467 citations · 824 across the 49 of their papers we have counts for
19 papers · 1 filter
XFreq-GS: Cross-Frequency Wireless Radiation Field Reconstruction with 3D Gaussian Splatting
Sheng Wang, Hengtao He, Chaozheng Wen +5
Channel modeling is fundamental to the analysis, design, and optimization of wireless communication systems, which, however, accurate wireless channel modeling remains challenging,…
Deep Learning-based Low-Overhead Beam Alignment for mmWave Massive MIMO Systems
Weijie Jin, Jing Zhang, Hengtao He +3
Millimeter-wave massive multiple-input multiple-output systems employ highly directional beamforming to overcome severe path loss, and their performance critically depends on accur…
Multimodal Mixture-of-Experts for ISAC in Low-Altitude Wireless Networks
Kai Zhang, Wentao Yu, Hengtao He +3
Integrated sensing and communication (ISAC) is a key enabler for low-altitude wireless networks (LAWNs), providing simultaneous environmental perception and data transmission in co…
Multimodal Deep Learning-Empowered Beam Prediction in Future THz ISAC Systems
Kai Zhang, Wentao Yu, Hengtao He +3
Integrated sensing and communication (ISAC) systems operating at terahertz (THz) bands are envisioned to enable both ultra-high data-rate communication and precise environmental aw…
AI and Deep Learning for Terahertz Ultra-Massive MIMO: From Model-Driven Approaches to Foundation Models
Wentao Yu, Hengtao He, Shenghui Song +4
This study explored the transformative potential of artificial intelligence (AI) in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output (UM-MI…
Low-Complexity CSI Feedback for FDD Massive MIMO Systems via Learning to Optimize
Yifan Ma, Hengtao He, Shenghui Song +2
In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems, the growing number of base station antennas leads to prohibitive feedback overhead for dow…
Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck
Hongru Li, Jiawei Shao, Hengtao He +3
Task-oriented communication aims to extract and transmit task-relevant information to significantly reduce the communication overhead and transmission latency. However, the unpredi…
Approximate Message Passing-Enhanced Graph Neural Network for OTFS Data Detection
Wenhao Zhuang, Yuyi Mao, Hengtao He +4
Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to support high-mobility wireless communications, for which, cost-effective data detectors are…
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…
Deep Learning for Near-Field XL-MIMO Transceiver Design: Principles and Techniques
Wentao Yu, Yifan Ma, Hengtao He +3
Massive multiple-input multiple-output (MIMO) has been a critical enabling technology in 5th generation (5G) wireless networks. With the advent of 6G, a natural evolution is to emp…
Task-Oriented Communication with Out-of-Distribution Detection: An Information Bottleneck Framework
Hongru Li, Wentao Yu, Hengtao He +4
Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for dow…
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…
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…
Adaptive Channel Estimation Based on Model-Driven Deep Learning for Wideband mmWave Systems
Weijie Jin, Hengtao He, Chao-Kai Wen +2
Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging due to the beam squint effect. To solve the problem, we propose a learnable iterative shrinkage…
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach
Hengtao He, Rui Wang, Weijie Jin +3
Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To comp…
Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM
Jing Zhang, Hengtao He, Chao-Kai Wen +2
Channel estimation and signal detection are very challenging for an orthogonal frequency division multiplexing (OFDM) system without cyclic prefix (CP). In this article, deep learn…