2 citations · 5 across the 6 of their papers we have counts for
9 papers · 1 filter
Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection
Ibrahim Kilinc, Ryan M. Dreifuerst, Junghoon Kim +1
Codebook-based beam selection is one approach for configuring millimeter wave communication links. The overhead required to reconfigure the transmit and receive beam pair, though,…
Neural Codebook Design for Network Beam Management
Ryan M. Dreifuerst, Robert W. Heath
Obtaining accurate and timely channel state information (CSI) is a fundamental challenge for large antenna systems. Mobile systems like 5G use a beam management framework that join…
Hierarchical ML Codebook Design for Extreme MIMO Beam Management
Ryan M. Dreifuerst, Robert W. Heath
Beam management is a strategy to unify beamforming and channel state information (CSI) acquisition with large antenna arrays in 5G. Codebooks serve multiple uses in beam management…
ML Codebook Design for Initial Access and CSI Type-II Feedback in Sub-6GHz 5G NR
Ryan M. Dreifuerst, Robert W. Heath
Beam codebooks are a recent feature to enable high dimension multiple-input multiple-output in 5G. Codebooks comprised of customizable beamforming weights can be used to transmit r…
Massive MIMO in 5G: How Beamforming, Codebooks, and Feedback Enable Larger Arrays
Ryan M. Dreifuerst, Robert W. Heath
Massive multiple-input multiple-output (MIMO) is an important technology in fifth generation (5G) cellular networks and beyond. To help design the beamforming at the base station,…
Massive MIMO Beam Management in Sub-6 GHz 5G NR
Ryan M. Dreifuerst, Robert W. Heath, Ali Yazdan
Beam codebooks are a new feature of massive multiple-input multiple-output (M-MIMO) in 5G new radio (NR). Codebooks comprised of beamforming vectors are used to transmit reference…