Ryan Mitchell Dreifuerst

Works (8)

Updated: January 4th, 2025 05:04

2024 article

Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection

2024 IEEE INTERNATIONAL BLACK SEA CONFERENCE ON COMMUNICATIONS AND NETWORKING, BLACKSEACOM 2024, pp. 54–59.

By: I. Kilinc*, R. Dreifuerst n, J. Kim * & R. Heath* 

Source: Web Of Science
Added: October 28, 2024

2024 article

Context-aware Codebook Design for 6G Extreme MIMO Systems

2024 IEEE 25TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS, SPAWC 2024, pp. 121–125.

By: R. Dreifuerst n, I. Kilinc* & R. Heath* 

author keywords: 6G; Massive MIMO; Extreme MIMO; CSI; Codebooks
Source: Web Of Science
Added: December 23, 2024

2023 journal article

Intent-Aware Radio Resource Scheduling in a RAN Slicing Scenario Using Reinforcement Learning

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 23(3), 2253–2267.

By: C. Nahum, V. Lopes *, R. Dreifuerst n, P. Batista *, I. Correa, K. Cardoso *, A. Klautau, R. Heath Jr

author keywords: Radio access networks; Ultra reliable low latency communication; Radio resource scheduling; RAN slicing; intentaware scheduling; reinforcement learning
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Source: Web Of Science
Added: July 8, 2024

2023 journal article

Machine Learning Codebook Design for Initial Access and CSI Type-II Feedback in Sub-6-GHz 5G NR

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 23(6), 6411–6424.

By: R. Dreifuerst n & R. Heath Jr

author keywords: Amplitude modulation; 5G mobile communication; Training; Array signal processing; MIMO communication; Discrete Fourier transforms; Neural networks; 6G mobile communication; extreme multiple-input multiple-output (MIMO); beam management; deep learning
Source: Web Of Science
Added: July 22, 2024

2023 review

Massive MIMO in 5G: How Beamforming, Codebooks, and Feedback Enable Larger Arrays

[Review of ]. IEEE COMMUNICATIONS MAGAZINE, 61(12), 18–23.

By: R. Dreifuerst n & R. Heath n 

author keywords: Wireless communication; Geometry; Cellular networks; 5G mobile communication; Array signal processing; Massive MIMO; Robustness
Source: Web Of Science
Added: September 9, 2024

2022 journal article

Deep Reinforcement Learning-Based Scheduling for Multiband Massive MIMO

IEEE ACCESS, 10, 125509–125525.

By: V. Lopes *, C. Nahum, R. Dreifuerst*, P. Batista *, A. Klautau, K. Cardoso *, R. Heath Jr 

author keywords: Multiband scheduling; MIMO; DRL-based scheduling; mmWave
TL;DR: This paper considers a massive MIMO system where channels are dynamically allocated in different frequency bands and proposes deep reinforcement learning (DRL) agents to perform user scheduling and shows that the DRL models outperformed baselines obtaining a 20% higher network sum rate and an 84% smaller packet loss rate. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: January 3, 2023

2022 article

Massive MIMO Beam Management in Sub-6 GHz 5G NR

2022 IEEE 95TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-SPRING).

By: R. Dreifuerst*, R. Heath n  & A. Yazdan*

TL;DR: It is shown that machine learning can be used to train site-specific codebooks for initial access and approaches the optimal performance (perfect CSI and singular value decomposition (SVD)-based beamforming), using only a few bits of feedback. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: October 31, 2022

2022 journal article

SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 70, 4454–4467.

By: R. Dreifuerst n & R. Heath n 

author keywords: Signal processing algorithms; Estimation; Quantization (signal); Signal resolution; Task analysis; Neural networks; Data models; Sinusoid decomposition; estimation; deep-learning; low-resolution; detection
TL;DR: This work proposes SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples that incorporates signal reconstruction internally as domain knowledge within the network to enhance learning and surpass traditional algorithms in mean squared error and Chamfer error. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
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7. Affordable and Clean Energy (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: October 3, 2022

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