Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The viterbi algorithm
19733.8k citationsG. David ForneyProceedings of the IEEEprofile →
Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference
19721.7k citationsG. David ForneyIEEE Transactions on Information Theoryprofile →
On the design of low-density parity-check codes within 0.0045 dB of the Shannon limit
Countries citing papers authored by G. David Forney
Since
Specialization
Citations
This map shows the geographic impact of G. David Forney's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by G. David Forney with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites G. David Forney more than expected).
This network shows the impact of papers produced by G. David Forney. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by G. David Forney. The network helps show where G. David Forney may publish in the future.
Co-authorship network of co-authors of G. David Forney
This figure shows the co-authorship network connecting the top 25 collaborators of G. David Forney.
A scholar is included among the top collaborators of G. David Forney based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with G. David Forney. G. David Forney is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Forney, G. David, et al.. (2007). Channel Coding: The Road to Channel Capacity Fifty years of effort and invention have finally produced coding schemes that closely approach Shannon's channel capacity limit on memoryless communication channels.. Proceedings of the IEEE. 95(6). 1150–1177.6 indexed citations
Feigenbaum, Joan, G. David Forney, Brian Marcus, Robert J. McEliece, & Alexander Vardy. (1996). Introduction to the special issue on codes and complexity.. IEEE Transactions on Information Theory. 42. 1649–1659.8 indexed citations
Loeliger, Hans‐Andrea, G. David Forney, Thomas Mittelholzer, & Mitchell Trott. (1994). Minimality and observability of group systems. Linear Algebra and its Applications. 205-206. 937–963.42 indexed citations
9.
Calderbank, Robert, G. David Forney, & Nader Moayeri. (1993). Coding and Quantization.6 indexed citations
Forney, G. David. (1975). Minimal Bases of Rational Vector Spaces, with Applications to Multivariable Linear Systems. SIAM Journal on Control. 13(3). 493–520.497 indexed citations breakdown →
14.
Forney, G. David. (1974). Convolutional codes. II - Maximum-likelihood decoding. III - Sequential decoding. Information and Computation. 25.1 indexed citations
Forney, G. David. (1972). Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference. IEEE Transactions on Information Theory. 18(3). 363–378.1710 indexed citations breakdown →
Forney, G. David. (1970). Convolutional codes I: Algebraic structure. IEEE Transactions on Information Theory. 16(6). 720–738.454 indexed citations breakdown →
20.
Forney, G. David. (1965). Concatenated codes Technical report 440. NASA Technical Reports Server (NASA).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.