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John K. Karlof

dblp:11/635 · DBLP profile ↗
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8ranked-venue papers
3as first author
0since 2021 · last 1999
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 8 · 3 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
8 papers
Coding theory · 96% Mathematical optimization · 2% Information theory · 2%
Computer networks
2 papers
Physical-layer communications · 100%

Topics — the 14 heaviest of 15, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory › error-correcting codes › combinatorial coding theory
permutation codes
0.161999
Representing group codes as permutation codes · IEEE Trans. Inf. Theory 1999
Optimal permutation codes for the Gaussian channel · IEEE Trans. Inf. Theory 1997
Decoding spherical codes for the Gaussian channel · IEEE Trans. Inf. Theory 1993
Coding theory › error-correcting codes › block codes
group codes
0.171999
Representing group codes as permutation codes · IEEE Trans. Inf. Theory 1999
Optimal permutation codes for the Gaussian channel · IEEE Trans. Inf. Theory 1997
Permutation codes for the Gaussian channel · IEEE Trans. Inf. Theory 1989
Coding theory
error-correcting codes
0.031997
Optimal permutation codes for the Gaussian channel · IEEE Trans. Inf. Theory 1997
Decoding spherical codes for the Gaussian channel · IEEE Trans. Inf. Theory 1993
Permutation codes for the Gaussian channel · IEEE Trans. Inf. Theory 1989
Coding theory › error-correcting codes
code construction
0.021997
Optimal permutation codes for the Gaussian channel · IEEE Trans. Inf. Theory 1997
Permutation codes for the Gaussian channel · IEEE Trans. Inf. Theory 1989
Coding theory › error-correcting codes › code classification
code equivalence
0.011999
Representing group codes as permutation codes · IEEE Trans. Inf. Theory 1999
Coding theory › error-correcting codes
decoding
0.021999
Decoding spherical codes for the Gaussian channel · IEEE Trans. Inf. Theory 1993
Representing group codes as permutation codes · IEEE Trans. Inf. Theory 1999
Coding theory › error-correcting codes › decoding › decoding algorithms › optimal decoding
maximum-likelihood decoding
0.011993
Decoding spherical codes for the Gaussian channel · IEEE Trans. Inf. Theory 1993
Coding theory › error-correcting codes › decoding › sequential decoding
stack decoding
0.011999
Representing group codes as permutation codes · IEEE Trans. Inf. Theory 1999
Mathematical optimization › continuous optimization
nonlinear optimization
0.011997
Optimal permutation codes for the Gaussian channel · IEEE Trans. Inf. Theory 1997
Physical-layer communications › channel modeling
gaussian channel
0.021993
Decoding spherical codes for the Gaussian channel · IEEE Trans. Inf. Theory 1993
Permutation codes for the Gaussian channel · IEEE Trans. Inf. Theory 1989
Information theory › channel capacity
gaussian channel
0.021978
Optimal [M, 3] group codes for the Gaussian channel (Corresp.) · IEEE Trans. Inf. Theory 1978
On the existence of [M, n] group codes for the Gaussian channel with [M, n] odd · IEEE Trans. Inf. Theory 1977
Information theory › network information theory › broadcast channel
gaussian broadcast channel
0.011980
Group codes for the Gaussian broadcast channel with two receivers · IEEE Trans. Inf. Theory 1980
Coding theory › error-correcting codes
optimal codes
0.011978
Optimal [M, 3] group codes for the Gaussian channel (Corresp.) · IEEE Trans. Inf. Theory 1978
Combinatorics and discrete mathematics › representation theory
group representation theory
0.011977
On the existence of [M, n] group codes for the Gaussian channel with [M, n] odd · IEEE Trans. Inf. Theory 1977

Methods — techniques the papers use, named apart from their topics

group representation theory · 0.0iterative decoding · 0.0correlation decoding · 0.0minimum distance computation · 0.0geometric programming · 0.0numerical optimization · 0.0initial vector construction · 0.0orthogonal matrix groups · 0.0subgroup partitioning · 0.0sylow subgroup analysis · 0.0
YearPublicationVenuePosition
1999 Representing group codes as permutation codes
abstract
Given an abstract group /spl Gscr/, an N-dimensional orthogonal matrix representation G of /spl Gscr/, and an "initial vector" x/spl isin/R/sup N/, Slepian defined the group code generated by the representation G to be the set of vectors Gx. If G is a group of permutation matrices, the set Gx is called a "permutation code". For permutation codes a "stack algorithm" decoder exists that, in the presence of low noise, produces the maximum-likelihood estimate of the transmitted vector by using far fewer computations than the standard decoder. In this correspondence, a new concept of equivalence of codes of different dimensions is presented which is weaker than the usual definition of equivalent codes. We show that every group code is (weakly) equivalent to a permutation code and we discuss the minimal degree of this permutation code.
Ezio Biglieri, John K. Karlof, Emanuele Viterbo
IEEE Trans. Inf. Theory2
1997 Optimal permutation codes for the Gaussian channel
abstract
This correspondence contains a continuation of the table contained in Karlof (1989) listing the minimum distance of optimal permutation codes generated by primitive permutation groups of degree less than or equal to 20. We use a nonlinear programming algorithm developed using geometric programming techniques.
John K. Karlof, Yaw O. Chang
IEEE Trans. Inf. Theory1
1993 Decoding spherical codes for the Gaussian channel
abstract
A decoding algorithm for permutation codes that is equivalent to maximum-likelihood decoding, but less complex than the correlation decoder, is presented. A general construction for iteratively maximum-likelihood decodable (IMLD) codes is proved and used to construct IMLD codes for every dimension n. D. Slepian (1965) defined permutation modulation codes and presented an efficient algorithm for their decoding. Slepian's decoding scheme is one of the principal components of the permutation code decoding algorithm presented.>
John K. Karlof
IEEE Trans. Inf. Theory1
1989 Permutation codes for the Gaussian channel
abstract
The initial vector problem is considered for group codes for the Gaussian channel and, in particular, for codes generated by permutation groups. Variant I and variant II permutation codes are defined, and optimal initial vectors for these codes are constructed. A numerical algorithm for constructing optimal group codes is presented together with examples of this algorithm applied to several permutation groups.>
John K. Karlof
IEEE Trans. Inf. Theory1
1983 Group codes for the M-receiver Gaussian broadcast channel
abstract
TheM-receiver Gaussian broadcast channel is a communication system in which a single codeword is transmitted overMdistinct Gaussian channels and is received byMreceivers. The receivers have no contact with each other and the channels have different signal-to-noise ratios. The purpose of this correspondence is to define the concept of group code for theM-receiver Gaussian broadcast channel and study permutation codes as a special case. Such a code is generated by an initial vectorx, a groupGof orthogonaln-by-nmatrices, and a sequence of subgroups ofG.
Charles P. Downey, John K. Karlof
IEEE Trans. Inf. Theory2
1980 Group codes for the Gaussian broadcast channel with two receivers
abstract
The two-receiver Gaussian broadcast channel is a model of a communication system where a single codeword is transmitted over two distinct Gaussian channels that have different signal-to, noise ratios. The receiver with the better signal-to-noise ratio decodes all of the information carried by the codeword, while the other receiver decodes only some of the information. The concept of group codes for the Gaussian broadcast channel is developed and applied to permutation codes for the Gaussian broadcast channel. A group code for the Gaussian broadcast channel is a group code for the single-user Gaussian channel in which a subgroup of the generating matrix group is used to partition the codewords into disjoint subsets called clouds. Distinct clouds represent distinct messages for the noisier channel while the individual codewords represent distinct messages for the other channel. The clouds share some of the distance properties of group codes. Necessary and sufficient conditions are given for finding good group codes for the Gaussian broadcast channel (in terms of minimum distance).
Charles P. Downey, John K. Karlof
IEEE Trans. Inf. Theory2
1978 Optimal [M, 3] group codes for the Gaussian channel (Corresp.)
Charles P. Downey, John K. Karlof
IEEE Trans. Inf. Theory2
1977 On the existence of [M, n] group codes for the Gaussian channel with [M, n] odd
abstract
The question of the existence of nonplanar[M,n]group codes for the Gaussian channel has been settled except in the case ofnodd andModd and composite. For this unsettled case, it is shown that the existence of a nonplanar[M,n]group code is implied by the existence of a groupGsatisfying i)|G|is even, ii)Ghas a faithful complex irreducible representationTof the first kind, and iii)Trestricted to the two-Sylow subgroup ofGcontains the identity representation. A partial converse of this existence result is also given. Finally, it is shown that for each oddnnot of the form2^{m} - 1, there exists a nonplanar[M,n ]group code withModd and composite.
Charles P. Downey, John K. Karlof
IEEE Trans. Inf. Theory2