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Michael Gastpar

Michael Gastpar

École Polytechnique Fédérale de Lausanne
Switzerland

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

Electronics and Electrical Engineering D-index 45 Citations 13,441 218 World Ranking 2116 National Ranking 50

Computer Science D-index 45 Citations 13,861 281 World Ranking 4502 National Ranking 105

Research.com Recognitions

Awards & Achievements

2017 - IEEE Fellow For contributions to network information theory

Overview

What is he best known for?

The fields of study Michael Gastpar is best known for:

Distortion (music)
Amplifier
Computer network

Michael Gastpar applies the principles of Ergodic theory and Upper and lower bounds in his work under Mathematical analysis. His Mathematical analysis research extends to Upper and lower bounds, which is thematically connected. His Computer network study frequently links to other fields, such as Random access. Random access and Computer network are frequently intertwined in his study. Many of his studies involve connections with topics such as Linear network coding and Network packet. He combines topics linked to Network packet with his work on Linear network coding. His work on Decoding methods is being expanded to include thematically relevant topics such as Single antenna interference cancellation. He combines topics linked to Telecommunications with his work on Single antenna interference cancellation. His study in Decoding methods extends to Telecommunications with its themes.

His most cited work include:

The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing (100 citations)
Random Access With Physical-Layer Network Coding (35 citations)
A Survey on Interference Networks: Interference Alignment and Neutralization (32 citations)

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

His Mathematical analysis study frequently intersects with other fields, such as Upper and lower bounds and Bounded function. His Mathematical analysis research extends to Bounded function, which is thematically connected. In the field of Statistics Michael Gastpar connects related research areas like Coding (social sciences) and Signal-to-noise ratio (imaging). Geometry is intertwined with Converse and Scaling in his research. His Scaling study frequently draws connections between related disciplines such as Geometry. His Programming language study frequently draws connections to other fields, such as Set (abstract data type) and Constant (computer programming). Set (abstract data type) and Programming language are commonly linked in his work. In most of his Channel (broadcasting) studies, his work intersects topics such as Transmitter. Algorithm connects with themes related to Gaussian noise in his study.

Michael Gastpar most often published in these fields:

Channel (broadcasting) (68.75%)
Mathematical analysis (68.75%)
Algorithm (62.50%)

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.

Best Publications

Cooperative strategies and capacity theorems for relay networks

G. Kramer;M. Gastpar;P. Gupta.
IEEE Transactions on Information Theory (2005)

3066 Citations

Compute-and-Forward: Harnessing Interference Through Structured Codes

B. Nazer;M. Gastpar.
IEEE Transactions on Information Theory (2011)

1070 Citations

On the capacity of wireless networks: the relay case

M. Gastpar;M. Vetterli.
international conference on computer communications (2002)

673 Citations

Computation Over Multiple-Access Channels

B.. Nazer;M.. Gastpar.
international symposium on information theory (2007)

588 Citations

To code, or not to code: lossy source-channel communication revisited

M. Gastpar;B. Rimoldi;M. Vetterli.
IEEE Transactions on Information Theory (2003)

547 Citations

On Capacity Under Receive and Spatial Spectrum-Sharing Constraints

M. Gastpar.
allerton conference on communication, control, and computing (2007)

483 Citations

Uncoded transmission is exactly optimal for a simple Gaussian "sensor" network

M. Gastpar.
information theory and applications (2007)

419 Citations

On the capacity of large Gaussian relay networks

M. Gastpar;M. Vetterli.
IEEE Transactions on Information Theory (2005)

404 Citations

Source-channel communication in sensor networks

Michael Gastpar;Martin Vetterli.
information processing in sensor networks (2003)

337 Citations

Reliable Physical Layer Network Coding

B Nazer;M Gastpar.
Proceedings of the IEEE (2011)

320 Citations

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Michael Gastpar

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.

Research.com Recognitions

Awards & Achievements

Overview

What is he best known for?

The fields of study Michael Gastpar is best known for:

His most cited work include:

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

Michael Gastpar most often published in these fields:

Best Publications

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