What is he best known for?
The fields of study he is best known for:
- Statistics
- Telecommunications
- Algorithm
His primary areas of study are Decoding methods, Algorithm, Theoretical computer science, Electronic engineering and Information theory.
His study of Concatenated error correction code is a part of Decoding methods.
His work carried out in the field of Algorithm brings together such families of science as Detection theory and Automatic repeat request.
Alex Grant has researched Electronic engineering in several fields, including Rayleigh fading, Frequency-division multiplexing, Orthogonal frequency-division multiplexing, Signal and Computer network.
His Information theory study combines topics from a wide range of disciplines, such as Multicast, Entropy, Linear network coding and Topology.
His Entropy study combines topics in areas such as Discrete mathematics and Computation.
His most cited work include:
- Performance Analysis of MIMO-MRC in Double-Correlated Rayleigh Environments (261 citations)
- Iterative detection in code‐division multiple‐access with error control coding (261 citations)
- Rayleigh fading multi-antenna channels (169 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Algorithm, Decoding methods, Theoretical computer science, Communication channel and Discrete mathematics.
Algorithm is often connected to Mathematical optimization in his work.
His study looks at the relationship between Decoding methods and topics such as Electronic engineering, which overlap with Orthogonal frequency-division multiplexing.
His study in Communication channel is interdisciplinary in nature, drawing from both Signal and Topology.
His research on Discrete mathematics also deals with topics like
- Random variable, Linear programming and Information theory most often made with reference to Entropy,
- Linear network coding which connect with Multicast.
His Concatenated error correction code research is multidisciplinary, incorporating perspectives in Convolutional code and Linear code.
He most often published in these fields:
- Algorithm (35.37%)
- Decoding methods (28.86%)
- Theoretical computer science (21.14%)
What were the highlights of his more recent work (between 2011-2019)?
- Discrete mathematics (17.07%)
- Algorithm (35.37%)
- Entropy (11.38%)
In recent papers he was focusing on the following fields of study:
Alex Grant focuses on Discrete mathematics, Algorithm, Entropy, Linear network coding and Linear programming.
His Discrete mathematics research incorporates themes from Upper and lower bounds and Combinatorics, Lattice.
His biological study spans a wide range of topics, including Theoretical computer science and Single antenna interference cancellation, Communication channel.
Alex Grant combines subjects such as Logarithm, Mathematical optimization and Applied mathematics with his study of Entropy.
His Linear network coding research integrates issues from Computation and Inequality.
His Decoding methods research incorporates themes from Tuple, Topology, Information theory, Optical fiber and Signal design.
Between 2011 and 2019, his most popular works were:
- Lossy Broadcasting With Complementary Side Information (29 citations)
- PAM-SCFDE for Optical Wireless Communications (29 citations)
- Network Coding Capacity Regions via Entropy Functions (25 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Telecommunications
- Algorithm
His main research concerns Entropy, Discrete mathematics, Electronic engineering, Communications system and Algorithm.
The Entropy study combines topics in areas such as Logarithm, Mathematical optimization and Linear network coding.
As part of the same scientific family, Alex Grant usually focuses on Discrete mathematics, concentrating on Linear programming and intersecting with Probability distribution.
His biological study spans a wide range of topics, including Terminal, Forward error correction, Communication channel, Header and Bit stuffing.
His work on Decoding methods as part of general Algorithm research is frequently linked to Linear network, bridging the gap between disciplines.
His work in Decoding methods addresses issues such as Relay channel, which are connected to fields such as Hamming code and Theoretical computer science.
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