Yu Zhang

Orcid: 0000-0001-6851-5594

Affiliations:
  • Arizona State University, School of Electrical, Computer and Energy Engineering, Tempe, AZ, USA


According to our database1, Yu Zhang authored at least 17 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

2020
2021
2022
2023
2024
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5
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Legend:

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Links

Online presence:

On csauthors.net:

Bibliography

2024
Real-World Evaluation of Full-Duplex Millimeter Wave Communication Systems.
IEEE Trans. Wirel. Commun., September, 2024

Site-Specific Beam Alignment in 6G via Deep Learning.
IEEE Commun. Mag., August, 2024

Online Beam Learning With Interference Nulling for Millimeter Wave MIMO Systems.
IEEE Trans. Wirel. Commun., May, 2024

Decentralized Interference-Aware Codebook Learning in Millimeter Wave MIMO Systems.
CoRR, 2024

Zone-Specific CSI Feedback for Massive MIMO: A Situation-Aware Deep Learning Approach.
CoRR, 2024

2023
Deep Learning of Near Field Beam Focusing in Terahertz Wideband Massive MIMO Systems.
IEEE Wirel. Commun. Lett., March, 2023

A Digital Twin Assisted Framework for Interference Nulling in Millimeter Wave MIMO Systems.
Proceedings of the IEEE International Conference on Communications, 2023

Steer+: Robust Beam Refinement for Full-Duplex Millimeter Wave Communication Systems (Invited Paper).
Proceedings of the 57th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2023, Pacific Grove, CA, USA, October 29, 2023

2022
Reinforcement Learning of Beam Codebooks in Millimeter Wave and Terahertz MIMO Systems.
IEEE Trans. Commun., 2022

Neural Networks Based Beam Codebooks: Learning mmWave Massive MIMO Beams That Adapt to Deployment and Hardware.
IEEE Trans. Commun., 2022

Predicting Future CSI Feedback For Highly-Mobile Massive MIMO Systems.
CoRR, 2022

Online Beam Learning for Interference Nulling in Hardware-Constrained mm Wave MIMO Systems.
Proceedings of the 56th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2022, Pacific Grove, CA, USA, October 31, 2022

2021
Learning Reflection Beamforming Codebooks for Arbitrary RIS and Non-Stationary Channels.
CoRR, 2021

2020
Deep Learning for Massive MIMO With 1-Bit ADCs: When More Antennas Need Fewer Pilots.
IEEE Wirel. Commun. Lett., 2020

Learning Beam Codebooks with Neural Networks: Towards Environment-Aware mmWave MIMO.
Proceedings of the 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2020

Deep Reinforcement Learning for Intelligent Reflecting Surfaces: Towards Standalone Operation.
Proceedings of the 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2020

Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems.
Proceedings of the 54th Asilomar Conference on Signals, Systems, and Computers, 2020


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