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13 papers · 1 filter
Joint Radiation Power, Antenna Position, and Beamforming Optimization for Pinching-Antenna Systems with Motion Power Consumption
Yiming Xu, Dongfang Xu, Xianghao Yu +3
Pinching-antenna systems (PASS) have been recently proposed to improve the performance of wireless networks by reconfiguring both the large-scale and small-scale channel conditions…
Joint Channel Estimation and Cooperative Localization for Near-Field Ultra-Massive MIMO
Ruoxiao Cao, Hengtao He, Xianghao Yu +5
The next-generation wireless networks are envisioned to jointly support high-rate communications and ubiquitous sensing. Ultra-Massive Multiple-Input Multiple-Output (UM-MIMO) offe…
Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO
Wentao Yu, Hengtao He, Xianghao Yu +4
Holographic MIMO (HMIMO) is being increasingly recognized as a key enabling technology for 6G wireless systems through the deployment of an extremely large number of antennas withi…
Learning Bayes-Optimal Channel Estimation for Holographic MIMO in Unknown EM Environments
Wentao Yu, Hengtao He, Xianghao Yu +4
Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to expl…
An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation
Wentao Yu, Yifei Shen, Hengtao He +4
Terahertz ultra-massive MIMO (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless networks, for which channel estimation is highly challenging. Traditional analyti…
Blind Performance Prediction for Deep Learning Based Ultra-Massive MIMO Channel Estimation
Wentao Yu, Hengtao He, Xianghao Yu +3
Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance. However, the uncertainty of prevailing deep…
Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback
Yifan Ma, Xianghao Yu, Jun Zhang +2
In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base stat…
Intelligent Reflecting Surface-Aided Maneuvering Target Sensing: True Velocity Estimation
Lei Xie, Xianghao Yu, S. H. Song
Maneuvering target sensing will be an important service of future vehicular networks, where precise velocity estimation is one of the core tasks. To this end, the recently proposed…
Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks
Wentao Yu, Yifei Shen, Hengtao He +3
Terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless systems. Due to the joint effect of its array aperture…
Learn to Communicate with Neural Calibration: Scalability and Generalization
Yifan Ma, Yifei Shen, Xianghao Yu +3
The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. U…
Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation
Yifan Ma, Yifei Shen, Xianghao Yu +3
Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. However, these two modules have…
Hybrid Beamforming for 5G and Beyond Millimeter-Wave Systems: A Holistic View
Jun Zhang, Xianghao Yu, Khaled B. Letaief
Millimeter-wave (mm-wave) communication is a key technology for future wireless networks. To combat significant path loss and exploit the abundant mm-wave spectrum, effective beamf…
Doubling Phase Shifters for Efficient Hybrid Precoder Design in Millimeter-Wave Communication Systems
Xianghao Yu, Jun Zhang, Khaled B. Letaief
Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter-wave (mm-wave) communications, but its precoder design is highly complicated. In t…