Demo Data from VLDB 2026 / reviewers in the wild / expert
John K. Karlof
dblp:11/635
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › error-correcting codes › combinatorial coding theory
permutation codes |
0.1 | 6 | 1999 | 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 |
0.1 | 7 | 1999 | 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 |
0.0 | 3 | 1997 | 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 |
0.0 | 2 | 1997 | 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 |
0.0 | 1 | 1999 | Representing group codes as permutation codes · IEEE Trans. Inf. Theory 1999 |
Coding theory › error-correcting codes
decoding |
0.0 | 2 | 1999 | 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 |
0.0 | 1 | 1993 | Decoding spherical codes for the Gaussian channel · IEEE Trans. Inf. Theory 1993 |
Coding theory › error-correcting codes › decoding › sequential decoding
stack decoding |
0.0 | 1 | 1999 | Representing group codes as permutation codes · IEEE Trans. Inf. Theory 1999 |
Mathematical optimization › continuous optimization
nonlinear optimization |
0.0 | 1 | 1997 | Optimal permutation codes for the Gaussian channel · IEEE Trans. Inf. Theory 1997 |
Physical-layer communications › channel modeling
gaussian channel |
0.0 | 2 | 1993 | 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 |
0.0 | 2 | 1978 | 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 |
0.0 | 1 | 1980 | Group codes for the Gaussian broadcast channel with two receivers · IEEE Trans. Inf. Theory 1980 |
Coding theory › error-correcting codes
optimal codes |
0.0 | 1 | 1978 | Optimal [M, 3] group codes for the Gaussian channel (Corresp.) · IEEE Trans. Inf. Theory 1978 |
Combinatorics and discrete mathematics › representation theory
group representation theory |
0.0 | 1 | 1977 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1999 | Representing group codes as permutation codesabstractGiven 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. Theory | 2 |
| 1997 | Optimal permutation codes for the Gaussian channelabstractThis 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. Theory | 1 |
| 1993 | Decoding spherical codes for the Gaussian channelabstractA 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. Theory | 1 |
| 1989 | Permutation codes for the Gaussian channelabstractThe 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. Theory | 1 |
| 1983 | Group codes for the M-receiver Gaussian broadcast channelabstractTheM-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. Theory | 2 |
| 1980 | Group codes for the Gaussian broadcast channel with two receiversabstractThe 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. Theory | 2 |
| 1978 | Optimal [M, 3] group codes for the Gaussian channel (Corresp.)
Charles P. Downey, John K. Karlof |
IEEE Trans. Inf. Theory | 2 |
| 1977 | On the existence of [M, n] group codes for the Gaussian channel with [M, n] oddabstractThe 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. Theory | 2 |