Yu Zhang
Orcid: 0000-0001-6851-5594Affiliations:
- Arizona State University, School of Electrical, Computer and Energy Engineering, Tempe, AZ, USA
According to our database1,
Yu Zhang
authored at least 16 papers
between 2020 and 2024.
Collaborative distances:
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Bibliography
2024
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
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
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