Publications de Michael Christoph Gastpar

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Maximum Throughput Gain of Compute-and-Forward for Multiple Unicast

Michael Christoph Gastpar, Jasper Goseling

Compute-and-Forward (CF), also known as reliable physical layer network coding, is a technique that provides the possibility of exploiting the features of broadcast and superposition in wireless networks. It has been shown that the throughput for multiple ...

Institute of Electrical and Electronics Engineers2014

Integer-forcing Linear Receivers

Michael Christoph Gastpar

Linear receivers are often used to reduce the implementation complexity of multiple-antenna systems. In a traditional linear receiver architecture, the receive antennas are used to separate out the codewords sent by each transmit antenna, which can then be ...

Institute of Electrical and Electronics Engineers2014

Capacity Scaling of Cognitive Networks: Beyond Interference-Limited Communication

Michael Christoph Gastpar

The capacity scaling laws of two overlaid networks are investigated, which are located in the same area sharing the same wireless resources with different priorities. The primary network can be regarded as an existing communication system operated in a lic ...

Institute of Electrical and Electronics Engineers2014

Subject-specific modulation of local field potential spectral power during brain–machine interface control in primates

Michael Christoph Gastpar

Objective. Intracortical brain–machine interfaces (BMIs) have predominantly utilized spike activity as the control signal. However, an increasing number of studies have shown the utility of local field potentials (LFPs) for decoding motor related signals. ...

Institute of Physics2014

Asymmetric Compute-and-Forward with CSIT

Michael Christoph Gastpar, Jingge Zhu

We present a modified compute-and-forward scheme which utilizes Channel State Information at the Transmitters (CSIT) in a natural way. The modified scheme allows different users to have different coding rates, and use CSIT to achieve larger rate region. Th ...

2014

Successive Refinement of Gaussian Projections

Michael Christoph Gastpar, Guillaume Jean Op 't Veld

Successive refinement is a technique to encode a source iteratively, improving the distortion at each step. If the code rates of all steps combined can be as efficient as coding only for the final distortion in one go, then the source is said to be success ...

2014

On Distributed Successive Refinement with Lossless Recovery

Michael Christoph Gastpar, Chien-Yi Wang

The problem of successive refinement in distributed source coding and in joint source-channel coding is considered. The emphasis is placed on the case where the sources have to be recovered losslessly in the second stage. In distributed source coding, it i ...

2014

Compute-and-Forward: Finding the Best Equation

Michael Christoph Gastpar, Saeid Sahraei

Compute-and-Forward is an emerging technique to deal with interference. It allows the receiver to decode a suitably chosen integer linear combination of the transmitted messages. The integer coefficients should be adapted to the channel fading state. Optim ...

2014

Gaussian (dirty) multiple access channels: a compute-and-forward perspective

Michael Christoph Gastpar, Jingge Zhu

Lattice codes are applied to the two-user Gaussian multiple access channel (MAC) combined with a modified compute-and-forward transmitting scheme. It is shown that non-corner points on the boundary of the capacity region can be achieved by decoding two int ...

IEEE2014

Michael Christoph Gastpar

Graph Chatbot

Chattez avec Graph Search

Maximum Throughput Gain of Compute-and-Forward for Multiple Unicast

Integer-forcing Linear Receivers

Capacity Scaling of Cognitive Networks: Beyond Interference-Limited Communication

Subject-specific modulation of local field potential spectral power during brain–machine interface control in primates

Asymmetric Compute-and-Forward with CSIT

Successive Refinement of Gaussian Projections

On Distributed Successive Refinement with Lossless Recovery

Compute-and-Forward: Finding the Best Equation

Compute-and-forward for discrete memoryless networks

Gaussian (dirty) multiple access channels: a compute-and-forward perspective

Subject-specific modulation of local field potential spectral power during brain–machine interface control in primates

Compute-and-forward for discrete memoryless networks

Successive Refinement of Gaussian Projections

On Distributed Successive Refinement with Lossless Recovery

Compute-and-Forward: Finding the Best Equation

Maximum Throughput Gain of Compute-and-Forward for Multiple Unicast

Capacity Scaling of Cognitive Networks: Beyond Interference-Limited Communication

Asymmetric Compute-and-Forward with CSIT

Integer-forcing Linear Receivers

Gaussian (dirty) multiple access channels: a compute-and-forward perspective