29 citations · 83 across the 11 of their papers we have counts for
11 papers
Revisiting Trace Norm Minimization for Tensor Tucker Completion: A Direct Multilinear Rank Learning Approach
Xueke Tong, Hancheng Zhu, Lei Cheng +1
To efficiently express tensor data using the Tucker format, a critical task is to minimize the multilinear rank such that the model would not be over-flexible and lead to overfitti…
Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach
Hao Zhang, Qingfeng Lin, Yang Li +2
Activity detection is an important task in the next generation grant-free multiple access. While there are a number of existing algorithms designed for this purpose, they mostly re…
Overcoming Beam Squint in Dual-Wideband mmWave MIMO Channel Estimation: A Bayesian Multi-Band Sparsity Approach
Le Xu, Lei Cheng, Ngai Wong +2
The beam squint effect, which manifests in different steering matrices in different sub-bands, has been widely considered a challenge in millimeter wave (mmWave) multiinput multi-o…
To Fold or Not to Fold: Graph Regularized Tensor Train for Visual Data Completion
Le Xu, Lei Cheng, Ngai Wong +1
Tensor train (TT) representation has achieved tremendous success in visual data completion tasks, especially when it is combined with tensor folding. However, folding an image or v…
Output-Dependent Gaussian Process State-Space Model
Zhidi Lin, Lei Cheng, Feng Yin +2
Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade. However, the outputs of the transition…
Reconfigurable Intelligent Surface-Aided 6G Massive Access: Coupled Tensor Modeling and Sparse Bayesian Learning
Xiaodan Shao, Lei Cheng, Xiaoming Chen +2
This paper investigates a reconfigurable intelligent surface (RIS)-aided unsourced random access (URA) scheme for the sixth-generation (6G) wireless networks with massive sporadic…
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference
Yangge Chen, Lei Cheng, Yik-Chung Wu
Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning through the lens of dual-graph regularization, which has significantly improved t…
Towards Overfitting Avoidance: Tuning-free Tensor-aided Multi-user Channel Estimation for 3D Massive MIMO Communications
Lei Cheng, Qingjiang Shi
Channel estimation has long been deemed as one of the most critical problems in three-dimensional (3D) massive multiple-input multiple-output (MIMO), which is recognized as the lea…
Tensor Train Factorization and Completion under Noisy Data with Prior Analysis and Rank Estimation
Le Xu, Lei Cheng, Ngai Wong +1
Tensor train (TT) decomposition, a powerful tool for analyzing multidimensional data, exhibits superior performance in many machine learning tasks. However, existing methods for TT…
Edge Learning with Unmanned Ground Vehicle: Joint Path, Energy and Sample Size Planning
Dan Liu, Shuai Wang, Zhigang Wen +3
Edge learning (EL), which uses edge computing as a platform to execute machine learning algorithms, is able to fully exploit the massive sensing data generated by Internet of Thing…
Massive MIMO Multicast Beamforming Via Accelerated Random Coordinate Descent
Shuai Wang, Lei Cheng, Minghua Xia +1
One key feature of massive multiple-input multiple-output systems is the large number of antennas and users. As a result, reducing the computational complexity of beamforming desig…