Abstract—Stopping sets determine the performance of low-density parity-check (LDPC) codes under iterative decoding over erasure channels. We derive several results on the asymptotic behavior of stopping sets in Tanner-graph ensembles, including the following. An expression for the normalized average stopping set distribution, yielding, in particular, a critical fraction of the block length above which codes have exponentially many stopping sets of that size. A relation between the degree distribution and the likely size of the smallest nonempty stopping set, showing that for a I

We discuss three structures of modified low-density parity-check (LDPC) code ensembles designed for transmission over arbitrary discrete memoryless channels. The first structure is based on the well known binary LDPC codes following constructions proposed by Gallager and McEliece, the second is based on LDPC codes of arbitrary (q-ary) alphabets employing modulo-q addition, as presented by Gallager, and the third is based on LDPC codes defined over the field GF(q). All structures are obtained by applying a quantization mapping on a coset LDPC ensemble. We present tools for the analysis of non-binary codes and show that all configurations, under maximum-likelihood decoding, are capable of reliable communication at rates arbitrarily close to channel capacity of any discrete memoryless channel. We discuss practical iterative decoding of our structures and present simulation results for the AWGN channel confirming the effectiveness of the codes.

We present a construction algorithm for short block length irregular low-density parity-check (LDPC) codes. Based on a novel interpretation of stopping sets in terms of the paritycheck matrix, we present an approximate trellis-based search algorithm that detects many stopping sets. Growing the parity check matrix by a combination of random generation and the trellis-based search, we obtain codes that possess error floors orders of magnitude below randomly constructed codes and significantly better than other comparable constructions.

We introduce a new family of concatenated codes with an outer low-density parity-check (LDPC) code and an inner low-density generator matrix (LDGM) code, and prove that these codes can achieve capacity under any memoryless binaryinput output-symmetric (MBIOS) channel using maximum-likelihood (ML) decoding with bounded graphical complexity, i.e., the number of edges per information bit in their graphical representation is bounded. We also show that these codes can achieve capacity for the special case of the binary erasure channel (BEC) under belief propagation (BP) decoding with bounded decoding complexity per information bit for all erasure probabilities in (0, 1). By deriving and analyzing the average weight distribution (AWD) and the corresponding asymptotic growth rate of these codes with a rate-1 inner LDGM code, we also show that these codes achieve the Gilbert-Varshamov bound with asymptotically high probability. This result can be attributed to the presence of the inner rate-1 LDGM code, which is demonstrated to help eliminate high weight codewords in the LDPC code while maintaining a vanishingly small amount of low weight codewords. 1

Abstract — Measuring network flow sizes is important for tasks like accounting/billing, network forensics and security. Per-flow accounting is considered hard because it requires that many counters be updated at a very high speed; however, the large fast memories needed for storing the counters are prohibitively expensive. Therefore, current approaches aim to obtain approximate flow counts; that is, to detect large elephant flows and then measure their sizes. Recently the authors and their collaborators have developed [1] a novel method for per-flow traffic measurement that is fast, highly memory efficient and accurate. At the core of this method is a novel counter architecture called “counter braids.” In this paper, we analyze the performance of the counter braid architecture under a Maximum Likelihood (ML) flow size estimation algorithm and show that it is optimal; that is, the number of bits needed to store the size of a flow matches the entropy lower bound. While the ML algorithm is optimal, it is too complex to implement. In [1] we have developed an easy-to-implement and efficient message passing algorithm for estimating flow sizes that is analyzed elsewhere. I.

Abstract — A data compression scheme is defined to be smooth if its image (the codeword) depends gracefully on the source (the data). Smoothness is a desirable property in many practical contexts, and widely used source coding schemes lack of it. We introduce a family of smooth source codes based on sparse graph constructions, and prove them to achieve the (information theoretic) optimal compression rate for a dense set of iid sources. As a byproduct, we show how Gallager bound on sparsity can be overcome using non-linear function nodes. I.

Abstract — We consider lossy compression of a binary symmetric source by means of a low-density generator-matrix code. We derive two lower bounds on the rate distortion function which are valid for any low-density generator-matrix code with a given node degree distribution L(x) on the set of generators and for any encoding algorithm. These bounds show that, due to the sparseness of the code, the performance is strictly bounded away from the Shannon rate-distortion function. In this sense, our bounds represent a natural generalization of Gallager’s bound on the maximum rate at which low-density parity-check codes can be used for reliable transmission. Our bounds are similar in spirit to the technique recently developed by Dimakis, Wainwright, and Ramchandran, but they apply to individual codes. I.

In this paper, an expression for the asymptotic growth rate of the number of small linear-weight codewords of irregular doubly-generalized LDPC (D-GLDPC) codes is derived. The expression is compact and generalizes existing results for LDPC and generalized LDPC (GLDPC) codes. Ensembles with check or variable node minimum distance greater than 2 are shown to be asymptotically good, while for other ensembles a fundamental parameter is identified which discriminates between an asymptotically small and an asymptotically large expected number of small linear-weight codewords. Also, in the latter case it is shown that the growth rate depends only on the check and variable nodes with minimum distance 2. An important connection between this new result and the stability condition of D-GLDPC codes over the BEC is highlighted. Such a connection, previously observed for LDPC and GLDPC codes, is now extended to the case of D-GLDPC codes. Finally, it is shown that the analysis may be extended to include the growth rate of the stopping set size distribution of irregular D-GLDPC codes.

Abstract — Iterative decoding for linear block codes over erasure channels may be much simpler than optimal decoding but its performance is usually not as good. Here, we present a general iterative decoding technique that gives a more refined tradeoff between complexity and performance. In each iteration, a system of equations is solved. In case the maximum number of equations to be solved is just one, the general iterative decoder reduces to the well-known iterative decoder. On the other hand, if the maximum number is set to the redundancy of the codes, the general iterative decoder gives the same performance as the optimal decoder. Varying the maximum number of equations to be solved in each iteration between these two extremes allows for a better match, in terms of performance and complexity, to the system specifications. Stopping sets and stopping redundancy are important concepts in the analysis of the performance and complexity of iterative decoders on the erasure channel. In consequence of the new generalized decoding procedure, the notions of stopping sets and stopping redundancy are generalized as well. Basic properties and examples of both generalized stopping sets and generalized stopping redundancy are presented in this paper. I.

In this paper, capacity-achieving codes for memoryless binary-input output-symmetric (MBIOS) channels under maximum-likelihood (ML) decoding with bounded graphical complexity are investigated. The graphical complexity of a code is defined as the number of edges in the graphical representation of the code per information bit and is proportional to the decoding complexity per information bit per iteration under iterative decoding. Irregular repeat-accumulate (IRA) codes are studied first. By deriving their asymptotic average weight distribution (AAWD) it is shown that simple nonsystematic IRA ensembles outperform systematic IRA and regular low-density parity-check (LDPC) ensembles with the same graphical complexity, and are only 0.124 dB away from the Shannon limit for the binary-input additive white Gaussian noise (BIAWGN) channel. However, a conclusive result as to whether these nonsystematic IRA codes can really achieve capacity cannot be reached. Motivated by this inconclusive result, a new family of codes is proposed, called low-density parity-check and generator matrix (LDPC-GM) codes, which are serially concatenated codes with an outer LDPC code and an inner low-density generator matrix (LDGM) code. It is proved that these codes can achieve capacity on any MBIOS channel using ML decoding and also achieve capacity on any BEC using belief propagation (BP) decoding, both with bounded graphical complexity. Moreover, these codes are shown to have linearly increasing minimum distances and achieve the asymptotic Gilbert-Varshamov bound for all rates. 1 I.