467 citations · 824 across the 49 of their papers we have counts for
26 papers · 1 filter
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…
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…
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…
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…
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…
Model-Driven Deep Learning for Distributed Detection with Binary Quantization
Wei Guo, Meng He, Chuan Huang +4
Within the realm of rapidly advancing wireless sensor networks (WSNs), distributed detection assumes a significant role in various practical applications. However, critical challen…
Deep Learning-Based Adaptive Joint Source-Channel Coding using Hypernetworks
Songjie Xie, Hengtao He, Hongru Li +4
Deep learning-based joint source-channel coding (DJSCC) is expected to be a key technique for {the} next-generation wireless networks. However, the existing DJSCC schemes still fac…
Model-Driven Sensing-Node Selection and Power Allocation for Tracking Maneuvering Targets in Perceptive Mobile Networks
Lei Xie, Hengtao He, Shenghui Song +1
Maneuvering target tracking will be an important service of future wireless networks to assist innovative applications such as intelligent transportation. However, tracking maneuve…
Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems
Hengtao He, Xianghao Yu, Jun Zhang +2
As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial…
GNN-Enhanced Approximate Message Passing for Massive/Ultra-Massive MIMO Detection
Hengtao He, Alva Kosasih, Xianghao Yu +4
Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput…
Cell-Free Massive MIMO for 6G Wireless Communication Networks
Hengtao He, Xianghao Yu, Jun Zhang +2
The recently commercialized fifth-generation (5G) wireless communication networks achieved many improvements, including air interface enhancement, spectrum expansion, and network i…
Cell-Free Massive MIMO Detection: A Distributed Expectation Propagation Approach
Hengtao He, Xianghao Yu, Jun Zhang +2
Cell-free massive MIMO is one of the core technologies for future wireless networks. It is expected to bring enormous benefits, including ultra-high reliability, data throughput, e…
Distributed Expectation Propagation Detection for Cell-Free Massive MIMO
Hengtao He, Hanqing Wang, Xianghao Yu +3
In cell-free massive MIMO networks, an efficient distributed detection algorithm is of significant importance. In this paper, we propose a distributed expectation propagation (EP)…
Model-Driven Deep Learning for Massive MU-MIMO with Finite-Alphabet Precoding
Hengtao He, Mengjiao Zhang, Shi Jin +2
Massive multiuser multiple-input multiple-output (MU-MIMO) has been the mainstream technology in fifth-generation wireless systems. To reduce high hardware costs and power consumpt…
Model-Driven Deep Learning for MIMO Detection
Hengtao He, Chao-Kai Wen, Shi Jin +1
In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and…
A Model-Driven Deep Learning Network for MIMO Detection
Hengtao He, Chao-Kai Wen, Shi Jin +1
In this paper, we propose a model-driven deep learning network for multiple-input multiple-output (MIMO) detection. The structure of the network is specially designed by unfolding…
Model-Driven Deep Learning for Physical Layer Communications
Hengtao He, Shi Jin, Chao-Kai Wen +3
Intelligent communication is gradually considered as the mainstream direction in future wireless communications. As a major branch of machine learning, deep learning (DL) has been…
Bayesian Optimal Data Detector for Hybrid mmWave MIMO-OFDM Systems with Low-Resolution ADCs
Hengtao He, Chao-Kai Wen, Shi Jin
Hybrid analog-digital precoding architectures and low-resolution analog-to-digital converter (ADC) receivers are two solutions to reduce hardware cost and power consumption for mil…