What is he best known for?
The fields of study he is best known for:
- Algorithm
- Statistics
- Computer network
His primary scientific interests are in Algorithm, Decoding methods, Concatenated error correction code, Low-density parity-check code and Turbo code.
His work on Communication channel expands to the thematically related Decoding methods.
His Concatenated error correction code study integrates concerns from other disciplines, such as Channel code, Additive white Gaussian noise, Modulation and Precoding.
Krishna R. Narayanan interconnects Forward error correction, Code word, Coding and Binary number in the investigation of issues within Low-density parity-check code.
His research in Turbo code tackles topics such as Theoretical computer science which are related to areas like Transmission and Turbo equalizer.
The study incorporates disciplines such as Electronic engineering and Bit error rate in addition to Convolutional code.
His most cited work include:
- Joint Physical Layer Coding and Network Coding for Bidirectional Relaying (430 citations)
- LDPC-based space-time coded OFDM systems over correlated fading channels: Performance analysis and receiver design (255 citations)
- Weakly Secure Network Coding (243 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Algorithm, Decoding methods, Communication channel, Theoretical computer science and Low-density parity-check code.
Concatenated error correction code, Turbo code, Serial concatenated convolutional codes, Sequential decoding and Block code are the subjects of his Algorithm studies.
He combines subjects such as Forward error correction, Convolutional code, Linear code and Error floor with his study of Concatenated error correction code.
His work on Reed–Solomon error correction as part of general Decoding methods research is frequently linked to Concatenation, bridging the gap between disciplines.
His studies in Communication channel integrate themes in fields like Transmitter and Topology.
His work in Theoretical computer science addresses subjects such as Binary erasure channel, which are connected to disciplines such as Binary symmetric channel.
He most often published in these fields:
- Algorithm (56.72%)
- Decoding methods (46.27%)
- Communication channel (30.22%)
What were the highlights of his more recent work (between 2018-2021)?
- Algorithm (56.72%)
- Decoding methods (46.27%)
- Compressed sensing (7.09%)
In recent papers he was focusing on the following fields of study:
Krishna R. Narayanan mainly focuses on Algorithm, Decoding methods, Compressed sensing, Random access and Group testing.
The various areas that Krishna R. Narayanan examines in his Algorithm study include Additive white Gaussian noise, Communication channel and Statistical model.
His Decoding methods research includes elements of Coding and Parity bit.
His work deals with themes such as Forward error correction and Block code, which intersect with Parity bit.
The Random access study which covers Belief propagation that intersects with Concatenated error correction code.
His study in Group testing is interdisciplinary in nature, drawing from both False alarm and Pooling.
Between 2018 and 2021, his most popular works were:
- Variational Graph Recurrent Neural Networks (33 citations)
- A Joint Graph Based Coding Scheme for the Unsourced Random Access Gaussian Channel (29 citations)
- Semi-Implicit Graph Variational Auto-Encoders (23 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Statistics
- Telecommunications
Krishna R. Narayanan mostly deals with Decoding methods, False alarm, Sample size determination, Pooling and Identification.
His Decoding methods study introduces a deeper knowledge of Algorithm.
His Algorithm research is multidisciplinary, relying on both Coding and Communication channel.
His Communication channel research incorporates themes from Computational complexity theory, Adder, Block code, Tree and Parity bit.
His study in False alarm is interdisciplinary in nature, drawing from both Group testing and Statistical power.
He interconnects Transmission, Distributed computing and Asynchronous communication in the investigation of issues within Random access.
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