论文标题
基于统一转换的广义近似消息传递
A Unitary Transform Based Generalized Approximate Message Passing
论文作者
论文摘要
我们考虑从{\ Mathbb r}^n $中恢复未知信号$ {\ mathbf x} \从通过通用线性模型（GLM）获得的一般非线性测量中，即$ {\ Mathbf y} = f \ left（其中$ f（\ cdot）$是componentwise非线性函数。基于统一变换近似消息传递（UAMP）和期望传播，提出了基于统一转换的广义近似消息传递（Guamp）算法，用于一般测量矩阵$ \ bf {a} $，特别是高度相关的矩阵。对量化压缩感应的实验结果表明，所提出的Guamp在相关矩阵下的最先进的GAMP和GVAMP $ \ bf {a} $明显优于最先进的GAMP和GVAMP。
We consider the problem of recovering an unknown signal ${\mathbf x}\in {\mathbb R}^n$ from general nonlinear measurements obtained through a generalized linear model (GLM), i.e., ${\mathbf y}= f\left({\mathbf A}{\mathbf x}+{\mathbf w}\right)$, where $f(\cdot)$ is a componentwise nonlinear function. Based on the unitary transform approximate message passing (UAMP) and expectation propagation, a unitary transform based generalized approximate message passing (GUAMP) algorithm is proposed for general measurement matrices $\bf{A}$, in particular highly correlated matrices. Experimental results on quantized compressed sensing demonstrate that the proposed GUAMP significantly outperforms state-of-the-art GAMP and GVAMP under correlated matrices $\bf{A}$.
