D-Index & Metrics Best Publications

Ralf Koetter

Technical University of Munich
Germany

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name Citations Publications World Ranking National Ranking

Computer Science D-index 52 Citations 18,947 140 World Ranking 3283 National Ranking 145

Overview

What is he best known for?

The fields of study he is best known for:

Computer network
Algorithm
Statistics

Ralf Koetter mainly investigates Decoding methods, Linear network coding, Computer network, Multicast and Wireless network. His Decoding methods research is multidisciplinary, incorporating perspectives in Discrete mathematics and Communication channel. He combines subjects such as Turbo equalizer, Data transmission and Equalizer with his study of Communication channel.

Ralf Koetter has researched Linear network coding in several fields, including Theoretical computer science, Distributed computing, Grassmannian, Node and Johnson bound. His Computer network study frequently links to adjacent areas such as Channel capacity. His Multicast study frequently draws connections between related disciplines such as Information theory.

His most cited work include:

An algebraic approach to network coding (2452 citations)
A Random Linear Network Coding Approach to Multicast (2396 citations)
Turbo equalization: principles and new results (1142 citations)

What are the main themes of his work throughout his whole career to date?

Decoding methods, Algorithm, Linear network coding, Computer network and Communication channel are his primary areas of study. His work carried out in the field of Decoding methods brings together such families of science as Discrete mathematics and Error detection and correction. The various areas that Ralf Koetter examines in his Algorithm study include Pixel, Theoretical computer science and Code.

His Linear network coding research includes themes of Distributed computing, Multicast, Throughput, Wireless network and Topology. His Distributed computing research incorporates elements of Information theory and Stochastic geometry models of wireless networks. His Computer network course of study focuses on Wireless and Coding.

He most often published in these fields:

Decoding methods (44.17%)
Algorithm (37.42%)
Linear network coding (26.38%)

What were the highlights of his more recent work (between 2008-2014)?

Linear network coding (26.38%)
Computer network (20.86%)
Decoding methods (44.17%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Linear network coding, Computer network, Decoding methods, Communication channel and Channel capacity. His studies deal with areas such as Distributed computing, Multicast, Scheduling and Throughput as well as Linear network coding. His Distributed computing research includes elements of Network topology and Wireless network.

His work on Network packet, Peer-to-peer, Distributed data store and Routing as part of general Computer network study is frequently connected to Block, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Decoding methods study is concerned with Algorithm in general. His study on Communication channel also encompasses disciplines like

Bounding overwatch which is related to area like Unicast and Mathematical optimization,
Topology that intertwine with fields like Theoretical computer science.

Between 2008 and 2014, his most popular works were:

Adaptive network coding for broadcast channels (115 citations)
Beyond Shannon: the quest for fundamental performance limits of wireless ad hoc networks (90 citations)
A Theory of Network Equivalence— Part I: Point-to-Point Channels (81 citations)

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.