1 citations · 2 across the 17 of their papers we have counts for
6 papers · 1 filter
Joint channel estimation and data detection in massive MIMO systems based on diffusion models
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. Our proposed method solves the bli…
Unsupervised Learning of Sampling Distributions for Particle Filters
Fernando Gama, Nicolas Zilberstein, Martin Sevilla +2
Accurate estimation of the states of a nonlinear dynamical system is crucial for their design, synthesis, and analysis. Particle filters are estimators constructed by simulating tr…
Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the \emph{underdamped} Langevin (stochastic) dynamic. Our detector achieves state-of-the…
Annealed Langevin Dynamics for Massive MIMO Detection
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
Solving the optimal symbol detection problem in multiple-input multiple-output (MIMO) systems is known to be NP-hard. Hence, the objective of any detector of practical relevance is…
Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Hence, the objective of any detector of practical relevance is to get r…
Robust MIMO Detection using Hypernetworks with Learned Regularizers
Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady +2
Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Recently, there has been a growing interest to get reasonably close to…