In the 2004 Shannon Lecture, McEliece presented an expression for the number of stopping sets of size three in a Hamming code. In this paper, we investigate how this number depends on the parity-check matrix used in the decoding process. First, we present basic results on stopping set enumerators for block codes in general. Next, we focus on stopping set enumerators for Hamming codes. Our main result is a parity-check matrix of relatively small size for which the number of stopping sets of size three equals the number of codewords of weight three in the Hamming code.
Stopping sets and stopping set distribution of an low-density parity-check code are used to determine the performance of this code under iterative decoding over a binary erasure channel (BEC). Let
C be a binary
[n,k] linear code with parity-check matrix
H, where the rows of
H may be dependent. A stopping set
S of
C with parity-check matrix
H is a subset of column indices of
H such that the restriction of
H to
S does not contain a row of weight one. The stopping set distribution
{Ti(H)}i=0n enumerates the number of stopping sets with size
i of
C with parity-check matrix
H. Note that stopping sets and stopping set distribution are related to the parity-check matrix
H of
C. Let
H∗ be the parity-check matrix of
C which is formed by all the non-zero codewords of its dual code
C⊥. A parity-check matrix
H is called BEC-optimal if
Ti(H)=Ti(H∗),i=0,1,...,n and
H has the smallest number of rows. On the BEC, iterative decoder of
C with BEC-optimal parity-check matrix is an optimal decoder with much lower decoding complexity than the exhaustive decoder. In this paper, we study stopping sets, stopping set distributions and BEC-optimal parity-check matrices of binary linear codes. Using finite geometry in combinatorics, we obtain BEC-optimal parity-check matrices and then determine the stopping set distributions for the Simplex codes, the Hamming codes, the first order Reed-Muller codes and the extended Hamming codes.
It is now well known that the performance of a linear code
C under iterative decoding on a binary erasure channel (and other channels) is determined by the size of the smallest stopping set in the Tanner graph for
C. Several recent papers refer to this parameter as the \emph{stopping distance}
s of
C. This is somewhat of a misnomer since the size of the smallest stopping set in the Tanner graph for
C depends on the corresponding choice of a parity-check matrix. It is easy to see that
s≤d, where
d is the minimum Hamming distance of
C, and we show that it is always possible to choose a parity-check matrix for
C (with sufficiently many dependent rows) such that
s=d. We thus introduce a new parameter, termed the \emph{stopping redundancy} of
C, defined as the minimum number of rows in a parity-check matrix
H for
C such that the corresponding stopping distance
s(H) attains its largest possible value, namely
s(H)=d. We then derive general bounds on the stopping redundancy of linear codes. We also examine several simple ways of constructing codes from other codes, and study the effect of these constructions on the stopping redundancy. Specifically, for the family of binary Reed-Muller codes (of all orders), we prove that their stopping redundancy is at most a constant times their conventional redundancy. We show that the stopping redundancies of the binary and ternary extended Golay codes are at most 35 and 22, respectively. Finally, we provide upper and lower bounds on the stopping redundancy of MDS codes.
The performance of iterative decoding techniques for linear block codes correcting erasures depends very much on the sizes of the stopping sets associated with the underlying Tanner graph, or, equivalently, the parity-check matrix representing the code. In this correspondence, we introduce the notion of dead-end sets to explicitly demonstrate this dependency. The choice of the parity-check matrix entails a tradeoff between performance and complexity. We give bounds on the complexity of iterative decoders achieving optimal performance in terms of the sizes of the underlying parity-check matrices. Further, we fully characterize codes for which the optimal stopping set enumerator equals the weight enumerator.
It is now well known that the performance of a linear code Copf under iterative decoding on a binary erasure channel (and other channels) is determined by the size of the smallest stopping set in the Tanner graph for Copf. Several recent papers refer to this parameter as the stopping distance s of Copf. This is somewhat of a misnomer since the size of the smallest stopping set in the Tanner graph for Copf depends on the corresponding choice of a parity-check matrix. It is easy to see that s les d, where d is the minimum Hamming distance of Copf, and we show that it is always possible to choose a parity-check matrix for Copf (with sufficiently many dependent rows) such that s=d. We thus introduce a new parameter, the stopping redundancy of Copf, defined as the minimum number of rows in a parity- check matrix H for Copf such that the corresponding stopping distance s(H) attains its largest possible value, namely, s(H)=d. We then derive general bounds on the stopping redundancy of linear codes. We also examine several simple ways of constructing codes from other codes, and study the effect of these constructions on the stopping redundancy. Specifically, for the family of binary Reed-Muller codes (of all orders), we prove that their stopping redundancy is at most a constant times their conventional redundancy. We show that the stopping redundancies of the binary and ternary extended Golay codes are at most 34 and 22, respectively. Finally, we provide upper and lower bounds on the stopping redundancy of MDS codes