Sequential decoding

About: Sequential decoding is a research topic. Over the lifetime, 8667 publications have been published within this topic receiving 204271 citations.

A Two-Stage Iterative Decoding of LDPC Codes for Lowering Error Floors

Abstract: In iterative decoding of LDPC codes, trapping sets often lead to high error floors. In this work, we propose a two-stage iterative decoding to break trapping sets. Simulation results show that the error floor performance can be significantly improved with this decoding scheme.

A trellis-based recursive maximum-likelihood decoding algorithm for binary linear block codes

Abstract: This paper presents an efficient trellis-based maximum-likelihood decoding algorithm for binary linear block codes. This algorithm is recursive in nature and is devised based on the structural properties and optimum sectionalization of a code trellis. The complexity of the proposed decoding algorithm is analyzed. Numerical results show that the proposed decoding algorithm significantly reduces the decoding complexity. A recursive method for finding the optimum sectionalization of a trellis in terms of computational complexity is given.

A parallel decoder of programmable Huffman codes

Abstract: Huffman coding, a variable-length entropy coding scheme, is an integral component of international standards on image and video compression including high-definition television (HDTV). The high-bandwidth HDTV systems of data rate in excess of 100 Mpixels/s presents a challenge for designing a fast and economic circuit for intrinsically sequential Huffman decoding operations. This paper presents an algorithm and a circuit implementation for parallel decoding of programmable Huffman codes by using the numerical properties of Huffman codes. The 1.2 /spl mu/m CMOS implementation for a single JPEG AC table of 256 codewords of up to 16-b codeword lengths is estimated to run at 10 MHz with a chip area of 11 mm/sup 2/, decoding one codeword per cycle. The design can be pipelined to deliver a throughput of 80 MHz for decoding input streams of consecutive Huffman codes. Furthermore, our programmable scheme can be easily integrated into data paths of video processors to support different Huffman tables used in image/video applications. >

Bounds on a probability for the heavy tailed distribution and the probability of deficient decoding in sequential decoding

Abstract: Although sequential decoding of convolutional codes gives a very small decoding error probability, the overall reliability is limited by the probability PG of deficient decoding, the term introduced by Jelinek to refer to decoding failures caused mainly by buffer overflow. The number of computational efforts in sequential decoding has the Pareto distribution and it is this "heavy tailed" distribution that characterizes PG. The heavy tailed distribution appears in many fields and buffer overflow is a typical example of the behaviors in which the heavy tailed distribution plays an important role. In this paper, we give a new bound on a probability in the tail of the heavy tailed distribution and, using the bound, prove the long-standing conjecture on PG, that is, PG ap constanttimes1/(sigmarhoNrho-1) for a large speed factor sigma of the decoder and for a large receive buffer size N whenever the coding rate R and rho satisfy E(rho)=rhoR for 0 les rho les 1

Message-Passing Algorithms and Improved LP Decoding

Abstract: Linear programming (LP) decoding for low-density parity-check codes (and related domains such as compressed sensing) has received increased attention over recent years because of its practical performance-coming close to that of iterative decoding algorithms-and its amenability to finite-blocklength analysis. Several works starting with the work of Feldman showed how to analyze LP decoding using properties of expander graphs. This line of analysis works for only low error rates, about a couple of orders of magnitude lower than the empirically observed performance. It is possible to do better for the case of random noise, as shown by Daskalakis and Koetter and Vontobel. Building on work of Koetter and Vontobel, we obtain a novel understanding of LP decoding, which allows us to establish a 0.05 fraction of correctable errors for rate-½ codes; this comes very close to the performance of iterative decoders and is significantly higher than the best previously noted correctable bit error rate for LP decoding. Our analysis exploits an explicit connection between LP decoding and message-passing algorithms and, unlike other techniques, directly works with the primal linear program. An interesting byproduct of our method is a notion of a “locally optimal” solution that we show to always be globally optimal (i.e., it is the nearest codeword). Such a solution can in fact be found in near-linear time by a “reweighted” version of the min-sum algorithm, obviating the need for LP. Our analysis implies, in particular, that this reweighted version of the min-sum decoder corrects up to a 0.05 fraction of errors.