Viterbi decoder

Summary

A Viterbi decoder uses the Viterbi algorithm for decoding a bitstream that has been encoded using a convolutional code or trellis code. There are other algorithms for decoding a convolutionally encoded stream (for example, the Fano algorithm). The Viterbi algorithm is the most resource-consuming, but it does the maximum likelihood decoding. It is most often used for decoding convolutional codes with constraint lengths k≤3, but values up to k=15 are used in practice. Viterbi decoding was developed by Andrew J. Viterbi and published in the paper There are both hardware (in modems) and software implementations of a Viterbi decoder. Viterbi decoding is used in the iterative Viterbi decoding algorithm. Hardware implementation A hardware Viterbi decoder for basic (not punctured) code usually consists of the following major blocks: *Branch metric unit (BMU) *Path metric unit (PMU) *Traceback unit (TBU) Branch metric unit (BMU) A branch metric unit's functi

Official source

https://en.wikipedia.org/wiki/Viterbi_decoder

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (22)

Related publications

Related people (4)

Related people

Related units (3)

Related units

Related concepts (5)

Related concepts

Related courses (5)

Related courses

Related lectures (11)

Related lectures

This dissertation presents a systematic exposition on finite-block-length coding theory and practice. We begin with the task of identifying the maximum achievable rates over noisy, finite-block-length constrained channels, referred to as (ε, n)-capacity Cεn, with ε denoting target block-error probability and n the block length. We characterize how the optimum codes look like over finite-block-length constrained channels. Constructing good, short-block-length error-correcting codes defined on sparse graphs is the focus of the thesis. We propose a new, general method for constructing Tanner graphs having a large girth by progressively establishing edges or connections between symbol and check nodes in an edge-by-edge manner, called progressive edge-growth (PEG) construction. Lower bounds on the girth of PEG Tanner graphs and on the minimum distance of the resulting low-density parity-check (LDPC) codes are derived in terms of parameters of the graphs. The PEG construction attains essentially the same girth as Gallager's explicit construction for regular graphs, both of which meet or exceed an extension of the Erdös-Sachs bound. The PEG construction proves to be powerful for generating good, short-block-length binary LDPC codes. Furthermore, we show that the binary interpretation of GF(2b) codes on the cycle Tanner graph TG(2, dc), if b grows sufficiently large, can be used over the binary-input additive white Gaussian noise (AWGN) channel as "good code for optimum decoding" and "good code for iterative decoding". Codes on sparse graphs are often decoded iteratively by a sum-product algorithm (SPA) with low complexity. We investigate efficient digital implementations of the SPA for decoding binary LDPC codes from both the architectural and algorithmic point of view, and describe new reduced-complexity derivatives thereof. The unified treatment of decoding techniques for LDPC codes provides flexibility in selecting the appropriate design point in high-speed applications from a performance, latency, and computational complexity perspective.

EPFL2003

Decay of correlations for sparse graph error correcting codes

Shrinivas Kudekar, Nicolas Macris

The subject of this paper is transmission over a general class of binary-input memoryless symmetric channels using error correcting codes based on sparse graphs, namely, low-density generator-matrix and low-density parity-check codes. The optimal (or ideal) decoder based on the posterior measure over the code-bits and its relationship to the suboptimal belief propagation decoder are investigated. We consider the correlation (or covariance) between two code-bits, averaged over the noise realizations, as a function of the graph distance for the optimal decoder. Our main result is that this correlation decays exponentially fast for given low-density generator-matrix codes and a high enough noise parameter and also for given low-density parity-check codes and a low enough noise parameter. This has many consequences. Appropriate performance curves — called generalized extrinsic information transfer (GEXIT) functions — of the belief propagation and optimal decoders match in high/low noise regimes. This means that in high/low noise regimes the performance curves of the optimal decoder can be computed by density evolution. Another interpretation is that the replica predictions of spin-glass theory are exact. Our methods are rather general and use cluster expansions first developed in the context of mathematical statistical mechanic

2011

Decay of Correlations in Low Density Parity Check Codes: Low Noise Regime

Shrinivas Kudekar, Nicolas Macris

Consider transmission over a binary additive white gaussian noise channel using a fixed low-density parity check code. We consider the posterior measure over the code bits and the corresponding correlation between two codebits, averaged over the noise realizations. We show that for low enough noise variance this average correlation decays exponentially fast with the graph distance between the code bits. One consequence of this result is that for low enough noise variance the GEXIT functions (further averaged over a standard code ensemble) of the belief propagation and optimal decoders are the same.

2009

EPFL2003