these ensembles. In Section II we present a review of our method for finding finite and asymptotic weight enumerators for protograph-based G-LDPC code ensembles. Then, we present the conjecture in Section III with some examples.

DOI
10.1109/ita.2008.4601024

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In earlier work, we presented a method for finding ensemble weight enumerator for protograph-based generalized LDPC (G-LDPC) codes, and leveraged this method to find ensemble stopping set enumerator and ensemble trapping set enumerator. The method is conceptually simple, but when the dimensionality of the constraint nodes (number of their code-words) grows, it becomes difficult to handle the computational complexity, which rise while evaluating these enumerators. To deal with this difficulty, we posed a conjecture, which greatly reduce the computational complexity. Trails to proof this conjecture showed that the proof is a challenging problem. Also, proving it will strengthen the theory of enumerating protograph-based G-LDPC code ensembles. Which in turn helps in predicating the average performances for codes drawn from these ensembles. In Section II we present a review of our method for finding finite and asymptotic weight enumerators for protograph-based G-LDPC code ensembles. Then, we present the conjecture in Section III with some examples.
Protograph-based LDPC codes have the advantages of a simple design (or search) procedure and highly structured encoders and decoders. These advantages have also been exploited in the design of protograph-based generalized LDPC (G-LDPC) codes. Recently, a technique for computing ensemble weight enumerators and stopping set enumerators for protograph-based LDPC codes has been published. In the current paper, we extend those results to protograph-based G-LDPC codes. That is, we first derive ensemble weight and stopping set enumerators for finite-length G-LDPC codes based on protographs, and then we consider the asymptotic case. In the weight enumerator case, the asymptotic results allow us to determine whether or not the typical minimum distance in the ensemble grows linearly with codeword length. In the stopping set enumerator case, the asymptotic results allows us to determine whether or not the typical smallest stopping set size grows linearly with codeword length.
Among the recent advances in the area of low-density parity-check (LDPC) codes, protograph-based LDPC codes have the advantages of a simple design procedure and highly structured encoders and decoders. These advantages can also be exploited in the design of protograph-based generalized LDPC (G-LDPC) codes. In this dissertation we provide analytical tools which aid the design of protograph-based LDPC and G-LDPC codes. Specifically, we propose a method for computing the codeword-weight enumerators for finite-length protograph-based G-LDPC code ensembles, and then we consider the asymptotic case when the block-length goes to infinity. These results help the designer identify good ensembles of protograph-based G-LDPC codes in the minimum distance sense (i.e., ensembles which have minimum distances grow linearly with code length). Furthermore, good code ensembles can be characterized by good stopping set, trapping set, or pseudocodeword properties, which assist in the design of G-LDPC codes with low floors. We leverage our method for computing codeword-weight enumerators to compute stopping-set, and pseudocodeword enumerators for the finite-length and the asymptotic ensembles of protograph-based G-LDPC codes. Moreover, we introduce a method for computing trapping set enumerators for finite-length (and asymptotic) protograph-based LDPC code ensembles. Trapping set enumerators for G-LDPC codes represents a more complex problem which we do not consider here. Inspired by our method for computing trapping set enumerators for protograph-based LDPC code ensembles, we developed an algorithm for estimating the trapping set enumerators for a specific LDPC code given its parity-check matrix. We used this algorithm to enumerate trapping sets for several LDPC codes from communication standards. Finally, we study coded-modulation schemes with LDPC codes and pulse position modulation (LDPC-PPM) over the free-space optical channel. We present three different decoding schemes and compare their performances. In addition, we developed a new density evolution tool for use in the design of LDPC codes with good performances over this channel.
Protograph-based LDPC and generalized LDPC (G-LDPC) codes have the advantages of a simple design procedure and highly structured encoders and decoders. The design of such “protograph-based codes” relies on what is effectively a computer-based search. As such, following Gallager, it is prudent to restrict the search to a “good ensemble,” for example, an ensemble whose minimum distance grows linearly with codeword length. A good ensemble can also mean one with good stopping set, trapping set, or pseudocodeword properties. In this paper, ensemble codeword weight enumerators for finite-length LDPC and G-LDPC codes based on protographs were derived, and then the asymptotic case was considered. The asymptotic results allow us to determine whether or not the typical relative minimum distance in the ensemble grows linearly with codeword length. Then, the codeword weight enumerator technique is adapted to yield ensemble stopping set, trapping set, and pseudocodeword enumerators for protograph LDPC and G-LDPC codes. In this case, the asymptotic results allow us to determine whether or not the typical relative smallest stopping set size, trapping set size, and pseudoweight grows linearly with codeword length. Trapping set enumerators for G-LDPC code ensembles represent a more complex problem which we do not consider here.
Protograph-based LDPC codes have the advantages of a simple design (or search) procedure and highly structured encoders and decoders. These advantages have also been exploited in the design of protograph-based generalized LDPC (G-LDPC) codes. Recently, a technique for computing ensemble weight enumerators for protograph-based LDPC codes has been published. In the current paper, we extend those results to protograph-based G-LDPC codes. That is, we first derive ensemble weight enumerators for finite-length G-LDPC codes based on protographs, and then we consider the asymptotic case. The asymptotic results allow us to determine whether or not the typical minimum distance in the ensemble grows linearly with codeword length.
This paper considers the problem of finding average enumerators for the class of protograph ensembles, which are related in a certain way to quasi-cyclic codes. Our methods, which are necessarily different from those used to compute enumerators for classical irregular ensembles, can be applied to both codeword and stopping set weight enumerators. The method divides codewords into types based on their partial weight enumerator. For each type, an exponent can be computed for the average number of codewords of that type. Maximizing over types of fixed average weight gives the average enumerator which we seek. Although this maximization step is in general difficult because of non-unique local maxima, we can compute it for simple cases. We show that certain ensembles exist which have a linearly growing minimum distance with high probability, while others have at most sublinearly growing minimum distance with high probability