1.5k citations · 2k across the 19 of their papers we have counts for
9 papers · 1 filter
Sparse MIMO-OFDM Channel Estimation via RKHS Regularization
James Delfeld, Gian Marti, Chris Dick
We propose a method for channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless communication systems. The method exp…
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…
Low Complexity Hybrid Beamforming for mmWave Full-Duplex Integrated Access and Backhaul
Elyes Balti, Chris Dick, Brian L. Evans
We consider an integrated access and backhaul (IAB) node operating in full-duplex (FD) mode. We analyze simultaneous transmission from the New Radio gNB to the IAB node on the back…
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…
Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard
Debashri Roy, Batool Salehi, Stella Banou +7
Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguratio…
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…
Signal Processing Based Deep Learning for Blind Symbol Decoding and Modulation Classification
Samer Hanna, Chris Dick, Danijela Cabric
Blindly decoding a signal requires estimating its unknown transmit parameters, compensating for the wireless channel impairments, and identifying the modulation type. While deep le…
Combining Deep Learning and Linear Processing for Modulation Classification and Symbol Decoding
Samer Hanna, Chris Dick, Danijela Cabric
Deep learning has been recently applied to many problems in wireless communications including modulation classification and symbol decoding. Many of the existing end-to-end learnin…