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Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning
Sagar Shrestha, Xiao Fu, Mingyi Hong
This work revisits the joint beamforming (BF) and antenna selection (AS) problem, as well as its robust beamforming (RBF) version under imperfect channel state information (CSI). S…
Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing
Meng Ding, Xiao Fu, Xi-Le Zhao
The block-term tensor decomposition model with multilinear rank- terms (or, the "LL1 tensor decomposition" in short) offers a valuable alternative for hyperspectral un…
Memory-Efficient Convex Optimization for Self-Dictionary Separable Nonnegative Matrix Factorization: A Frank-Wolfe Approach
Tri Nguyen, Xiao Fu, Ruiyuan Wu
Nonnegative matrix factorization (NMF) often relies on the separability condition for tractable algorithm design. Separability-based NMF is mainly handled by two types of approache…
Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective
Haoran Sun, Wenqiang Pu, Xiao Fu +2
There has been a growing interest in developing data-driven, and in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such…
Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models
Sagar Shrestha, Xiao Fu, Mingyi Hong
The spectrum cartography (SC) technique constructs multi-domain (e.g., frequency, space, and time) radio frequency (RF) maps from limited measurements, which can be viewed as an il…
Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment
Haoran Sun, Wenqiang Pu, Minghe Zhu +3
There has been a growing interest in developing data-driven and in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such a…
Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling
Meng Ding, Xiao Fu, Ting-Zhu Huang +2
This work revisits coupled tensor decomposition (CTD)-based hyperspectral super-resolution (HSR). HSR aims at fusing a pair of hyperspectral and multispectral images to recover a s…
Computing Large-Scale Matrix and Tensor Decomposition with Structured Factors: A Unified Nonconvex Optimization Perspective
Xiao Fu, Nico Vervliet, Lieven De Lathauwer +2
The proposed article aims at offering a comprehensive tutorial for the computational aspects of structured matrix and tensor factorization. Unlike existing tutorials that mainly fo…
On Recoverability of Randomly Compressed Tensors with Low CP Rank
Shahana Ibrahim, Xiao Fu, Xingguo Li
Our interest lies in the recoverability properties of compressed tensors under the \textit{canonical polyadic decomposition} (CPD) model. The considered problem is well-motivated i…
Spectrum Cartography via Coupled Block-Term Tensor Decomposition
Guoyong Zhang, Xiao Fu, Jun Wang +2
Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i.e., a radio map)---from limited samples taken spars…
Hyperspectral Super-resolution: A Coupled Nonnegative Block-term Tensor Decomposition Approach
Guoyong Zhang, Xiao Fu, Kejun Huang +1
Hyperspectral super-resolution (HSR) aims at fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image (SRI). Recently, a coupled tenso…
Amplitude Retrieval for Channel Estimation of MIMO Systems with One-Bit ADCs
Cheng Qian, Xiao Fu, Nicholas D. Sidiropoulos
This letter revisits the channel estimation problem for MIMO systems with one-bit analog-to-digital converters (ADCs) through a novel algorithm--Amplitude Retrieval (AR). Unlike th…
Tensor Completion from Regular Sub-Nyquist Samples
Charilaos I. Kanatsoulis, Xiao Fu, Nicholas D. Sidiropoulos +1
Signal sampling and reconstruction is a fundamental engineering task at the heart of signal processing. The celebrated Shannon-Nyquist theorem guarantees perfect signal reconstruct…
Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization
Xiao Fu, Shahana Ibrahim, Hoi-To Wai +2
This work considers the problem of computing the canonical polyadic decomposition (CPD) of large tensors. Prior works mostly leverage data sparsity to handle this problem, which is…
Tensor-Based Channel Estimation for Dual-Polarized Massive MIMO Systems
Cheng Qian, Xiao Fu, Nicholas D. Sidiropoulos +1
The 3GPP suggests to combine dual polarized (DP) antenna arrays with the double directional (DD) channel model for downlink channel estimation. This combination strikes a good bala…
Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach
Charilaos I. Kanatsoulis, Xiao Fu, Nicholas D. Sidiropoulos +1
Hyperspectral super-resolution refers to the problem of fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image (SRI) that has fine s…
Tensor-Based Parameter Estimation of Double Directional Massive MIMO Channel with Dual-Polarized Antennas
Cheng Qian, Xiao Fu, Nicholas D. Sidiropoulos +1
The 3GPP suggests to combine dual polarized (DP) antenna arrays with the double directional (DD) channel model for downlink channel estimation. This combination strikes a good bala…
Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications
Xiao Fu, Kejun Huang, Nicholas D. Sidiropoulos +1
Nonnegative matrix factorization (NMF) has become a workhorse for signal and data analytics, triggered by its model parsimony and interpretability. Perhaps a bit surprisingly, the…
Tensors, Learning, and 'Kolmogorov Extension' for Finite-alphabet Random Vectors
Nikos Kargas, Nicholas D. Sidiropoulos, Xiao Fu
Estimating the joint probability mass function (PMF) of a set of random variables lies at the heart of statistical learning and signal processing. Without structural assumptions, s…