Chris Dick

Nvidia (United States) — United States

Engineering · Physical Sciences

25h-index3.9kcitations141works4.32yr avg
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Research Topics

Advanced Wireless Communication Techniques(49), Advanced MIMO Systems Optimization(37), Digital Filter Design and Implementation(26), Wireless Communication Networks Research(22), Advancements in PLL and VCO Technologies(14)

Publications141 total

CSI-Based User Positioning, Channel Charting, and Device Classification with an NVIDIA 5G Testbed
arXiv (Cornell University)·2025Open Access
AI-Native 6G: Empowering Intelligent RAN With Accelerated Compute
IEEE Wireless Communications·2025
Sparse MIMO-OFDM Channel Estimation via RKHS Regularization
ArXiv.org·2025Open Access
Better Together: Leveraging Multiple Digital Twins for Deployment Optimization of Airborne Base Stations
IEEE Transactions on Mobile Computing·2025Open Access
X5G: An Open, Programmable, Multi-Vendor, End-to-End, Private 5G O-RAN Testbed With NVIDIA ARC and OpenAirInterface
IEEE Transactions on Mobile Computing·2025· 3 cited
Optimizing Puncturing Patterns of 5G NR LDPC Codes for Few-Iteration Decoding
arXiv (Cornell University)·2024Open Access
Optimizing Puncturing Patterns of 5G NR LDPC Codes for Few-Iteration Decoding
2024· 3 cited
A Primer on Generative AI for Telecom: From Theory to Practice
arXiv (Cornell University)·2024Open Access
X5G: An Open, Programmable, Multi-vendor, End-to-end, Private 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface
arXiv (Cornell University)·2024Open Access
An Open, Programmable, Multi-Vendor 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface
2024· 27 cited
FLASH-and-Prune: Federated Learning for Automated Selection of High-Band mmWave Sectors using Model Pruning
IEEE Transactions on Mobile Computing·2024· 9 cited
$\text{Omni-CNN}$: A Modality-Agnostic Neural Network for mmWave Beam Selection
IEEE Transactions on Vehicular Technology·2024· 9 cited
Embracing AI in 5G-Advanced Toward 6G: A Joint 3GPP and O-RAN Perspective
IEEE Communications Standards Magazine·2023· 42 cited
ML-Based Feedback-Free Adaptive MCS Selection for Massive Multi-User MIMO
2023· 6 cited
An Open, Programmable, Multi-vendor 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface
arXiv (Cornell University)·2023· 1 citedOpen Access
ML-Based Feedback-Free Adaptive MCS Selection for Massive Multi-User MIMO
arXiv (Cornell University)·2023Open Access
6G Digital Twin Networks: From Theory to Practice
IEEE Communications Magazine·2023· 135 cited
Accelerated Massive MIMO Detector Based on Annealed Underdamped Langevin Dynamics
2023· 4 cited
Bit Error and Block Error Rate Training for ML-Assisted Communication
2023· 12 cited
Going beyond RF: A survey on how AI-enabled multimodal beamforming will shape the NextG standard
Computer Networks·2023· 26 cited
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Frequent Co-authors

Michael Wu(10), Joseph R. Cavallaro(10), Christoph Studer(10), Ashutosh Sabharwal(9), J. S. Harris(8), Bei Yin(5), Sambhav R. Jain(3), Albert Gural(3), Debashri Roy(3), Batool Salehi(3), Kaushik Chowdhury(3), Kiarash Amiri(3), Raghu Rao(3), Elettra Venosa(3), Melissa Duarte(2), Guohui Wang(2), Evan Everett(2), Xingqin Lin(2), Lopamudra Kundu(2), Rahman Doost-Mohammady(2)