82 citations · 246 across the 24 of their papers we have counts for
24 papers
Diffusion-Based Noise-Adaptive Null-Space Channel Estimation for OFDM Systems
Heqiang Qi, Yirun Chen, Xiangming Meng +3
Accurate channel estimation in orthogonal frequency division multiplexing (OFDM) systems remains challenging when demodulation reference signal (DMRS) observations are sparse and n…
Average case analysis of Lasso under ultra-sparse conditions
Koki Okajima, Xiangming Meng, Takashi Takahashi +1
We analyze the performance of the least absolute shrinkage and selection operator (Lasso) for the linear model when the number of regressors grows larger keeping the true suppo…
QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative Models
Xiangming Meng, Yoshiyuki Kabashima
In practical compressed sensing (CS), the obtained measurements typically necessitate quantization to a limited number of bits prior to transmission or storage. This nonlinear quan…
Quantized Compressed Sensing with Score-based Generative Models
Xiangming Meng, Yoshiyuki Kabashima
We consider the general problem of recovering a high-dimensional signal from noisy quantized measurements. Quantization, especially coarse quantization such as 1-bit sign measureme…
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems
Xiangming Meng, Yoshiyuki Kabashima
With the rapid development of diffusion models and flow-based generative models, there has been a surge of interests in solving noisy linear inverse problems, e.g., super-resolutio…
A Unitary Transform Based Generalized Approximate Message Passing
Jiang Zhu, Xiangming Meng, Xupeng Lei +1
We consider the problem of recovering an unknown signal from general nonlinear measurements obtained through a generalized linear model (GLM), i.e.,…
Stochastic Neural Networks with Infinite Width are Deterministic
Liu Ziyin, Hanlin Zhang, Xiangming Meng +3
This work theoretically studies stochastic neural networks, a main type of neural network in use. We prove that as the width of an optimized stochastic neural network tends to infi…
On Model Selection Consistency of Lasso for High-Dimensional Ising Models
Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima
We theoretically analyze the model selection consistency of least absolute shrinkage and selection operator (Lasso), both with and without post-thresholding, for high-dimensional I…
Ising Model Selection Using -Regularized Linear Regression: A Statistical Mechanics Analysis
Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima
We theoretically analyze the typical learning performance of -regularized linear regression (-LinR) for Ising model selection using the replica method from statis…
Unitary Approximate Message Passing for Sparse Bayesian Learning
Man Luo, Qinghua Guo, Ming Jin +4
Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm. However, it does not work well for a generic measure…
Structure Learning in Inverse Ising Problems Using -Regularized Linear Estimator
Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima
The inference performance of the pseudolikelihood method is discussed in the framework of the inverse Ising problem when the -regularized (ridge) linear regression is adopt…
Training Restricted Boltzmann Machines with Binary Synapses using the Bayesian Learning Rule
Xiangming Meng
Restricted Boltzmann machines (RBMs) with low-precision synapses are much appealing with high energy efficiency. However, training RBMs with binary synapses is challenging due to t…
Training Binary Neural Networks using the Bayesian Learning Rule
Xiangming Meng, Roman Bachmann, Mohammad Emtiyaz Khan
Neural networks with binary weights are computation-efficient and hardware-friendly, but their training is challenging because it involves a discrete optimization problem. Surprisi…
Turbo-like Iterative Multi-user Receiver Design for 5G Non-orthogonal Multiple Access
Xiangming Meng, Yiqun Wu, Chao Wang +1
Non-orthogonal multiple access (NoMA) as an efficient way of radio resource sharing has been identified as a promising technology in 5G to help improving system capacity, user conn…
A Universal Receiver for Uplink NOMA Systems
Xiangming Meng, Yiqun Wu, Chao Wang +1
Given its capability in efficient radio resource sharing, non-orthogonal multiple access (NOMA) has been identified as a promising technology in 5G to improve the system capacity,…
Grid-less Variational Bayesian Inference of Line Spectral from Quantized Samples
Jiang Zhu, Qi Zhang, Xiangming Meng
Efficient estimation of line spectral from quantized samples is of significant importance in information theory and signal processing, e.g., channel estimation in energy efficient…
Bilinear Adaptive Generalized Vector Approximate Message Passing
Xiangming Meng, Jiang Zhu
This paper considers the generalized bilinear recovery problem which aims to jointly recover the vector and the matrix from componentwise nonlinear measurem…
Compressive Massive Random Access for Massive Machine-Type Communications (mMTC)
Malong Ke, Zhen Gao, Yongpeng Wu +1
In future wireless networks, one fundamental challenge for massive machine-type communications (mMTC) lies in the reliable support of massive connectivity with low latency. Against…
Vector Approximate Message Passing Algorithm for Structured Perturbed Sensing Matrix
Jiang Zhu, Qi Zhang, Xiangming Meng +1
In this paper, we consider a general form of noisy compressive sensing (CS) where the sensing matrix is not precisely known. Such cases exist when there are imperfections or unknow…
Binary Sparse Bayesian Learning Algorithm for One-bit Compressed Sensing
Jiang Zhu, Lin Han, Xiangming Meng +1
In this letter, a binary sparse Bayesian learning (BSBL) algorithm is proposed to slove the one-bit compressed sensing (CS) problem in both single measurement vector (SMV) and mult…
A Unified Bayesian Inference Framework for Generalized Linear Models
Xiangming Meng, Sheng Wu, Jiang Zhu
In this letter, we present a unified Bayesian inference framework for generalized linear models (GLM) which iteratively reduces the GLM problem to a sequence of standard linear mod…
Low Complexity Receiver for Uplink SCMA System via Expectation Propagation
Xiangming Meng, Yiqun Wu, Yan Chen +1
Sparse code multiple access (SCMA) scheme is considered to be one promising non-orthogonal multiple access technology for the future fifth generation (5G) communications. Due to th…
Approximate Message Passing with Nearest Neighbor Sparsity Pattern Learning
Xiangming Meng, Sheng Wu, Linling Kuang +3
We consider the problem of recovering clustered sparse signals with no prior knowledge of the sparsity pattern. Beyond simple sparsity, signals of interest often exhibits an underl…
Concise Derivation of Complex Bayesian Approximate Message Passing via Expectation Propagation
Xiangming Meng, Sheng Wu, Linling Kuang +1
In this paper, we address the problem of recovering complex-valued signals from a set of complex-valued linear measurements. Approximate message passing (AMP) is one state-of-the-a…