116 citations · 196 across the 22 of their papers we have counts for
22 papers
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence
Yifei Shen, Jiawei Shao, Xinjie Zhang +5
The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper…
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…
Sparse Mixture-of-Experts are Domain Generalizable Learners
Bo Li, Yifei Shen, Jingkang Yang +5
Human visual perception can easily generalize to out-of-distributed visual data, which is far beyond the capability of modern machine learning models. Domain generalization (DG) ai…
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…
Graph Neural Networks for Wireless Communications: From Theory to Practice
Yifei Shen, Jun Zhang, S. H. Song +1
Deep learning-based approaches have been developed to solve challenging problems in wireless communications, leading to promising results. Early attempts adopted neural network arc…
Neural Piecewise-Constant Delay Differential Equations
Qunxi Zhu, Yifei Shen, Dongsheng Li +1
Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data…
How Neural Architectures Affect Deep Learning for Communication Networks?
Yifei Shen, Jun Zhang, Khaled B. Letaief
In recent years, there has been a surge in applying deep learning to various challenging design problems in communication networks. The early attempts adopt neural architectures in…
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…
How Powerful is Graph Convolution for Recommendation?
Yifei Shen, Yongji Wu, Yao Zhang +4
Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empir…
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…
AI Empowered Resource Management for Future Wireless Networks
Yifei Shen, Jun Zhang, S. H. Song +1
Resource management plays a pivotal role in wireless networks, which, unfortunately, leads to challenging NP-hard problems. Artificial Intelligence (AI), especially deep learning t…
Invariant Information Bottleneck for Domain Generalization
Bo Li, Yifei Shen, Yezhen Wang +6
Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain generalization. Nevertheless, the loss function is difficult to optimize for nonlinear…
Decentralized Statistical Inference with Unrolled Graph Neural Networks
He Wang, Yifei Shen, Ziyuan Wang +4
In this paper, we investigate the decentralized statistical inference problem, where a network of agents cooperatively recover a (structured) vector from private noisy samples with…
A Low-Complexity Algorithmic Framework for Large-Scale IRS-Assisted Wireless Systems
Yifan Ma, Yifei Shen, Xianghao Yu +3
Intelligent reflecting surfaces (IRSs) are revolutionary enablers for next-generation wireless communication networks, with the ability to customize the radio propagation environme…
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis
Yifei Shen, Yuanming Shi, Jun Zhang +1
Deep learning has recently emerged as a disruptive technology to solve challenging radio resource management problems in wireless networks. However, the neural network architecture…
Blind Data Detection in Massive MIMO via -norm Maximization over the Stiefel Manifold
Ye Xue, Yifei Shen, Vincent Lau +2
Massive MIMO has been regarded as a key enabling technique for 5G and beyond networks. Nevertheless, its performance is limited by the large overhead needed to obtain the high-dime…
Complete Dictionary Learning via -norm Maximization
Yifei Shen, Ye Xue, Jun Zhang +2
Dictionary learning is a classic representation learning method that has been widely applied in signal processing and data analytics. In this paper, we investigate a family of $\el…
A Graph Neural Network Approach for Scalable Wireless Power Control
Yifei Shen, Yuanming Shi, Jun Zhang +1
Deep neural networks have recently emerged as a disruptive technology to solve NP-hard wireless resource allocation problems in a real-time manner. However, the adopted neural netw…
LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples
Yifei Shen, Yuanming Shi, Jun Zhang +1
Effective resource management plays a pivotal role in wireless networks, which, unfortunately, results in challenging mixed-integer nonlinear programming (MINLP) problems in most c…
Transfer Learning for Mixed-Integer Resource Allocation Problems in Wireless Networks
Yifei Shen, Yuanming Shi, Jun Zhang +1
Effective resource allocation plays a pivotal role for performance optimization in wireless networks. Unfortunately, typical resource allocation problems are mixed-integer nonlinea…
Scalable network adaptation for Cloud-RANs: An imitation learning approach
Yifei Shen, Yuanming Shi, Jun Zhang +1
Network adaptation is essential for the efficient operation of Cloud-RANs. Unfortunately, it leads to highly intractable mixed-integer nonlinear programming problems. Existing solu…
Comparing large-scale graphs based on quantum probability theory
Hayoung Choi, Hosoo Lee, Yifei Shen +1
In this paper, a new measurement to compare two large-scale graphs based on the theory of quantum probability is proposed. An explicit form for the spectral distribution of the cor…