Roxana Smarandache

Maximum Distance Separable Convolutional Codes

Abstract. A maximum distance separable (MDS) block code is a linear code whose distance is maximal among all linear block codes of rate k/n. It is well known that MDS block codes do exist if the field size is more than n. In this paper we generalize this concept to the class of convolutional codes of a fixed rate k/n and a fixed code degree δ. In order to achieve this result we will introduce a natural upper bound for the free distance generalizing the Singleton bound. The main result of the paper shows that this upper bound can be achieved in all cases if one allows sufficiently many field elements.

Strongly-MDS convolutional codes

Maximum-distance separable (MDS) convolutional codes have the property that their free distance is maximal among all codes of the same rate and the same degree. In this paper, a class of MDS convolutional codes is introduced whose column distances reach the generalized Singleton bound at the earliest possible instant. Such codes are called strongly-MDS convolutional codes. They also have a maximum or near-maximum distance profile. The extended row distances of these codes will also be discussed briefly.

Deriving Good LDPC Convolutional Codes from LDPC Block Codes

Low-density parity-check (LDPC) convolutional codes are capable of achieving excellent performance with low encoding and decoding complexity. In this paper, we discuss several graph-cover-based methods for deriving families of time-invariant and time-varying LDPC convolutional codes from LDPC block codes and show how earlier proposed LDPC convolutional code constructions can be presented within this framework. Some of the constructed convolutional codes significantly outperform the underlying LDPC block codes. We investigate some possible reasons for this “convolutional gain,” and we also discuss the-mostly moderate-decoder cost increase that is incurred by going from LDPC block to LDPC convolutional codes.

LDPC Codes for Compressed Sensing

We present a mathematical connection between channel coding and compressed sensing. In particular, we link, on the one hand, channel coding linear programming decoding (CC-LPD), which is a well-known relaxation of maximum-likelihood channel decoding for binary linear codes, and, on the other hand, compressed sensing linear programming decoding (CS-LPD), also known as basis pursuit, which is a widely used linear programming relaxation for the problem of finding the sparsest solution of an underdetermined system of linear equations. More specifically, we establish a tight connection between CS-LPD based on a zero-one measurement matrix over the reals and CC-LPD of the binary linear channel code that is obtained by viewing this measurement matrix as a binary parity-check matrix. This connection allows the translation of performance guarantees from one setup to the other. The main message of this paper is that parity-check matrices of “good” channel codes can be used as provably “good” measurement matrices under basis pursuit. In particular, we provide the first deterministic construction of compressed sensing measurement matrices with an order-optimal number of rows using high-girth low-density parity-check codes constructed by Gallager.

Quasi-Cyclic LDPC Codes: Influence of Proto- and Tanner-Graph Structure on Minimum Hamming Distance Upper Bounds

Quasi-cyclic (QC) low-density parity-check (LDPC) codes are an important instance of proto-graph-based LDPC codes. In this paper we present upper bounds on the minimum Hamming distance of QC LDPC codes and study how these upper bounds depend on graph structure parameters (like variable degrees, check node degrees, girth) of the Tanner graph and of the underlying proto-graph. Moreover, for several classes of proto-graphs we present explicit QC LDPC code constructions that achieve (or come close to) the respective minimum Hamming distance upper bounds. Because of the tight algebraic connection between QC codes and convolutional codes, we can state similar results for the free Hamming distance of convolutional codes. In fact, some QC code statements are established by first proving the corresponding convolutional code statements and then using a result by Tanner that says that the minimum Hamming distance of a QC code is upper bounded by the free Hamming distance of the convolutional code that is obtained by “unwrapping” the QC code.

Constructions of MDS-convolutional codes

Maximum-distance separable (MDS) convolutional codes are characterized through the property that the free distance attains the generalized singleton bound. The existence of MDS convolutional codes was established by two of the authors by using methods from algebraic geometry. This correspondence provides an elementary construction of MDS convolutional codes for each rate k/n and each degree /spl delta/. The construction is based on a well-known connection between quasi-cyclic codes and convolutional codes.

Diversity Polynomials for the Analysis of Temporal Correlations in Wireless Networks

The interference in wireless networks is temporally correlated, since the node or user locations are correlated over time and the interfering transmitters are a subset of these nodes. For a wireless network where (potential) interferers form a Poisson point process and use ALOHA for channel access, we calculate the joint success and outage probabilities of n transmissions over a reference link. The results are based on the diversity polynomial, which captures the temporal interference correlation. The joint outage probability is used to determine the diversity gain (as the SIR goes to infinity), and it turns out that there is no diversity gain in simple retransmission schemes, even with independent Rayleigh fading over all links. We also determine the complete joint SIR distribution for two transmissions and the distribution of the local delay, which is the time until a repeated transmission over the reference link succeeds.

Convolutional codes with maximum distance profile

Abstract Maximum distance profile codes are characterized by the property that two trajectories which start at the same state and proceed to a different state will have the maximum possible minimum distance from each other relative to any other convolutional code of the same rate and degree. In this paper we use methods from systems theory to characterize maximum distance profile codes algebraically. The main result shows that maximum distance profile codes form a generic set inside the variety which parametrizes the set of convolutional codes of a fixed rate and a fixed degree.

On Deriving Good LDPC Convolutional Codes from QC LDPC Block Codes

In this paper we study the iterative decoding behavior of time-invariant and time-varying LDPC convolutional codes derived by unwrapping QC LDPC block codes. In particular, for a time-varying LDPC convolutional code, we show that the minimum pseudo-weight of the convolutional code is at least as large as the minimum pseudo-weight of the underlying QC code. We also prove that the unwrapped convolutional codes have fewer short cycles than the QC codes. These results taken together lead to improved BER performance in the low-to-moderate SNR region, where the decoding behavior is influenced by the complete pseudo-codeword spectra and by the Tanner graph cycle histogram, with the time-varying convolutional codes outperforming both the underlying QC block codes and their time-invariant convolutional counterparts.

On regular quasicyclic LDPC codes from binomials

In the past, several authors have considered quasicyclic LDPC codes whose circulant matrices in the parity-check matrix are cyclically shifted identity matrices. By composing a parity-check matrix not only with such matrices but also with sums of two cyclically shifted identity matrices and with zero matrices, one can increase the minimum distance while keeping the same regularity. Specifically, whereas for (3, 4)-regular codes in the first class the best minimum distance is 24, the best minimum distance in the second class is 32. We give examples of codes that achieve these bounds.