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Found 9 papers, 1 papers with code
Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection
Codebook-based beam selection is one approach for configuring millimeter wave communication links.
Neural Codebook Design for Network Beam Management
Obtaining accurate and timely channel state information (CSI) is a fundamental challenge for large antenna systems.
Hierarchical ML Codebook Design for Extreme MIMO Beam Management
Beam management is a strategy to unify beamforming and channel state information (CSI) acquisition with large antenna arrays in 5G.
Intent-aware Radio Resource Scheduling in a RAN Slicing Scenario using Reinforcement Learning
In this scenario, radio resource scheduling (RRS) is responsible for dealing with scarce and limited frequency spectrum resources available at the RAN domain while fulfilling the slice intents.
ML Codebook Design for Initial Access and CSI Type-II Feedback in Sub-6GHz 5G NR
In this paper, we characterize the role of each codebook used during the beam management process and design a neural network to find codebooks that improve overall system performance.
Massive MIMO in 5G: How Beamforming, Codebooks, and Feedback Enable Larger Arrays
Massive multiple-input multiple-output (MIMO) is an important technology in fifth generation (5G) cellular networks and beyond.
Massive MIMO Beam Management in Sub-6 GHz 5G NR
The performance, however, is limited by the codebook design.
Optimizing Coverage and Capacity in Cellular Networks using Machine Learning
Wireless cellular networks have many parameters that are normally tuned upon deployment and re-tuned as the network changes.
End-to-End Radio Fingerprinting with Neural Networks
This paper presents a novel method for classifying radio frequency (RF) devices from their transmission signals.
