no code implementations • 17 Nov 2023 • Nicolas Zilberstein, Ananthram Swami, Santiago Segarra
We propose a joint channel estimation and data detection algorithm for massive multilple-input multiple-output systems based on diffusion models.
no code implementations • 8 May 2023 • Nicolas Zilberstein, Ashutosh Sabharwal, Santiago Segarra
We propose a solution for linear inverse problems based on higher-order Langevin diffusion.
no code implementations • 2 Feb 2023 • Fernando Gama, Nicolas Zilberstein, Martin Sevilla, Richard Baraniuk, Santiago Segarra
Thus, the crux of particle filters lies in designing sampling distributions that are both easy to sample from and lead to accurate estimators.
1 code implementation • 26 Oct 2022 • Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the \emph{underdamped} Langevin (stochastic) dynamic.
1 code implementation • 11 May 2022 • Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
Based on the proposed MIMO detector, we also design a robust version of the method by unfolding and parameterizing one term -- the score of the likelihood -- by a neural network.
1 code implementation • 24 Feb 2022 • Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem.
no code implementations • 13 Oct 2021 • Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
Our method is based on hypernetworks that generate the parameters of a neural network-based detector that works well on a specific channel.
1 code implementation • 6 Oct 2021 • Fernando Gama, Nicolas Zilberstein, Richard G. Baraniuk, Santiago Segarra
Particle filtering is used to compute good nonlinear estimates of complex systems.