164 citations · 450 across the 67 of their papers we have counts for
22 papers · 1 filter
Task-Oriented Communication with Hybrid-Precision Models
Songjie Xie, Wei Guo, Shenghui Song +3
Edge inference has emerged as a promising solution for the proliferation of artificial intelligence (AI) services by deploying models at the network edge to circumvent cloud-routin…
Multi-Modal Environment-Aware Beam Management for Massive MIMO: A Geometry-Driven Virtual Base Station Framework
Yijie Bian, Wei Guo, Jie Yang +4
High-frequency massive multiple-input multiple-output (MIMO) systems promise ultra-high data rates. However, efficient beam management remains challenging due to the prohibitive be…
Sensing-Assisted Channel Estimation for Flexible-Antenna Systems: A Unified Framework
Ruoxiao Cao, Wentao Yu, Zixin Wang +4
Flexible-antenna systems, which use a small number of radio frequency (RF) chains to dynamically access a large set of candidate antenna locations, have emerged as a hardware-effic…
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…
A Tractable Approach to Massive Communication and Ubiquitous Connectivity in 6G Standardization
Junyi Jiang, Wei Chen, Xin Guo +6
The full-scale 6G standardization has attracted considerable recent attention, especially since the first 3GPP-wide 6G workshop held in March 2025. To understand the practical and…
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…
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…
Power Allocation for Device-to-Device Interference Channel Using Truncated Graph Transformers
Dohoon Kim, Shenghui Song
Power control for the device-to-device interference channel with single-antenna transceivers has been widely analyzed with both model-based methods and learning-based approaches. A…
Joint BS Selection, User Association, and Beamforming Design for Network Integrated Sensing and Communication
Yiming Xu, Dongfang Xu, Lei Xie +1
Different from conventional radar, the cellular network in the integrated sensing and communication (ISAC) system enables collaborative sensing by multiple sensing nodes, e.g., bas…
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…
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…
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…