What is he best known for?
The fields of study he is best known for:
- Computer network
- Telecommunications
- Statistics
Gerhard Kramer mainly investigates Channel capacity, Computer network, Communication channel, Decoding methods and Transmitter.
His Channel capacity research integrates issues from Discrete mathematics, Random variable, Additive white Gaussian noise, MIMO and Gaussian noise.
In the field of Computer network, his study on Linear network coding, Network simulation, Active networking and Near-far problem overlaps with subjects such as Broadcast control channel.
His research in Communication channel intersects with topics in Relay channel and Topology.
The concepts of his Decoding methods study are interwoven with issues in Relay and Fading.
His work carried out in the field of Transmitter brings together such families of science as Encoder, Finite set, Multicast, Electronic engineering and Realization.
His most cited work include:
- Cooperative strategies and capacity theorems for relay networks (2590 citations)
- Capacity Limits of Optical Fiber Networks (1545 citations)
- Design of low-density parity-check codes for modulation and detection (979 citations)
What are the main themes of his work throughout his whole career to date?
Gerhard Kramer mainly focuses on Communication channel, Topology, Algorithm, Computer network and Electronic engineering.
Gerhard Kramer is interested in Channel capacity, which is a branch of Communication channel.
Gerhard Kramer interconnects Theoretical computer science, Random variable, Noise, MIMO and Broadcast channels in the investigation of issues within Topology.
The study incorporates disciplines such as Encoder and Binary number in addition to Algorithm.
In his work, Fading is strongly intertwined with Relay, which is a subfield of Computer network.
His Electronic engineering research incorporates elements of Optical fiber, Signal, Digital subscriber line and Communications system.
He most often published in these fields:
- Communication channel (28.19%)
- Topology (19.04%)
- Algorithm (18.31%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (18.31%)
- Upper and lower bounds (13.73%)
- Communication channel (28.19%)
In recent papers he was focusing on the following fields of study:
Algorithm, Upper and lower bounds, Communication channel, Key and Optical fiber are his primary areas of study.
In his study, Codebook is inextricably linked to Encoder, which falls within the broad field of Algorithm.
Specifically, his work in Communication channel is concerned with the study of Additive white Gaussian noise.
His biological study spans a wide range of topics, including Information theory, Attenuation, Spectral efficiency and Nonlinear system.
The Topology study combines topics in areas such as Phase noise, Relay, Noise and Sense.
His research in Decoding methods is mostly concerned with Low-density parity-check code.
Between 2016 and 2021, his most popular works were:
- Capacity Bounds for Discrete-Time, Amplitude-Constrained, Additive White Gaussian Noise Channels (31 citations)
- Privacy, Secrecy, and Storage With Multiple Noisy Measurements of Identifiers (27 citations)
- Secure and Reliable Key Agreement with Physical Unclonable Functions. (22 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Telecommunications
- Statistics
Gerhard Kramer focuses on Algorithm, Communication channel, Upper and lower bounds, Binary number and Encoder.
His Algorithm study combines topics in areas such as Probability distribution and Lossy compression.
Gerhard Kramer studies Additive white Gaussian noise, a branch of Communication channel.
His study on Upper and lower bounds also encompasses disciplines like
- Entropy which connect with Invertible matrix, Exponential growth and Relay,
- Random variable, which have a strong connection to Telecommunications link and Mathematical optimization.
His research in Topology focuses on subjects like Noise, which are connected to Optical fiber.
He has included themes like Markov process and Markov chain in his Decoding methods study.
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