467 citations · 824 across the 49 of their papers we have counts for
52 papers
Model-Driven Deep Learning for Distributed Detection with Low-Bit Quantization
Wei Guo, Meng He, Chuan Huang +4
Distributed detection plays a fundamental role in communication-efficient collaborative inference over large-scale networks, where spatially distributed nodes must cooperate using…
Task-Oriented Precoding for Edge Inference over Large-Scale MIMO Systems
Hongru Li, Zeyan Zhuang, Zixin Wang +4
Future wireless networks are expected to support networked artificial intelligence (AI) services, where multiple devices transmit learned features to an edge server for distributed…
A Unified Two-Stage Generative Diffusion Framework for Channel Estimation and Port Selection in Multiuser MIMO-FAS
Erqiang Tang, Wei Guo, Hengtao He +3
Fluid antenna systems (FAS) have emerged as a promising technology for next-generation wireless systems. However, practical multiuser multiple-input multiple-output FAS (MIMO-FAS)…
SPA-MAE: A Physics-Guided CSI Foundation Model for Wireless Physical Layer
Chen Chen, Weijie Jin, Hengtao He +2
Deep learning (DL) has been widely used in future 6G physical layer communications, but task-specific DL models are difficult to generalize across different physical layer tasks. R…
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…
Sensing Mutual Information for Communication Signal with Deterministic Pilots and Random Data Payloads
Lei Xie, Hengtao He, Jun Tong +2
The recent emergence of the integrated sensing and communication (ISAC) framework has sparked significant interest in quantifying the sensing capabilities inherent in communication…
Adaptive Matched Filtering for Sensing With Communication Signals in Cluttered Environments
Lei Xie, Hengtao He, Yifeng Xiong +2
This paper investigates the performance of the adaptive matched filtering (AMF) in cluttered environments, particularly when operating with superimposed signals. Since the instanta…
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…
Fluid Antenna-Assisted MU-MIMO Systems with Decentralized Baseband Processing
Tianyi Liao, Wei Guo, Hengtao He +3
The fluid antenna system (FAS) has emerged as a disruptive technology, offering unprecedented degrees of freedom (DoF) for wireless communication systems. However, optimizing fluid…
Accurate and Fast Channel Estimation for Fluid Antenna Systems with Diffusion Models
Erqiang Tang, Wei Guo, Hengtao He +3
Fluid antenna systems (FAS) offer enhanced spatial diversity for next-generation wireless systems. However, acquiring accurate channel state information (CSI) remains challenging d…
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…
Mutual Information-Empowered Task-Oriented Communication: Principles, Applications and Challenges
Hongru Li, Songjie Xie, Jiawei Shao +5
Mutual information (MI)-based guidelines have recently proven to be effective for designing task-oriented communication systems, where the ultimate goal is to extract and transmit…
Communication-Efficient Distributed On-Device LLM Inference Over Wireless Networks
Kai Zhang, Hengtao He, Shenghui Song +2
Large language models (LLMs) have demonstrated remarkable success across various application domains, but their enormous sizes and computational demands pose significant challenges…
Joint Beamforming and Antenna Position Optimization for Fluid Antenna-Assisted MU-MIMO Networks
Tianyi Liao, Wei Guo, Hengtao He +3
The fluid antenna system (FAS) is a disruptive tech-nology for future wireless communication networks. This paper considers the joint optimization of beamforming matrices and anten…
Remote Training in Task-Oriented Communication: Supervised or Self-Supervised with Fine-Tuning?
Hongru Li, Hang Zhao, Hengtao He +3
Task-oriented communication focuses on extracting and transmitting only the information relevant to specific tasks, effectively minimizing communication overhead. Most existing met…
Distributed On-Device LLM Inference With Over-the-Air Computation
Kai Zhang, Hengtao He, Shenghui Song +2
Large language models (LLMs) have achieved remarkable success across various artificial intelligence tasks. However, their enormous sizes and computational demands pose significant…
Siamese Machine Unlearning with Knowledge Vaporization and Concentration
Songjie Xie, Hengtao He, Shenghui Song +2
In response to the practical demands of the ``right to be forgotten" and the removal of undesired data, machine unlearning emerges as an essential technique to remove the learned k…
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…
Toward Real-Time Edge AI: Model-Agnostic Task-Oriented Communication with Visual Feature Alignment
Songjie Xie, Hengtao He, Shenghui Song +2
Task-oriented communication presents a promising approach to improve the communication efficiency of edge inference systems by optimizing learning-based modules to extract and tran…
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks
Tianqu Kang, Zixin Wang, Hengtao He +3
Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges. Federated fine-tuning (FedFT) mitigate…
Fast Adaptation for Deep Learning-based Wireless Communications
Ouya Wang, Hengtao He, Shenglong Zhou +4
The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges h…
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…
The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective
Tianqu Kang, Lumin Liu, Hengtao He +3
Federated Learning (FL) is an emerging paradigm that holds great promise for privacy-preserving machine learning using distributed data. To enhance privacy, FL can be combined with…