47 citations · 94 across the 13 of their papers we have counts for
14 papers
Discover Fast Power Allocation Solution for Multi-Target Tracking via AlphaEvolve Evolution
Zhenkang Hou, Wenqiang Pu, Junkun Yan +2
Efficient radar resource allocation is a fundamental yet computationally challenging problem, as optimal solutions typically require iterative optimization with high complexity. Mo…
Learning an Opponent-aware Anti-jamming Strategy via Online Convex Optimization
Liangqi Liu, Wenqiang Pu, Yingru Li +1
The dynamic competition against intelligent jammer systems presents a significant challenge to modern radar. Traditional active anti-jamming strategy learning methods often suffer…
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games
Yingru Li, Liangqi Liu, Wenqiang Pu +2
This work tackles the complexities of multi-player scenarios in \emph{unknown games}, where the primary challenge lies in navigating the uncertainty of the environment through band…
Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization
Liangqi Liu, Wenqiang Pu, Yingru Li +2
The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sampl…
An Efficient Alternating Riemannian/Projected Gradient Descent Ascent Algorithm for Fair Principal Component Analysis
Meng Xu, Bo Jiang, Wenqiang Pu +2
Fair principal component analysis (FPCA), a ubiquitous dimensionality reduction technique in signal processing and machine learning, aims to find a low-dimensional representation f…
On the Iteration Complexity of Smoothed Proximal ALM for Nonconvex Optimization Problem with Convex Constraints
Jiawei Zhang, Wenqiang Pu, Zhi-Quan Luo
It is well-known that the lower bound of iteration complexity for solving nonconvex unconstrained optimization problems is , which can be achieved by standard gradient de…
Counterfactual Regret Minimization for Anti-jamming Game of Frequency Agile Radar
Huayue Li, Zhaowei Han, Wenqiang Pu +3
The competition between radar and jammer is one emerging issue in modern electronic warfare, which in principle can be viewed as a non-cooperative game with two players. In this wo…
To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?
Bingqing Song, Haoran Sun, Wenqiang Pu +2
Machine learning has become successful in solving wireless interference management problems. Different kinds of deep neural networks (DNNs) have been trained to accomplish key task…
Efficient Estimation of Sensor Biases for the 3-Dimensional Asynchronous Multi-Sensor System
Wenqiang Pu, Ya-Feng Liu, Zhi-Quan Luo
An important preliminary procedure in multi-sensor data fusion is \textit{sensor registration}, and the key step in this procedure is to estimate sensor biases from their noisy mea…
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…
Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses
Wenqiang Pu, Shahana Ibrahim, Xiao Fu +1
This work considers low-rank canonical polyadic decomposition (CPD) under a class of non-Euclidean loss functions that frequently arise in statistical machine learning and signal p…
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…
A Penalized Inequality-Constrained Approach for Robust Beamforming with DoF Limitation
Wenqiang Pu, Jinjun Xiao, Tao Zhang +1
A well-known challenge in beamforming is how to optimally utilize the degrees of freedom (DoF) of the array to design a robust beamformer, especially when the array DoF is limited.…
Optimal Estimation of Sensor Biases for Asynchronous Multi-Sensor Data Fusion
Wenqiang Pu, Ya-Feng Liu, Junkun Yan +2
An important step in a multi-sensor surveillance system is to estimate sensor biases from their noisy asynchronous measurements. This estimation problem is computationally challeng…