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About 2,568 papers for “maximum likelihood decoding”

A Recursive Maximum Likelihood Decoding

Shu Lin, Tadao Kasami, Toru Fujiwara et al. · 1998

Cited by 0

Hamming distance Maximum Likelihood Decoding

In This Lecture · 2016

Cited by 0

Keywords: Maximum-Likelihood Decoding, Adaptive Decoding Complementary Decoding, Ordered Algebraic Decoding

Xianren Wu, Hamid R. Sadjadpour (contact, Zhi Tian · 2004

In this paper, we propose a new two-stage structure for computationally efficient Maximum-Likelihood decoding (MLD) of linear block codes. With this structure, near optimal MLD performance can be achieved at low complexity through two-stage processing.The first stage of processing estimates a minimum sufficient set (MSS) of candidate codewords that contains the optimal codeword, while the second stage performs optimal or sub-optimal decoding search within the estimated MSS of small size. Based on the new structure, we propose a decoding algorithm that systematically trades off between the decoding complexity and the bounded block error rate performance. A low-complexity complementary decoding algorithm is developed to estimate the MSS, followed by an ordered algebraic decoding (OAD) algorithm to achieve flexible system design. Since the size of the MSS changes with the signal-to-noise ratio (SNR), the overall decoding complexity adaptively scales with the quality of the communication link. Theoretical analysis is provided to evaluate the potential complexity reduction enabled by the proposed decoding structure. I.

Cited by 0

Convolutional codes II. Maximum-likelihood decoding

G. David Forney · Information and Control · 1974

Cited by 256

Raptor codes under maximum likelihood decoding

Lazaro, Francisco, Liva, Gianluigi, Paolini, Enrico et al. · tub.dok (Hamburg University of Technology) · 2019

Cited by 0

Fountain codes under maximum likelihood decoding

Francisco Lázaro · elib (German Aerospace Center) · 2017

The journey towards this dissertation was fascinating but also long and challenging.It was only thanks to the technical advise, encouragement and love of

Cited by 3Open access

Performance of Recursive Maximum Likelihood Turbo Decoding

Sumitha Krishnamurthi · OhioLink ETD Center (Ohio Library and Information Network) · 2003

Cited by 0Open access

Software implementation of Viterbi maximum-likelihood decoding

Caonabo Almonte · OhioLink ETD Center (Ohio Library and Information Network) · 1981

Viterbi's [1] first article, "Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm", provides the theoretical background upon which the algorithm is based.It provides theoretical bounds to probability of error from both above and below.In his second artical [2] on the subject, "Convolutional Codes and their Performance in Communication Systems", Viterbi provides a tutorial approach to the basic properties of convolutional codes and provides examples of maximum likelihood decoders.These were used to develop the first two sets of working modules for the simulator.These two articles are the backbone of all the work reported in this thesis.J. A. Heller and I. M. Jacobs [13] in their article, "Viterbi Decoding for Satellite and Space Communication", present examples of efficient systems on power limited and space channels.They also show the effect of hard vs. soft quantization of the received signal as well as an actually implemented system of constraint length 7. Finally, they compare Viterbi and sequential decoding, both algorithms for decoding convolutional codes.Other hardware implementations were found in an article by G.C. Clark,Jr., and R.C. Davis [7].Its title implies its content, "Two Recent Applications of Error-Correction Coding toCommunications Systems Design."Both of these articles were helpful in providing the author with a measure of realism, i.e., a feel for actual hardware implementations.This was invaluable in later decisions on implementations on the simulator itself.There are three articles, however, that contributed the most to the actual implementation of the Viterbi algorithm in the simulator.

Cited by 0Open access

Maximum-Likelihood Noncoherent Lattice Decoding of QAM

Daniel J. Ryan, Iain B. Collings, I. Vaughan et al. · 2006

We present a novel, maximum-likelihood (ML), lattice-decoding algorithm for noncoherent block detection of QAM signals. The computational complexity is polynomial in the block length; making it feasible for implementation compared with the exhaustive search ML detector. The algorithm works by enumerating the nearest neighbor regions for a plane defined by the received vector; in a conceptually similar manner to sphere decoding. Simulations show that the new algorithm significantly outperforms existing approaches.

Cited by 6

Statistical Pruning for Near-Maximum Likelihood Decoding

Radhika Gowaikar, Babak Hassibi · IEEE Transactions on Signal Processing · 2007

In many communications problems, maximum-likelihood (ML) decoding reduces to finding the closest (skewed) lattice point in N-dimensions to a given point xisin C N . In its full generality, this problem is known to be NP-complete. Recently, the expected complexity of the sphere decoder, a particular algorithm that solves the ML problem exactly, has been computed. An asymptotic analysis of this complexity has also been done where it is shown that the required computations grow exponentially in N for any fixed SNR. At the same time, numerical computations of the expected complexity show that there are certain ranges of rates, SNRs and dimensions N for which the expected computation (counted as the number of scalar multiplications) involves no more than N 3 computations. However, when the dimension of the problem grows too large, the required computations become prohibitively large, as expected from the asymptotic exponential complexity. In this paper, we propose an algorithm that, for large N, offers substantial computational savings over the sphere decoder, while maintaining performance arbitrarily close to ML. We statistically prune the search space to a subset that, with high probability, contains the optimal solution, thereby reducing the complexity of the search. Bounds on the error performance of the new method are proposed. The complexity of the new algorithm is analyzed through an upper bound. The asymptotic behavior of the upper bound for large N is also analyzed which shows that the upper bound is also exponential but much lower than the sphere decoder. Simulation results show that the algorithm is much more efficient than the original sphere decoder for smaller dimensions as well, and does not sacrifice much in terms of performance

Cited by 90

Correlative level coding and maximum-likelihood decoding

Hiroshi Kobayashi · IEEE Transactions on Information Theory · 1971

Modems for digital communication often adopt the so-called correlative level coding or the partial-response signaling, which attains a desired spectral shaping by introducing controlled intersymbol interference terms. In this paper, a correlative level encoder is treated as a linear finite-state machine and an application of the maximum-likelihood decoding (MLD) algorithm, which was originally proposed by Viterbi in decoding convolutional codes, is discussed. Asymptotic expressions for the probability of decoding error are obtained for a class of correlative level coding systems, and the results are confirmed by computer simulations. It is shown that a substantial performance gain is attainable by this probabilistic decoding method.

Cited by 135

Maximum-likelihood decoding of Reed-Solomon codes

Jelena Vučković, Branka Vucetic · 2002

We propose a maximum likelihood algorithm for Reed-Solomon codes of reasonable complexity. All possible error patterns are organised into decoding levels, each level corresponding to some range of distances from the received sequence. Levels are tested subsequently, until the first code word is found.

Cited by 3

Maximum likelihood decoding and burst error correction

Davidson, Charles Alexander · Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School) · 1972

A brief discussion of basic encoding and decoding on noisy channels is presented to provide a background for the experimental portion of this research. A portioned 3 state Gilbert model is used to model a burst channel and a method of calculating error-sequence probabilities using this model is introduced. Error sequence probability calculations are made using a (7,3) maximal length code and a (15.7) BCH code. Observations are made about the general type of decoding rule to use to give the lowest probability of decoding error on burst channels when using an interleaving technique.

Cited by 0

Neural Decoding of Finger Movements Using Skellam-Based Maximum-Likelihood Decoding

Hyun‐Chool Shin, Vikram Aggarwal, Soumyadipta Acharya et al. · IEEE Transactions on Biomedical Engineering · 2009

We present an optimal method for decoding the activity of primary motor cortex (M1) neurons in a nonhuman primate during single finger movements. The method is based on the maximum-likelihood (ML) inference, which assuming the probability of finger movements is uniform, is equivalent to the maximum a posteriori (MAP) inference. Each neuron's activation is first quantified by the change in firing rate before and after finger movement. We then estimate the probability density function of this activation given finger movement, i.e., Pr(neuronal activation (x) | finger movements (m)). Based on the ML criterion, we choose finger movements to maximize Pr(x |m). Experimentally, data were collected from 115 task-related neurons in M1 as the monkey performed flexion and extension of each finger and the wrist (12 movements). With as few as 20--25 randomly selected neurons, the proposed method decoded single-finger movements with 99% accuracy. Since the training and decoding procedures in the proposed method are simple and computationally efficient, the method can be extended for real-time neuroprosthetic control of a dexterous hand.

Cited by 52

Approaching Maximum Likelihood Decoding Performance via Reshuffling ORBGRAND

Li Wan, Wenyi Zhang · 2024

Guessing random additive noise decoding (GRAND) is a recently proposed decoding paradigm particularly suitable for codes with short length and high rate. Among its variants, ordered reliability bits GRAND (ORBGRAND) exploits soft information in a simple and effective fashion to schedule its queries, thereby allowing efficient hardware implementation. Compared with maximum likelihood (ML) decoding, however, ORBGRAND still exhibits noticeable performance loss in terms of block error rate (BLER). In order to improve the performance of ORBGRAND while still retaining its amenability to hardware implementation, a new variant of ORBGRAND termed RS-ORBGRAND is proposed, whose basic idea is to reshuffle the queries of ORBGRAND so that the expected number of queries is minimized. Numerical simulations show that RS-ORBGRAND leads to noticeable gains compared with ORBGRAND and its existing variants, and is only 0.1dB away from ML decoding, for BLER as low as 10-6.

Cited by 1

Maximum likelihood syndrome decoding of linear block codes

A. J. Han Vinck · Data Archiving and Networked Services (DANS) · 1978

Cited by 1Open access

Maximum-likelihood soft decision decoding of BCH codes

Alexander Vardy, Y. Be'ery · IEEE Transactions on Information Theory · 1994

The problem of efficient maximum-likelihood soft decision decoding of binary BCH codes is considered. It is known that those primitive BCH codes whose designed distance is one less than a power of two, contain subcodes of high dimension which consist of a direct-sum of several identical codes. The authors show that the same kind of direct-sum structure exists in all the primitive BCH codes, as well as in the BCH codes of composite block length. They also introduce a related structure termed the "concurring-sum", and then establish its existence in the primitive binary BCH codes. Both structures are employed to upper bound the number of states in the minimal trellis of BCH codes, and develop efficient algorithms for maximum-likelihood soft decision decoding of these codes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

Cited by 137

On Maximum-Likelihood Detection via Improved Sphere Decoding

Bowen Wang, Jingzhou Wu, Hang Yin et al. · 2022

This paper presents a design and implementation of a MIMO detector for bit Log-Likelihood Ratios (LLRs) output. Inspired by the insight that calculating <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> bit LLRs is equivalent to finding <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N+1$</tex> optimal symbols, we propose a piecewise scheme consisting of one global and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> parallel local optimizations for Maximum Likelihood (ML) detection of multidimensional constellations. For this purpose, we design a modified Sphere Decoding (SD) algorithm supporting specific sub-sphere searching utilizing the unvarying pattern of the QAM constellation. The proposed algorithm theoretically consumes only twice the time of SD to achieve ML detection. Experiments in various cases validate the near-capacity performance.

Cited by 1

Efficient maximum-likelihood decoding of spherical lattice codes

Karen Su, I. Berenguer, Ian Wassell et al. · IEEE Transactions on Communications · 2009

A new framework for efficient exact maximum-likelihood (ML) decoding of spherical lattice codes is developed. It employs a double-tree structure: The first is that which underlies established tree-search decoders; the second plays the crucial role of guiding the primary search by specifying admissible candidates and is our present focus. Lattice codes have long been of interest due to their rich structure, leading to decoding algorithms for unbounded lattices, as well as those with axis-aligned rectangular shaping regions. Recently, spherical Lattice Space-Time (LAST) codes were proposed to realize the optimal diversity-multiplexing tradeoff of MIMO channels. We address the so-called boundary control problem arising from the spherical shaping region defining these codes. This problem is complicated because of the varying number of candidates to consider at each search stage; it is not obvious how to address it effectively within the frameworks of existing decoders. Our proposed strategy is compatible with all sequential tree-search detectors, as well as auxiliary processing such as the MMSEGDFE and lattice reduction. We demonstrate the superior performance and complexity profiles achieved when applying the proposed boundary control in conjunction with two current efficient ML detectors and show an improvement of 1dB over the state-of-the-art at a comparable complexity.

Cited by 8

Approaching Maximum Likelihood Decoding Performance via Reshuffling ORBGRAND

Li Wan, Wenyi Zhang · arXiv (Cornell University) · 2024

Cited by 0Open access